Sample records for personal omics profiling
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Schmidt, Michael A; Goodwin, Thomas J
2013-01-01
Space flight is one of the most extreme conditions encountered by humans. Advances in Omics methodologies (genomics, transcriptomics, proteomics, and metabolomics) have revealed that unique differences exist between individuals. These differences can be amplified in extreme conditions, such as space flight. A better understanding of individual differences may allow us to develop personalized countermeasure packages that optimize the safety and performance of each astronaut. In this review, we explore the role of "Omics" in advancing our ability to: (1) more thoroughly describe the biological response of humans in space; (2) describe molecular attributes of individual astronauts that alter the risk profile prior to entering the space environment; (3) deploy Omics techniques in the development of personalized countermeasures; and (4) develop a comprehensive Omics-based assessment and countermeasure platform that will guide human space flight in the future. In this review, we advance the concept of personalized medicine in human space flight, with the goal of enhancing astronaut safety and performance. Because the field is vast, we explore selected examples where biochemical individuality might significantly impact countermeasure development. These include gene and small molecule variants associated with: (1) metabolism of therapeutic drugs used in space; (2) one carbon metabolism and DNA stability; (3) iron metabolism, oxidative stress and damage, and DNA stability; and (4) essential input (Mg and Zn) effects on DNA repair. From these examples, we advance the case that widespread Omics profiling should serve as the foundation for aerospace medicine and research, explore methodological considerations to advance the field, and suggest why personalized medicine may become the standard of care for humans in space.
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Tabassum, Rubina; Sivadas, Ambily; Agrawal, Vartika; Tian, Haozheng; Arafat, Dalia; Gibson, Greg
2015-08-13
Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes. Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression. People express an "omic personality" consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.
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Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
Stanberry, Larissa; Mias, George I.; Haynes, Winston; Higdon, Roger; Snyder, Michael; Kolker, Eugene
2013-01-01
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. PMID:24958148
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Watson for Genomics: Moving Personalized Medicine Forward.
Rhrissorrakrai, Kahn; Koyama, Takahiko; Parida, Laxmi
2016-08-01
The confluence of genomic technologies and cognitive computing has brought us to the doorstep of widespread usage of personalized medicine. Cognitive systems, such as Watson for Genomics (WG), integrate massive amounts of new omic data with the current body of knowledge to assist physicians in analyzing and acting on patient's genomic profiles. Copyright © 2016 Elsevier Inc. All rights reserved.
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Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael
2017-09-12
Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Putignani, Lorenza; Del Chierico, Federica; Vernocchi, Pamela; Cicala, Michele; Cucchiara, Salvatore; Dallapiccola, Bruno
2016-02-01
Gastrointestinal disorders, although clinically heterogeneous, share pathogenic mechanisms, including genetic susceptibility, impaired gut barrier function, altered microbiota, and environmental triggers (infections, social and behavioral factors, epigenetic control, and diet). Gut microbiota has been studied for inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) in either children or adults, while modifiable gut microbiota features, acting as risk and premorbid factors along the childhood-adulthood transition, have not been thoroughly investigated so far. Indeed, the relationship between variations of the entire host/microbiota/environmental scenario and clinical phenotypes is still not fully understood. In this respect, tracking gut dysbiosis grading may help deciphering host phenotype-genotype associations and microbiota shifts in an integrated top-down omics-based approach within large-scale pediatric and adult case-control cohorts. Large-scale gut microbiota signatures and host inflammation patterns may be integrated with dietary habits, under genetic and epigenetic constraints, providing gut dysbiosis profiles acting as risk predictors of IBD or IBS in preclinical cases. Tracking dysbiosis supports new personalized/stratified IBD and IBS prevention programmes, generating Decision Support System tools. They include (1) high risk or flare-up recurrence -omics-based dysbiosis profiles; (2) microbial and molecular biomarkers of health and disease; (3) -omics-based pipelines for laboratory medicine diagnostics; (4) health apps for self-management of score-based dietary profiles, which can be shared with clinicians for nutritional habit and lifestyle amendment; (5) -omics profiling data warehousing and public repositories for IBD and IBS profile consultation. Dysbiosis-related indexes can represent novel laboratory and clinical medicine tools preventing or postponing the disease, finally interfering with its natural history.
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Progress in oral personalized medicine: contribution of 'omics'.
Glurich, Ingrid; Acharya, Amit; Brilliant, Murray H; Shukla, Sanjay K
2015-01-01
Precision medicine (PM), representing clinically applicable personalized medicine, proactively integrates and interprets multidimensional personal health data, including clinical, 'omics', and environmental profiles, into clinical practice. Realization of PM remains in progress. The focus of this review is to provide a descriptive narrative overview of: 1) the current status of oral personalized medicine; and 2) recent advances in genomics and related 'omic' and emerging research domains contributing to advancing oral-systemic PM, with special emphasis on current understanding of oral microbiomes. A scan of peer-reviewed literature describing oral PM or 'omic'-based research conducted on humans/data published in English within the last 5 years in journals indexed in the PubMed database was conducted using mesh search terms. An evidence-based approach was used to report on recent advances with potential to advance PM in the context of historical critical and systematic reviews to delineate current state-of-the-art technologies. Special focus was placed on oral microbiome research associated with health and disease states, emerging research domains, and technological advances, which are positioning realization of PM. This review summarizes: 1) evolving conceptualization of personalized medicine; 2) emerging insight into roles of oral infectious and inflammatory processes as contributors to both oral and systemic diseases; 3) community shifts in microbiota that may contribute to disease; 4) evidence pointing to new uncharacterized potential oral pathogens; 5) advances in technological approaches to 'omics' research that will accelerate PM; 6) emerging research domains that expand insights into host-microbe interaction including inter-kingdom communication, systems and network analysis, and salivaomics; and 7) advances in informatics and big data analysis capabilities to facilitate interpretation of host and microbiome-associated datasets. Furthermore, progress in clinically applicable screening assays and biomarker definition to inform clinical care are briefly explored. Advancement of oral PM currently remains in research and discovery phases. Although substantive progress has been made in advancing the understanding of the role of microbiome dynamics in health and disease and is being leveraged to advance early efforts at clinical translation, further research is required to discern interpretable constituency patterns in the complex interactions of these microbial communities in health and disease. Advances in biotechnology and bioinformatics facilitating novel approaches to rapid analysis and interpretation of large datasets are providing new insights into oral health and disease, potentiating clinical application and advancing realization of PM within the next decade.
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Montague, Elizabeth; Stanberry, Larissa; Higdon, Roger; Janko, Imre; Lee, Elaine; Anderson, Nathaniel; Choiniere, John; Stewart, Elizabeth; Yandl, Gregory; Broomall, William; Kolker, Natali
2014-01-01
Abstract Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (MOPED, http://moped.proteinspire.org) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of MOPED is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, MOPED 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, MOPED provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org) metadata checklist. MOPED 2.5 has greatly increased the number of proteomics absolute and relative expression records to over 500,000, in addition to adding more than four million transcriptomics relative expression records. MOPED has an intuitive user interface with tabs for querying different types of omics expression data and new tools for data visualization. Summary information including expression data, pathway mappings, and direct connection between proteins and genes can be viewed on Protein and Gene Details pages. These connections in MOPED provide a context for multi-omics expression data exploration. Researchers are encouraged to submit omics data which will be consistently processed into expression summaries. MOPED as a multi-omics data resource is a pivotal public database, interdisciplinary knowledge resource, and platform for multi-omics understanding. PMID:24910945
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Metabolomics and Personalized Medicine.
Koen, Nadia; Du Preez, Ilse; Loots, Du Toit
2016-01-01
Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine. © 2016 Elsevier Inc. All rights reserved.
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[Proteomics and personalized medicine].
Rocchiccioli, Silvia; Tedeschi, Lorena; Citti, Lorenzo; Cecchettini, Antonella
2013-05-01
With the disclosure of the human genome a new era for bio-medicine has arisen, characterized by the challenge to investigate pathogenic mechanisms, studying simultaneously metabolites, DNA, RNA, and proteins. As a result, the "omics" revolution boomed, giving birth to a new medicine named "omics-based medicine". Among the other "omics", proteomics has been widely used in medicine, since it can produce a more "holistic" overview of a disease and provide a "constellation" of possible specific markers, a molecular fingerprinting that defines the clinical condition of an individual. Endpoint of this comprehensive and detailed analysis is the "diagnostic-omics", i.e. the achievement of personalized diagnoses with obvious benefits for prevention and therapy and this goal can be reached only with a perfect integration between clinicians and proteomists. To impact on the possible key factors involved in the pathological processes, oligonucleotide-based knock-down strategies can be helpful. They exploit omics-derived molecular tools (antisense, siRNA, ribozymes, decoys, and aptamers) that can be used to inhibit, at transcriptional or post-transcriptional levels, the events leading to protein synthesis, thus decreasing its expression. The identification of the pivotal mechanisms involved in diseases using global, "scenic" approaches such as the "omics" ones, and the subsequent validation and detailed description of the processes by specific molecular tools, can result in a more preventive, predictive and personalized medicine.
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Clustering multilayer omics data using MuNCut.
Teran Hidalgo, Sebastian J; Ma, Shuangge
2018-03-14
Omics profiling is now a routine component of biomedical studies. In the analysis of omics data, clustering is an essential step and serves multiple purposes including for example revealing the unknown functionalities of omics units, assisting dimension reduction in outcome model building, and others. In the most recent omics studies, a prominent trend is to conduct multilayer profiling, which collects multiple types of genetic, genomic, epigenetic and other measurements on the same subjects. In the literature, clustering methods tailored to multilayer omics data are still limited. Directly applying the existing clustering methods to multilayer omics data and clustering each layer first and then combing across layers are both "suboptimal" in that they do not accommodate the interconnections within layers and across layers in an informative way. In this study, we develop the MuNCut (Multilayer NCut) clustering approach. It is tailored to multilayer omics data and sufficiently accounts for both across- and within-layer connections. It is based on the novel NCut technique and also takes advantages of regularized sparse estimation. It has an intuitive formulation and is computationally very feasible. To facilitate implementation, we develop the function muncut in the R package NcutYX. Under a wide spectrum of simulation settings, it outperforms competitors. The analysis of TCGA (The Cancer Genome Atlas) data on breast cancer and cervical cancer shows that MuNCut generates biologically meaningful results which differ from those using the alternatives. We propose a more effective clustering analysis of multiple omics data. It provides a new venue for jointly analyzing genetic, genomic, epigenetic and other measurements.
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Davey, Peter A; Pernice, Mathieu; Sablok, Gaurav; Larkum, Anthony; Lee, Huey Tyng; Golicz, Agnieszka; Edwards, David; Dolferus, Rudy; Ralph, Peter
2016-09-01
Seagrass meadows are disappearing at alarming rates as a result of increasing coastal development and climate change. The emergence of omics and molecular profiling techniques in seagrass research is timely, providing a new opportunity to address such global issues. Whilst these applications have transformed terrestrial plant research, they have only emerged in seagrass research within the past decade; In this time frame we have observed a significant increase in the number of publications in this nascent field, and as of this year the first genome of a seagrass species has been sequenced. In this review, we focus on the development of omics and molecular profiling and the utilization of molecular markers in the field of seagrass biology. We highlight the advances, merits and pitfalls associated with such technology, and importantly we identify and address the knowledge gaps, which to this day prevent us from understanding seagrasses in a holistic manner. By utilizing the powers of omics and molecular profiling technologies in integrated strategies, we will gain a better understanding of how these unique plants function at the molecular level and how they respond to on-going disturbance and climate change events.
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The first decade of MALDI protein profiling: a lesson in translational biomarker research.
Albrethsen, Jakob
2011-05-16
MALDI protein profiling has identified several important challenges in omics-based biomarker research. First, research into the analytical performance of a novel omics-platform of potential diagnostic impact must be carried out in a critical manner and according to common guidelines. Evaluation studies should be performed at an early time and preferably before massive advancement into explorative biomarker research. In particular, MALDI profiling underscores the need for an adequate understanding of the causal relationship between molecular abundance and the quantitative measure in multivariate biomarker research. Secondly, MALDI profiling has raised awareness of the significant risk of false-discovery in biomarker research due to several confounding factors, including sample processing and unspecific host-response to disease. Here, the experience from MALDI profiling supports that a central challenge in unbiased molecular profiling is to pinpoint the aberrations of clinical interest among potentially massive unspecific changes that can accompany disease. The lessons from the first decade of MALDI protein profiling are relevant for future efforts in advancing omics-based biomarker research beyond the laboratory setting and into clinical verification. Copyright © 2011 Elsevier B.V. All rights reserved.
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HARNESSING BIG DATA FOR PRECISION MEDICINE: INFRASTRUCTURES AND APPLICATIONS.
Yu, Kun-Hsing; Hart, Steven N; Goldfeder, Rachel; Zhang, Qiangfeng Cliff; Parker, Stephen C J; Snyder, Michael
2017-01-01
Precision medicine is a health management approach that accounts for individual differences in genetic backgrounds and environmental exposures. With the recent advancements in high-throughput omics profiling technologies, collections of large study cohorts, and the developments of data mining algorithms, big data in biomedicine is expected to provide novel insights into health and disease states, which can be translated into personalized disease prevention and treatment plans. However, petabytes of biomedical data generated by multiple measurement modalities poses a significant challenge for data analysis, integration, storage, and result interpretation. In addition, patient privacy preservation, coordination between participating medical centers and data analysis working groups, as well as discrepancies in data sharing policies remain important topics of discussion. In this workshop, we invite experts in omics integration, biobank research, and data management to share their perspectives on leveraging big data to enable precision medicine.Workshop website: http://tinyurl.com/PSB17BigData; HashTag: #PSB17BigData.
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Integrative FourD omics approach profiles the target network of the carbon storage regulatory system
Sowa, Steven W.; Gelderman, Grant; Leistra, Abigail N.; Buvanendiran, Aishwarya; Lipp, Sarah; Pitaktong, Areen; Vakulskas, Christopher A.; Romeo, Tony; Baldea, Michael
2017-01-01
Abstract Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets. PMID:28126921
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Sowa, Steven W; Gelderman, Grant; Leistra, Abigail N; Buvanendiran, Aishwarya; Lipp, Sarah; Pitaktong, Areen; Vakulskas, Christopher A; Romeo, Tony; Baldea, Michael; Contreras, Lydia M
2017-02-28
Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Single Cell Multi-Omics Technology: Methodology and Application.
Hu, Youjin; An, Qin; Sheu, Katherine; Trejo, Brandon; Fan, Shuxin; Guo, Ying
2018-01-01
In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions.
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Single Cell Multi-Omics Technology: Methodology and Application
Hu, Youjin; An, Qin; Sheu, Katherine; Trejo, Brandon; Fan, Shuxin; Guo, Ying
2018-01-01
In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions. PMID:29732369
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Omics Profiling in Precision Oncology*
Yu, Kun-Hsing; Snyder, Michael
2016-01-01
Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology. PMID:27099341
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2013-01-01
The Diagnostic and Statistical Manual of Mental Disorders (DSM) is universally acknowledged as the prominent reference textbook for the diagnosis and assessment of psychiatric diseases. However, since the publication of its first version in 1952, controversies have been raised concerning its reliability and validity and the need for other novel clinical tools has emerged. Currently the DSM is in its fourth edition and a new fifth edition is expected for release in 2013, in an intense intellectual debate and in a call for new proposals. Since 1952, psychiatry has undergone many changes and is emerging as unique field in the medical area in which a novel approach is being demanded for properly treating patients: not the classical “one-size-fits-all” approach, but a more targeted and tailored diagnosis and therapeutics, taking into account the complex interactions among genes and their products, environment, culture and the psychological apparatus of the subject. OMICS sciences, being based on high-throughput technologies, are systems biology related fields (like genomics, proteomics, transcriptomics and so on). In the frame of the P5 medicine (personalized, participatory, predictive, preventive, psycho-cognitive), they could establish links between psychiatric diseases, which are disorders with a final common symptomatology with vastly heterogeneous biological, environmental and sociological underpinnings, and by understanding the psychiatric diseases beyond their classic symptomatic or syndromal definitions using OMICS research, one can have a broader picture and unprecedented links and reclassification of psychiatric nosology. Importantly, by understanding the basis of heterogeneity in diseases through OMICS research, one could also personalize treatment of psychiatric illnesses. In this manuscript, we discuss a gap in the current psychiatric research, namely the missing logical link among OMICS, personalized medicine and reclassification of diseases. Moreover, we explore the importance of incorporating OMICS-based quantitative dimensional criteria, besides the classical qualitative and categorical approach. PMID:23849623
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Hsu, Han-Hsiu; Araki, Michihiro; Mochizuki, Masao; Hori, Yoshimi; Murata, Masahiro; Kahar, Prihardi; Yoshida, Takanobu; Hasunuma, Tomohisa; Kondo, Akihiko
2017-03-02
Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput "omics" methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.
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Use of omics methods to determine the mode of action of natural phytotoxins
USDA-ARS?s Scientific Manuscript database
Technology has greatly increased the power of omics methods to profile transcription, protein, and metabolite responses to phytotoxins. These methods hold promise as a tool for providing clues to the modes of action of such compounds. However, to date, only two putative modes of action have been fou...
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Clinical multi-omics strategies for the effective cancer management.
Yoo, Byong Chul; Kim, Kyung-Hee; Woo, Sang Myung; Myung, Jae Kyung
2017-08-15
Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis. In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer. Copyright © 2017 Elsevier B.V. All rights reserved.
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Precision medicine--delivering the goods?
Peer, Dan
2014-09-28
Personalized (or precision) medicine aims to individualize therapeutic interventions, based on OMICS data such as genomics, proteomics, metabolomics etc', profiling together with histopathological insights to the type, stage, and the grade of the disease, as well as on the potential response of a particular patient to a particular treatment regimen. With next generation sequencing technologies, it is now possible to identify all germline variants of an individual in an affordable cost and thus paving the way for clinicians to provide healthcare from an individual perspective. In this special issue of Cancer Letters termed "Trends in Personalized Cancer Research" we bring together physicians and scientists summarizing the state-of-the-art in precision medicine from hematological malignancies and solid tumors with the current gold standard diagnostic and therapy to basic and translational research utilizing nanotechnology and RNA interference strategies for future personalized theranostics in oncology. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Evangelatos, Nikolaos; Satyamourthy, Kapaettu; Levidou, Georgia; Brand, Helmut; Bauer, Pia; Kouskouti, Christina; Brand, Angela
2018-05-01
"-Omics" systems sciences are at the epicenter of personalized medicine and public health, and drivers of knowledge-based biotechnology innovation. Bioinformatics, a core component of omics research, is one of the disciplines that first employed Free/Libre Open Source Software (FLOSS), and thus provided a fertile ground for its further development. Understanding the use and characteristics of FLOSS deployed in the omics field is valuable for future innovation strategies, policy and funding priorities. We conducted a bibliometric, longitudinal study of the use of FLOSS in sepsis omics research from 2011 to 2015 in the United States, EU-28 and China. Because sepsis is an interdisciplinary field at the intersection of multiple omics technologies and medical specialties, it was chosen as a model innovation ecosystem for this empirical analysis, which used publicly available data. Despite development of and competition from proprietary commercial software, scholars in omics continue to employ FLOSS routinely, and independent of the type of omics technology they work with. The number of articles using FLOSS increased significantly over time in the EU-28, as opposed to the United States and China (R = 0.96, p = 0.004). Furthermore, in an era where sharing of knowledge is being strongly advocated and promoted by public agencies and social institutions, we discuss possible correlations between the use of FLOSS and various funding sources in omics research. These observations and analyses provide new insights into the use of FLOSS in sepsis omics research across three (supra)national regions. Further benchmarking studies are warranted for FLOSS trends in other omics fields and geographical settings. These could, in time, lead to the development of new composite innovation and technology use metrics in omics systems sciences and bioinformatics communities.
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Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat
Metzger, Ute; Templin, Markus F.; Plummer, Simon; Ellinger-Ziegelbauer, Heidrun; Zell, Andreas
2014-01-01
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. PMID:24830643
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CyanOmics: an integrated database of omics for the model cyanobacterium Synechococcus sp. PCC 7002.
Yang, Yaohua; Feng, Jie; Li, Tao; Ge, Feng; Zhao, Jindong
2015-01-01
Cyanobacteria are an important group of organisms that carry out oxygenic photosynthesis and play vital roles in both the carbon and nitrogen cycles of the Earth. The annotated genome of Synechococcus sp. PCC 7002, as an ideal model cyanobacterium, is available. A series of transcriptomic and proteomic studies of Synechococcus sp. PCC 7002 cells grown under different conditions have been reported. However, no database of such integrated omics studies has been constructed. Here we present CyanOmics, a database based on the results of Synechococcus sp. PCC 7002 omics studies. CyanOmics comprises one genomic dataset, 29 transcriptomic datasets and one proteomic dataset and should prove useful for systematic and comprehensive analysis of all those data. Powerful browsing and searching tools are integrated to help users directly access information of interest with enhanced visualization of the analytical results. Furthermore, Blast is included for sequence-based similarity searching and Cluster 3.0, as well as the R hclust function is provided for cluster analyses, to increase CyanOmics's usefulness. To the best of our knowledge, it is the first integrated omics analysis database for cyanobacteria. This database should further understanding of the transcriptional patterns, and proteomic profiling of Synechococcus sp. PCC 7002 and other cyanobacteria. Additionally, the entire database framework is applicable to any sequenced prokaryotic genome and could be applied to other integrated omics analysis projects. Database URL: http://lag.ihb.ac.cn/cyanomics. © The Author(s) 2015. Published by Oxford University Press.
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Omics methods for probing the mode of action of natural and synthetic phytotoxins.
Duke, Stephen O; Bajsa, Joanna; Pan, Zhiqiang
2013-02-01
For a little over a decade, omics methods (transcriptomics, proteomics, metabolomics, and physionomics) have been used to discover and probe the mode of action of both synthetic and natural phytotoxins. For mode of action discovery, the strategy for each of these approaches is to generate an omics profile for phytotoxins with known molecular targets and to compare this library of responses to the responses of compounds with unknown modes of action. Using more than one omics approach enhances the probability of success. Generally, compounds with the same mode of action generate similar responses with a particular omics method. Stress and detoxification responses to phytotoxins can be much clearer than effects directly related to the target site. Clues to new modes of action must be validated with in vitro enzyme effects or genetic approaches. Thus far, the only new phytotoxin target site discovered with omics approaches (metabolomics and physionomics) is that of cinmethylin and structurally related 5-benzyloxymethyl-1,2-isoxazolines. These omics approaches pointed to tyrosine amino-transferase as the target, which was verified by enzyme assays and genetic methods. In addition to being a useful tool of mode of action discovery, omics methods provide detailed information on genetic and biochemical impacts of phytotoxins. Such information can be useful in understanding the full impact of natural phytotoxins in both agricultural and natural ecosystems.
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A roadmap towards personalized immunology.
Delhalle, Sylvie; Bode, Sebastian F N; Balling, Rudi; Ollert, Markus; He, Feng Q
2018-01-01
Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.
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'Omics' techniques for identifying flooding-response mechanisms in soybean.
Komatsu, Setsuko; Shirasaka, Naoki; Sakata, Katsumi
2013-11-20
Plant growth and productivity are adversely influenced by various environmental stresses, which often lead to reduced seedling growth and decreased crop yields. Plants respond to stressful conditions through changes in 'omics' profiles, including transcriptomics, proteomics, and metabolomics. Linking plant phenotype to gene expression patterns, protein abundance, and metabolite accumulation is one of the main challenges for improving agricultural production. 'Omics' approaches may shed insight into the mechanisms that function in soybean in response to environmental stresses, particularly flooding by frequent rain, which occurs worldwide due to changes in global climate. Flooding causes significant reductions in the growth and yield of several crops, especially soybean. The application of 'omics' techniques may facilitate the development of flood-tolerant cultivars of soybean. In this review, the use of 'omics' techniques towards understanding the flooding-responsive mechanisms of soybeans is discussed, as the findings from these studies are expected to have applications in both breeding and agronomy. This article is part of a Special Issue entitled: Translational Plant Proteomics. Copyright © 2012 Elsevier B.V. All rights reserved.
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Daher, Ahmad; de Groot, John
2018-01-01
Tumor heterogeneity is a major factor in glioblastoma's poor response to therapy and seemingly inevitable recurrence. Only two glioblastoma drugs have received Food and Drug Administration approval since 1998, highlighting the urgent need for new therapies. Profiling "omics" analyses have helped characterize glioblastoma molecularly and have thus identified multiple molecular targets for precision medicine. These molecular targets have influenced clinical trial design; many "actionable" mutation-focused trials are underway, but because they have not yet led to therapeutic breakthroughs, new strategies for treating glioblastoma, especially those with a pharmacological functional component, remain in high demand. In that regard, high-throughput screening that allows for expedited preclinical drug testing and the use of GBM models that represent tumor heterogeneity more accurately than traditional cancer cell lines is necessary to maximize the successful translation of agents into the clinic. High-throughput screening has been successfully used in the testing, discovery, and validation of potential therapeutics in various cancer models, but it has not been extensively utilized in glioblastoma models. In this report, we describe the basic aspects of high-throughput screening and propose a modified high-throughput screening model in which ex vivo and in vivo drug testing is complemented by post-screening pharmacological, pan-omic analysis to expedite anti-glioma drugs' preclinical testing and develop predictive biomarker datasets that can aid in personalizing glioblastoma therapy and inform clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.
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Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
Argelaguet, Ricard; Velten, Britta; Arnol, Damien; Dietrich, Sascha; Zenz, Thorsten; Marioni, John C; Buettner, Florian; Huber, Wolfgang; Stegle, Oliver
2018-06-20
Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.
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A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.
Gant, Timothy W; Sauer, Ursula G; Zhang, Shu-Dong; Chorley, Brian N; Hackermüller, Jörg; Perdichizzi, Stefania; Tollefsen, Knut E; van Ravenzwaay, Ben; Yauk, Carole; Tong, Weida; Poole, Alan
2017-12-01
A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.
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Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero
2018-04-15
Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.
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Ogawa, Diogo M. O.; Moriya, Shigeharu; Tsuboi, Yuuri; Date, Yasuhiro; Prieto-da-Silva, Álvaro R. B.; Rádis-Baptista, Gandhi; Yamane, Tetsuo; Kikuchi, Jun
2014-01-01
We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems. PMID:25330259
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Ogawa, Diogo M O; Moriya, Shigeharu; Tsuboi, Yuuri; Date, Yasuhiro; Prieto-da-Silva, Álvaro R B; Rádis-Baptista, Gandhi; Yamane, Tetsuo; Kikuchi, Jun
2014-01-01
We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.
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Higdon, Roger; Earl, Rachel K.; Stanberry, Larissa; Hudac, Caitlin M.; Montague, Elizabeth; Stewart, Elizabeth; Janko, Imre; Choiniere, John; Broomall, William; Kolker, Natali
2015-01-01
Abstract Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare. PMID:25831060
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Making sense of OMICS data in population-based environmental health studies.
Kyrtopoulos, Soterios A
2013-08-01
Although experience from the application of OMICS technologies in population-based environmental health studies is still relatively limited, the accumulated evidence shows that it can allow the identification of features (genes, proteins, and metabolites), or sets of such features, which are targeted by particular exposures or correlate with disease risk. Such features or profiles can therefore serve as biomarkers of exposure or disease risk. Blood-based OMIC profiles appear to reflect to some extent events occurring in target tissues and are associated with toxicity or disease and therefore have the potential to facilitate the elucidation of exposure-disease relationships. Further progress in this direction requires better understanding of the significance of exposure-induced network perturbations for disease initiation and progression and the development of a framework that combines agnostic searches with the utilization of prior knowledge, taking account of particular elements which characterize the structure and evolution of complex systems and brings in principles of systems biology. Copyright © 2013 Wiley Periodicals, Inc.
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Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology.
Aretz, Ina; Meierhofer, David
2016-04-27
Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.
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Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology
Aretz, Ina; Meierhofer, David
2016-01-01
Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology. PMID:27128910
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Poly-Omic Prediction of Complex Traits: OmicKriging
Wheeler, Heather E.; Aquino-Michaels, Keston; Gamazon, Eric R.; Trubetskoy, Vassily V.; Dolan, M. Eileen; Huang, R. Stephanie; Cox, Nancy J.; Im, Hae Kyung
2014-01-01
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. PMID:24799323
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Integrating multi-omic features exploiting Chromosome Conformation Capture data.
Merelli, Ivan; Tordini, Fabio; Drocco, Maurizio; Aldinucci, Marco; Liò, Pietro; Milanesi, Luciano
2015-01-01
The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell's natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.
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Ozdemir, Vural; Endrenyi, Laszlo; Aynacıoğlu, Sükrü; Bragazzi, Nicola Luigi; Dandara, Collet; Dove, Edward S; Ferguson, Lynnette R; Geraci, Christy Jo; Hafen, Ernst; Kesim, Belgin Eroğlu; Kolker, Eugene; Lee, Edmund J D; Llerena, Adrian; Nacak, Muradiye; Shimoda, Kazutaka; Someya, Toshiyuki; Srivastava, Sanjeeva; Tomlinson, Brian; Vayena, Effy; Warnich, Louise; Yaşar, Umit
2014-04-01
This article announces the recipient of the 2014 inaugural Werner Kalow Responsible Innovation Prize in Global Omics and Personalized Medicine by the Pacific Rim Association for Clinical Pharmacogenetics (PRACP): Bernard Lerer, professor of psychiatry and director of the Biological Psychiatry Laboratory, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. The Werner Kalow Responsible Innovation Prize is given to an exceptional interdisciplinary scholar who has made highly innovative and enduring contributions to global omics science and personalized medicine, with both vertical and horizontal (transdisciplinary) impacts. The prize is established in memory of a beloved colleague, mentor, and friend, the late Professor Werner Kalow, who cultivated the idea and practice of pharmacogenetics in modern therapeutics commencing in the 1950s. PRACP, the prize's sponsor, is one of the longest standing learned societies in the Asia-Pacific region, and was founded by Kalow and colleagues more than two decades ago in the then-emerging field of pharmacogenetics. In announcing this inaugural prize and its winner, we seek to highlight the works of prize winner, Professor Lerer. Additionally, we contextualize the significance of the prize by recalling the life and works of Professor Kalow and providing a brief socio-technical history of the rise of pharmacogenetics and personalized medicine as a veritable form of 21(st) century scientific practice. The article also fills a void in previous social science analyses of pharmacogenetics, by bringing to the fore the works of Kalow from 1995 to 2008, when he presciently noted the rise of yet another field of postgenomics inquiry--pharmacoepigenetics--that railed against genetic determinism and underscored the temporal and spatial plasticity of genetic components of drug response, with invention of the repeated drug administration (RDA) method that estimates the dynamic heritabilities of drug response. The prize goes a long way to cultivate transgenerational capacity and broader cognizance of the concept and practice of responsible innovation as an important criterion of 21(st) century omics science and personalized medicine. A new call is presently in place for the 2016 PRACP Werner Kalow prize. Nominations can be made in support of an exceptional individual interdisciplinary scholar, or alternatively, an entire research team, from any region in the world with a record of highly innovative contributions to global omics science and/or personalized medicine, in the spirit of responsible innovation. The application process is straightforward, requiring a signed, 1500-word nomination letter (by the applicant or sponsor) submitted not later than May 31, 2015.
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The human early-life exposome (HELIX): project rationale and design.
Vrijheid, Martine; Slama, Rémy; Robinson, Oliver; Chatzi, Leda; Coen, Muireann; van den Hazel, Peter; Thomsen, Cathrine; Wright, John; Athersuch, Toby J; Avellana, Narcis; Basagaña, Xavier; Brochot, Celine; Bucchini, Luca; Bustamante, Mariona; Carracedo, Angel; Casas, Maribel; Estivill, Xavier; Fairley, Lesley; van Gent, Diana; Gonzalez, Juan R; Granum, Berit; Gražulevičienė, Regina; Gutzkow, Kristine B; Julvez, Jordi; Keun, Hector C; Kogevinas, Manolis; McEachan, Rosemary R C; Meltzer, Helle Margrete; Sabidó, Eduard; Schwarze, Per E; Siroux, Valérie; Sunyer, Jordi; Want, Elizabeth J; Zeman, Florence; Nieuwenhuijsen, Mark J
2014-06-01
Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The "exposome" concept encompasses the totality of exposures from conception onward, complementing the genome. The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the "early-life exposome." Here we describe the general design of the project. In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures. HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.
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Karaosmanoglu, Kubra; Sayar, Nihat Alpagu; Kurnaz, Isil Aksan; Akbulut, Berna Sariyar
2014-01-01
Postgenomics drug development is undergoing major transformation in the age of multi-omics studies and drug repositioning. Rather than applications solely in personalized medicine, omics science thus additionally offers a better understanding of a broader range of drug targets and drug repositioning. Berberine is an isoquinoline alkaloid found in many medicinal plants. We report here a whole genome microarray study in tandem with proteomics techniques for mining the plethora of targets that are putatively involved in the antimicrobial activity of berberine against Escherichia coli. We found DNA replication/repair and transcription to be triggered by berberine, indicating that nucleic acids, in general, are among its targets. Our combined transcriptomics and proteomics multi-omics findings underscore that, in the presence of berberine, cell wall or cell membrane transport and motility-related functions are also specifically regulated. We further report a general decline in metabolism, as seen by repression of genes in carbohydrate and amino acid metabolism, energy production, and conversion. An involvement of multidrug efflux pumps, as well as reduced membrane permeability for developing resistance against berberine in E. coli was noted. Collectively, these findings offer original and significant leads for omics-guided drug discovery and future repositioning approaches in the postgenomics era, using berberine as a multi-omics case study.
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Renal injury in neonates: use of "omics" for developing precision medicine in neonatology.
Joshi, Mandar S; Montgomery, Kelsey A; Giannone, Peter J; Bauer, John A; Hanna, Mina H
2017-01-01
Preterm birth is associated with increased risks of morbidity and mortality along with increased healthcare costs. Advances in medicine have enhanced survival for preterm infants but the overall incidence of major morbidities has changed very little. Abnormal renal development is an important consequence of premature birth. Acute kidney injury (AKI) in the neonatal period is multifactorial and may increase lifetime risk of chronic kidney disease.Traditional biomarkers in newborns suffer from considerable confounders, limiting their use for early identification of AKI. There is a need to develop novel biomarkers that can identify, in real time, the evolution of renal dysfunction in an early diagnostic, monitoring and prognostic fashion. Use of "omics", particularly metabolomics, may provide valuable information regarding functional pathways underlying AKI and prediction of clinical outcomes.The emerging knowledge generated by the application of "omics" (genomics, proteomics, metabolomics) in neonatology provides new insights that can help to identify markers of early diagnosis, disease progression, and identify new therapeutic targets. Additionally, omics will have major implications in the field of personalized healthcare in the future. Here, we will review the current knowledge of different omics technologies in neonatal-perinatal medicine including biomarker discovery, defining as yet unrecognized biologic therapeutic targets, and linking of omics to relevant standard indices and long-term outcomes.
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A statistical framework for applying RNA profiling to chemical hazard detection.
Kostich, Mitchell S
2017-12-01
Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package. Published by Elsevier Ltd.
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Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro
2015-01-01
Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034
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[OMICS AND BIG DATA, MAJOR ADVANCES TOWARDS PERSONALIZED MEDICINE OF THE FUTURE?].
Scheen, A J
2015-01-01
The increasing interest for personalized medicine evolves together with two major technological advances. First, the new-generation, rapid and less expensive, DNA sequencing method, combined with remarkable progresses in molecular biology leading to the post-genomic era (transcriptomics, proteomics, metabolomics). Second, the refinement of computing tools (IT), which allows the immediate analysis of a huge amount of data (especially, those resulting from the omics approaches) and, thus, creates a new universe for medical research, that of analyzed by computerized modelling. This article for scientific communication and popularization briefly describes the main advances in these two fields of interest. These technological progresses are combined with those occurring in communication, which makes possible the development of artificial intelligence. These major advances will most probably represent the grounds of the future personalized medicine.
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Multi-omic profiles of hepatic metabolism in TPN-fed preterm pigs
USDA-ARS?s Scientific Manuscript database
New generation lipid emulsions comprised of fish oil or blends of soybean/fish/medium chain triglyceride/olive oil are emerging that result in favorable clinical metabolic outcomes in pediatric populations. Our aim was to characterize the lipidodomic, metabolomic, and transcriptomic profiles these ...
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Omics Research on the International Space Station
NASA Technical Reports Server (NTRS)
Love, John
2015-01-01
The International Space Station (ISS) is an orbiting laboratory whose goals include advancing science and technology research. Completion of ISS assembly ushered a new era focused on utilization, encompassing multiple disciplines such as Biology and Biotechnology, Physical Sciences, Technology Development and Demonstration, Human Research, Earth and Space Sciences, and Educational Activities. The research complement planned for upcoming ISS Expeditions 45&46 includes several investigations in the new field of omics, which aims to collectively characterize sets of biomolecules (e.g., genomic, epigenomic, transcriptomic, proteomic, and metabolomic products) that translate into organismic structure and function. For example, Multi-Omics is a JAXA investigation that analyzes human microbial metabolic cross-talk in the space ecosystem by evaluating data from immune dysregulation biomarkers, metabolic profiles, and microbiota composition. The NASA OsteoOmics investigation studies gravitational regulation of osteoblast genomics and metabolism. Tissue Regeneration uses pan-omics approaches with cells cultured in bioreactors to characterize factors involved in mammalian bone tissue regeneration in microgravity. Rodent Research-3 includes an experiment that implements pan-omics to evaluate therapeutically significant molecular circuits, markers, and biomaterials associated with microgravity wound healing and tissue regeneration in bone defective rodents. The JAXA Mouse Epigenetics investigation examines molecular alterations in organ specific gene expression patterns and epigenetic modifications, and analyzes murine germ cell development during long term spaceflight. Lastly, Twins Study ("Differential effects of homozygous twin astronauts associated with differences in exposure to spaceflight factors"), NASA's first foray into human omics research, applies integrated analyses to assess biomolecular responses to physical, physiological, and environmental stressors associated with spaceflight.
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Overview of 'Omics Technologies for Military Occupational Health Surveillance and Medicine.
Bradburne, Christopher; Graham, David; Kingston, H M; Brenner, Ruth; Pamuku, Matt; Carruth, Lucy
2015-10-01
Systems biology ('omics) technologies are emerging as tools for the comprehensive analysis and monitoring of human health. In order for these tools to be used in military medicine, clinical sampling and biobanking will need to be optimized to be compatible with downstream processing and analysis for each class of molecule measured. This article provides an overview of 'omics technologies, including instrumentation, tools, and methods, and their potential application for warfighter exposure monitoring. We discuss the current state and the potential utility of personalized data from a variety of 'omics sources including genomics, epigenomics, transcriptomics, metabolomics, proteomics, lipidomics, and efforts to combine their use. Issues in the "sample-to-answer" workflow, including collection and biobanking are discussed, as well as national efforts for standardization and clinical interpretation. Establishment of these emerging capabilities, along with accurate xenobiotic monitoring, for the Department of Defense could provide new and effective tools for environmental health monitoring at all duty stations, including deployed locations. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.
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Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro
2015-01-01
Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.
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Dorel, Mathurin; Viara, Eric; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna
2017-01-01
Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease. NaviCom is available at https://navicom.curie.fr. © The Author(s) 2017. Published by Oxford University Press.
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Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui
2017-07-15
New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier Inc.
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Malentacchi, Francesca; Mancini, Irene; Brandslund, Ivan; Vermeersch, Pieter; Schwab, Matthias; Marc, Janja; van Schaik, Ron H N; Siest, Gerard; Theodorsson, Elvar; Pazzagli, Mario; Di Resta, Chiara
2015-06-01
Developments in "-omics" are creating a paradigm shift in laboratory medicine leading to personalized medicine. This allows the increase in diagnostics and therapeutics focused on individuals rather than populations. In order to investigate whether laboratory medicine is ready to play a key role in the integration of personalized medicine in routine health care and set the state-of-the-art knowledge about personalized medicine and laboratory medicine in Europe, a questionnaire was constructed under the auspices of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and the European Society of Pharmacogenomics and Personalised Therapy (ESPT). The answers of the participating laboratory medicine professionals indicate that they are aware that personalized medicine can represent a new and promising health model, and that laboratory medicine should play a key role in supporting the implementation of personalized medicine in the clinical setting. Participants think that the current organization of laboratory medicine needs additional/relevant implementations such as (i) new technological facilities in -omics; (ii) additional training for the current personnel focused on the new methodologies; (iii) incorporation in the laboratory of new competencies in data interpretation and counseling; and (iv) cooperation and collaboration among professionals of different disciplines to integrate information according to a personalized medicine approach.
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Dwivedi, Bhakti; Kowalski, Jeanne
2018-01-01
While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/.
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Dwivedi, Bhakti
2018-01-01
While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/. PMID:29415010
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American Society of Human Genetics
... and Background Risk October 20, 2017 Personal Omics Data Informative for Precision Health and Preventive Care October 20, 2017 Physical Inactivity and Restless Sleep Exacerbate Genetic Risk of Obesity October 20, 2017 ASHG 2018 ...
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Rutka, James; Martin, Joseph; Kılıç, Türker
2014-12-01
The Science-in-Backstage interviews aim to share experiences by global medical and life sciences thought leaders on emergent technologies and novel scientific, medical, and educational practices, situating them in both a historical and contemporary science context so as to "look into the biotechnology and innovation futures" reflexively and intelligently. OMICS systems diagnostics and personalized medicine are greatly impacting brain surgery, not to forget the training of the next generation of neurosurgeons. What do the futures hold for the practice of, and education in 21(st) century brain surgery in the age of OMICS systems science, personalized medicine, and the use of simulation in surgeon training? James Rutka is a clinician scientist and a world leader in diagnosis and treatment of brain tumors. He is Professor and Chair of the Department of Surgery at the Faculty of Medicine, University of Toronto, a President Emeritus of the American Association of Neurological Surgeons, and Editor-in-Chief of the Journal of Neurosurgery. Professor Rutka was interviewed for the global medical, biotechnology, and life sciences readership of the OMICS: A Journal of Integrative Biology to speak on these pressing questions in his personal capacity as an independent senior scholar. The issues debated in the present interview are of broad relevance for 21(st) century surgery and postgenomics medicine. The interviewers were Professor Joseph B. Martin, Harvard Medical School Dean Emeritus in Boston and Joint Dean of Medicine at Bahçeşehir University in İstanbul, and the author of "Alfalfa to Ivy: Memoir of a Harvard Medical School Dean," and Professor Türker Kılıç, Dean of Medicine at Bahçeşehir University in İstanbul, and an elected member of the Turkish Academy of Sciences.
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Biomarkers intersect with the exposome
Rappaport, Stephen M.
2016-01-01
The exposome concept promotes use of omic tools for discovering biomarkers of exposure and biomarkers of disease in studies of diseased and healthy populations. A two-stage scheme is presented for profiling omic features in serum to discover molecular biomarkers and then for applying these biomarkers in follow-up studies. The initial component, referred to as an exposome-wide-association study (EWAS), employs metabolomics and proteomics to interrogate the serum exposome and, ultimately, to identify, validate and differentiate biomarkers of exposure and biomarkers of disease. Follow-up studies employ knowledge-driven designs to explore disease causality, prevention, diagnosis, prognosis and treatment. PMID:22672124
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Dagostino, Concetta; De Gregori, Manuela; Gieger, Christian; Manz, Judith; Gudelj, Ivan; Lauc, Gordan; Divizia, Laura; Wang, Wei; Sim, Moira; Pemberton, Iain K; MacDougall, Jane; Williams, Frances; Van Zundert, Jan; Primorac, Dragan; Aulchenko, Yurii; Kapural, Leonardo; Allegri, Massimo
2017-01-01
Chronic low back pain (CLBP) is one of the most common medical conditions, ranking as the greatest contributor to global disability and accounting for huge societal costs based on the Global Burden of Disease 2010 study. Large genetic and -omics studies provide a promising avenue for the screening, development and validation of biomarkers useful for personalized diagnosis and treatment (precision medicine). Multicentre studies are needed for such an effort, and a standardized and homogeneous approach is vital for recruitment of large numbers of participants among different centres (clinical and laboratories) to obtain robust and reproducible results. To date, no validated standard operating procedures (SOPs) for genetic/-omics studies in chronic pain have been developed. In this study, we validated an SOP model that will be used in the multicentre (5 centres) retrospective "PainOmics" study, funded by the European Community in the 7th Framework Programme, which aims to develop new biomarkers for CLBP through three different -omics approaches: genomics, glycomics and activomics. The SOPs describe the specific procedures for (1) blood collection, (2) sample processing and storage, (3) shipping details and (4) cross-check testing and validation before assays that all the centres involved in the study have to follow. Multivariate analysis revealed the absolute specificity and homogeneity of the samples collected by the five centres for all genetics, glycomics and activomics analyses. The SOPs used in our multicenter study have been validated. Hence, they could represent an innovative tool for the correct management and collection of reliable samples in other large-omics-based multicenter studies.
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Suarez-Kurtz, Guilherme; Stenne, Raphaëlle; Somogyi, Andrew A.; Someya, Toshiyuki; Kayaalp, S. Oğuz; Kolker, Eugene
2009-01-01
Abstract Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype–phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment. PMID:19290811
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Ozdemir, Vural; Suarez-Kurtz, Guilherme; Stenne, Raphaëlle; Somogyi, Andrew A; Someya, Toshiyuki; Kayaalp, S Oğuz; Kolker, Eugene
2009-02-01
Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype-phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment.
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Ali-Khan, Sarah E; Black, Lee; Palmour, Nicole; Hallett, Michael T; Avard, Denise
2015-01-01
There have been multiple calls for explicit integration of ethical, legal, and social issues (ELSI) in health technology assessment (HTA) and addressing ELSI has been highlighted as key in optimizing benefits in the Omics/Personalized Medicine field. This study examines HTAs of an early clinical example of Personalized Medicine (gene expression profile tests [GEP] for breast cancer prognosis) aiming to: (i) identify ELSI; (ii) assess whether ELSIs are implicitly or explicitly addressed; and (iii) report methodology used for ELSI integration. A systematic search for HTAs (January 2004 to September 2012), followed by descriptive and qualitative content analysis. Seventeen HTAs for GEP were retrieved. Only three (18%) explicitly presented ELSI, and only one reported methodology. However, all of the HTAs included implicit ELSI. Eight themes of implicit and explicit ELSI were identified. "Classical" ELSI including privacy, informed consent, and concerns about limited patient/clinician genetic literacy were always presented explicitly. Some ELSI, including the need to understand how individual patients' risk tolerances affect clinical decision-making after reception of GEP results, were presented both explicitly and implicitly in HTAs. Others, such as concern about evidentiary deficiencies for clinical utility of GEP tests, occurred only implicitly. Despite a wide variety of important ELSI raised, these were rarely explicitly addressed in HTAs. Explicit treatment would increase their accessibility to decision-makers, and may augment HTA efficiency maximizing their utility. This is particularly important where complex Personalized Medicine applications are rapidly expanding choices for patients, clinicians and healthcare systems.
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Perspective for Aquaponic Systems: "Omic" Technologies for Microbial Community Analysis.
Munguia-Fragozo, Perla; Alatorre-Jacome, Oscar; Rico-Garcia, Enrique; Torres-Pacheco, Irineo; Cruz-Hernandez, Andres; Ocampo-Velazquez, Rosalia V; Garcia-Trejo, Juan F; Guevara-Gonzalez, Ramon G
2015-01-01
Aquaponics is the combined production of aquaculture and hydroponics, connected by a water recirculation system. In this productive system, the microbial community is responsible for carrying out the nutrient dynamics between the components. The nutrimental transformations mainly consist in the transformation of chemical species from toxic compounds into available nutrients. In this particular field, the microbial research, the "Omic" technologies will allow a broader scope of studies about a current microbial profile inside aquaponics community, even in those species that currently are unculturable. This approach can also be useful to understand complex interactions of living components in the system. Until now, the analog studies were made to set up the microbial characterization on recirculation aquaculture systems (RAS). However, microbial community composition of aquaponics is still unknown. "Omic" technologies like metagenomic can help to reveal taxonomic diversity. The perspectives are also to begin the first attempts to sketch the functional diversity inside aquaponic systems and its ecological relationships. The knowledge of the emergent properties inside the microbial community, as well as the understanding of the biosynthesis pathways, can derive in future biotechnological applications. Thus, the aim of this review is to show potential applications of current "Omic" tools to characterize the microbial community in aquaponic systems.
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Molecular signatures from omics data: from chaos to consensus.
Sung, Jaeyun; Wang, Yuliang; Chandrasekaran, Sriram; Witten, Daniela M; Price, Nathan D
2012-08-01
In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Santurro, Alessandro; Vullo, Anna Maria; Borro, Marina; Gentile, Giovanna; La Russa, Raffaele; Simmaco, Maurizio; Frati, Paola; Fineschi, Vittorio
2017-01-01
Personalized medicine (PM), included in P5 medicine (Personalized, Predictive, Preventive, Participative and Precision medicine) is an innovative approach to the patient, emerging from the need to tailor and to fit the profile of each individual. PM promises to dramatically impact also on forensic sciences and justice system in ways we are only beginning to understand. The application of omics (genomic, transcriptomics, epigenetics/imprintomics, proteomic and metabolomics) is ever more fundamental in the so called "molecular autopsy". Emerging fields of interest in forensic pathology are represented by diagnosis and detection of predisposing conditions to fatal thromboembolic and hypertensive events, determination of genetic variants related to sudden death, such as congenital long QT syndromes, demonstration of lesions vitality, identification of biological matrices and species diagnosis of a forensic trace on crime scenes without destruction of the DNA. The aim of this paper is to describe the state-of-art in the application of personalized medicine in forensic sciences, to understand the possibilities of integration in routine investigation of these procedures with classical post-mortem studies and to underline the importance of these new updates in medical examiners' armamentarium in determining cause of death or contributing factors to death. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Multi-omic Mitoprotease Profiling Defines a Role for Oct1p in Coenzyme Q Production.
Veling, Mike T; Reidenbach, Andrew G; Freiberger, Elyse C; Kwiecien, Nicholas W; Hutchins, Paul D; Drahnak, Michael J; Jochem, Adam; Ulbrich, Arne; Rush, Matthew J P; Russell, Jason D; Coon, Joshua J; Pagliarini, David J
2017-12-07
Mitoproteases are becoming recognized as key regulators of diverse mitochondrial functions, although their direct substrates are often difficult to discern. Through multi-omic profiling of diverse Saccharomyces cerevisiae mitoprotease deletion strains, we predicted numerous associations between mitoproteases and distinct mitochondrial processes. These include a strong association between the mitochondrial matrix octapeptidase Oct1p and coenzyme Q (CoQ) biosynthesis-a pathway essential for mitochondrial respiration. Through Edman sequencing and in vitro and in vivo biochemistry, we demonstrated that Oct1p directly processes the N terminus of the CoQ-related methyltransferase, Coq5p, which markedly improves its stability. A single mutation to the Oct1p recognition motif in Coq5p disrupted its processing in vivo, leading to CoQ deficiency and respiratory incompetence. This work defines the Oct1p processing of Coq5p as an essential post-translational event for proper CoQ production. Additionally, our data visualization tool enables efficient exploration of mitoprotease profiles that can serve as the basis for future mechanistic investigations. Copyright © 2017 Elsevier Inc. All rights reserved.
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A statistical framework for applying RNA profiling to chemical hazard detection
Use of ‘omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-...
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Aynacıoğlu, Şükrü; Bragazzi, Nicola Luigi; Dandara, Collet; Dove, Edward S.; Ferguson, Lynnette R.; Geraci, Christy Jo; Hafen, Ernst; Kesim, Belgin Eroğlu; Kolker, Eugene; Lee, Edmund J.D.; LLerena, Adrian; Nacak, Muradiye; Shimoda, Kazutaka; Someya, Toshiyuki; Srivastava, Sanjeeva; Tomlinson, Brian; Vayena, Effy; Warnich, Louise; Yaşar, Ümit
2014-01-01
Abstract This article announces the recipient of the 2014 inaugural Werner Kalow Responsible Innovation Prize in Global Omics and Personalized Medicine by the Pacific Rim Association for Clinical Pharmacogenetics (PRACP): Bernard Lerer, professor of psychiatry and director of the Biological Psychiatry Laboratory, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. The Werner Kalow Responsible Innovation Prize is given to an exceptional interdisciplinary scholar who has made highly innovative and enduring contributions to global omics science and personalized medicine, with both vertical and horizontal (transdisciplinary) impacts. The prize is established in memory of a beloved colleague, mentor, and friend, the late Professor Werner Kalow, who cultivated the idea and practice of pharmacogenetics in modern therapeutics commencing in the 1950s. PRACP, the prize's sponsor, is one of the longest standing learned societies in the Asia-Pacific region, and was founded by Kalow and colleagues more than two decades ago in the then-emerging field of pharmacogenetics. In announcing this inaugural prize and its winner, we seek to highlight the works of prize winner, Professor Lerer. Additionally, we contextualize the significance of the prize by recalling the life and works of Professor Kalow and providing a brief socio-technical history of the rise of pharmacogenetics and personalized medicine as a veritable form of 21st century scientific practice. The article also fills a void in previous social science analyses of pharmacogenetics, by bringing to the fore the works of Kalow from 1995 to 2008, when he presciently noted the rise of yet another field of postgenomics inquiry—pharmacoepigenetics—that railed against genetic determinism and underscored the temporal and spatial plasticity of genetic components of drug response, with invention of the repeated drug administration (RDA) method that estimates the dynamic heritabilities of drug response. The prize goes a long way to cultivate transgenerational capacity and broader cognizance of the concept and practice of responsible innovation as an important criterion of 21st century omics science and personalized medicine. A new call is presently in place for the 2016 PRACP Werner Kalow prize. Nominations can be made in support of an exceptional individual interdisciplinary scholar, or alternatively, an entire research team, from any region in the world with a record of highly innovative contributions to global omics science and/or personalized medicine, in the spirit of responsible innovation. The application process is straightforward, requiring a signed, 1500-word nomination letter (by the applicant or sponsor) submitted not later than May 31, 2015. Wanderer, your footsteps are the road, and nothing more; wanderer, there is no road, the road is made by walking. By walking one makes the road, and upon glancing behind one sees the path that never will be trod again. Wanderer, there is no road – Only wakes upon the sea. Antonio Machado (1875–1939) “The real voyage of discovery consists not in seeking new landscapes but in having new eyes.” Marcel Proust (1871–1922) PMID:24649998
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Vitali, Francesca; Li, Qike; Schissler, A Grant; Berghout, Joanne; Kenost, Colleen; Lussier, Yves A
2017-12-18
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments. © The Author 2017. Published by Oxford University Press.
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77 FR 16246 - National Heart, Lung, and Blood Institute; Notice of Closed Meeting
Federal Register 2010, 2011, 2012, 2013, 2014
2012-03-20
... unwarranted invasion of personal privacy. Name of Committee: National Heart, Lung, and Blood Institute Special Emphasis Panel; Omics and Genetic Analysis. Date: April 6, 2012. Time: 8:30 a.m. to 12 p.m. Agenda: To...
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Li, Caixia; Tan, Xing Fei; Lim, Teck Kwang; Lin, Qingsong; Gong, Zhiyuan
2016-04-13
Omic approaches have been increasingly used in the zebrafish model for holistic understanding of molecular events and mechanisms of tissue functions. However, plasma is rarely used for omic profiling because of the technical challenges in collecting sufficient blood. In this study, we employed two mass spectrometric (MS) approaches for a comprehensive characterization of zebrafish plasma proteome, i.e. conventional shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) for an overview study and quantitative SWATH (Sequential Window Acquisition of all THeoretical fragment-ion spectra) for comparison between genders. 959 proteins were identified in the shotgun profiling with estimated concentrations spanning almost five orders of magnitudes. Other than the presence of a few highly abundant female egg yolk precursor proteins (vitellogenins), the proteomic profiles of male and female plasmas were very similar in both number and abundance and there were basically no other highly gender-biased proteins. The types of plasma proteins based on IPA (Ingenuity Pathway Analysis) classification and tissue sources of production were also very similar. Furthermore, the zebrafish plasma proteome shares significant similarities with human plasma proteome, in particular in top abundant proteins including apolipoproteins and complements. Thus, the current study provided a valuable dataset for future evaluation of plasma proteins in zebrafish.
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Li, Caixia; Tan, Xing Fei; Lim, Teck Kwang; Lin, Qingsong; Gong, Zhiyuan
2016-01-01
Omic approaches have been increasingly used in the zebrafish model for holistic understanding of molecular events and mechanisms of tissue functions. However, plasma is rarely used for omic profiling because of the technical challenges in collecting sufficient blood. In this study, we employed two mass spectrometric (MS) approaches for a comprehensive characterization of zebrafish plasma proteome, i.e. conventional shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) for an overview study and quantitative SWATH (Sequential Window Acquisition of all THeoretical fragment-ion spectra) for comparison between genders. 959 proteins were identified in the shotgun profiling with estimated concentrations spanning almost five orders of magnitudes. Other than the presence of a few highly abundant female egg yolk precursor proteins (vitellogenins), the proteomic profiles of male and female plasmas were very similar in both number and abundance and there were basically no other highly gender-biased proteins. The types of plasma proteins based on IPA (Ingenuity Pathway Analysis) classification and tissue sources of production were also very similar. Furthermore, the zebrafish plasma proteome shares significant similarities with human plasma proteome, in particular in top abundant proteins including apolipoproteins and complements. Thus, the current study provided a valuable dataset for future evaluation of plasma proteins in zebrafish. PMID:27071722
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From Sample to Multi-Omics Conclusions in under 48 Hours
Navas-Molina, Jose A.; Hyde, Embriette R.; Vázquez-Baeza, Yoshiki; Humphrey, Greg; Gaffney, James; Minich, Jeremiah J.; Melnik, Alexey V.; Herschend, Jakob; DeReus, Jeff; Durant, Austin; Dutton, Rachel J.; Khosroheidari, Mahdieh; Green, Clifford; da Silva, Ricardo; Dorrestein, Pieter C.; Knight, Rob
2016-01-01
ABSTRACT Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology. PMID:27822524
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Fitó, Montserrat; Melander, Olle; Martínez, José Alfredo; Toledo, Estefanía; Carpéné, Christian; Corella, Dolores
2016-01-01
Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition. PMID:27598147
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Min, Li; Zhao, Shengguo; Tian, He; Zhou, Xu; Zhang, Yangdong; Li, Songli; Yang, Hongjian; Zheng, Nan; Wang, Jiaqi
2017-06-01
Heat stress (HS) negatively affects various industries that rely on animal husbandry, particularly the dairy industry. A better understanding of metabolic responses in HS dairy cows is necessary to elucidate the physiological mechanisms of HS and offer a new perspective for future research. In this paper, we review the current knowledge of responses of body metabolism (lipid, carbohydrate, and protein), endocrine profiles, and bovine mammary epithelial cells during HS. Furthermore, we summarize the metabolomics and proteomics data that have revealed the metabolite profiles and differentially expressed proteins that are a feature of HS in dairy cows. Analysis of metabolic changes and "omics" data demonstrated that HS is characterized by reduced lipolysis, increased glycolysis, and catabolism of amino acids in dairy cows. Here, analysis of the impairment of immune function during HS and of the inflammation that arises after long-term HS might suggest new strategies to ameliorate the effects of HS in dairy production.
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Profiling microbial lignocellulose degradation and utilization by emergent omics technologies
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rosnow, Joshua J.; Anderson, Lindsey N.; Nair, Reji N.
2016-07-20
The use of plant materials to generate renewable biofuels and other high-value chemicals is the sustainable and preferable option, but will require considerable improvements to increase the rate and efficiency of lignocellulose depolymerization. This review highlights novel and emergent technologies that are being developed and deployed to characterize the process of lignocellulose degradation. The review will also illustrate how microbial communities deconstruct and metabolize lignocellulose by identifying the necessary genes and enzyme activities along with the reaction products. These technologies include multi-omic measurements, cell sorting and isolation, nuclear magnetic resonance spectroscopy (NMR), activity-based protein profiling, and direct measurement of enzymemore » activity. The recalcitrant nature of lignocellulose necessitates the need to characterize the methods microbes employ to deconstruct lignocellulose to inform new strategies on how to greatly improve biofuel conversion processes. New technologies are yielding important insights into microbial functions and strategies employed to degrade lignocellulose, providing a mechanistic blueprint to advance biofuel production.« less
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Profiling microbial lignocellulose degradation and utilization by emergent omics technologies.
Rosnow, Joshua J; Anderson, Lindsey N; Nair, Reji N; Baker, Erin S; Wright, Aaron T
2017-08-01
The use of plant materials to generate renewable biofuels and other high-value chemicals is the sustainable and preferable option, but will require considerable improvements to increase the rate and efficiency of lignocellulose depolymerization. This review highlights novel and emerging technologies that are being developed and deployed to characterize the process of lignocellulose degradation. The review will also illustrate how microbial communities deconstruct and metabolize lignocellulose by identifying the necessary genes and enzyme activities along with the reaction products. These technologies include multi-omic measurements, cell sorting and isolation, nuclear magnetic resonance spectroscopy (NMR), activity-based protein profiling, and direct measurement of enzyme activity. The recalcitrant nature of lignocellulose necessitates the need to characterize the methods microbes employ to deconstruct lignocellulose to inform new strategies on how to greatly improve biofuel conversion processes. New technologies are yielding important insights into microbial functions and strategies employed to degrade lignocellulose, providing a mechanistic blueprint in order to advance biofuel production.
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Zhai, Peng; Yang, Longshu; Guo, Xiao; Wang, Zhe; Guo, Jiangtao; Wang, Xiaoqi; Zhu, Huaiqiu
2017-10-02
During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community. We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples. MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .
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Martyniuk, Christopher J
2018-04-01
Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs. In recent years, one overarching objective of many has been to address fundamental questions concerning experimental design and the robustness of data collected under the broad umbrella of environmental genomics. These questions include: (1) the likelihood that molecular profiles return to a predefined baseline level following remediation efforts, (2) how reference site selection in an urban environment influences interpretation of omics data and (3) what is the most appropriate species to monitor in the environment from an omics point of view. In addition, inter-genomics studies have been conducted to assess transcriptome reproducibility in toxicology studies. One lesson learned from inter-genomics studies is that there are core molecular networks that can be identified by multiple laboratories using the same platform. This supports the idea that "omics-networks" defined a priori may be a viable approach moving forward for evaluating environmental impacts over time. Both spatial and temporal variability in ecosystem structure is expected to influence molecular responses to environmental stressors, and it is important to recognize how these variables, as well as individual factor (i.e. sex, age, maturation), may confound interpretation of network responses to chemicals. This mini-review synthesizes the progress made towards adopting these tools into environmental monitoring and identifies future challenges to be addressed, as we move into the next era of high throughput sequencing. A conceptual framework for validating and incorporating molecular networks into environmental monitoring programs is proposed. As AOPs become more defined and their potential in environmental monitoring assessments becomes more recognized, the AOP framework may prove to be the conduit between omics and penultimate ecological responses for environmental risk assessments. Copyright © 2018 Elsevier B.V. All rights reserved.
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Sadanandam, Anguraj; Wullschleger, Stephan; Lyssiotis, Costas A.; Grötzinger, Carsten; Barbi, Stefano; Bersani, Samantha; Körner, Jan; Wafy, Ismael; Mafficini, Andrea; Lawlor, Rita T.; Simbolo, Michele; Asara, John M.; Bläker, Hendrik; Cantley, Lewis C.; Wiedenmann, Bertram; Scarpa, Aldo; Hanahan, Douglas
2016-01-01
Seeking to assess the representative and instructive value of an engineered mouse model of pancreatic neuroendocrine tumors (PanNET) for its cognate human cancer, we profiled and compared mRNA and miRNA transcriptomes of tumors from both. Mouse PanNET tumors could be classified into two distinctive subtypes, well-differentiated islet/insulinoma tumors (IT) and poorly differentiated tumors associated with liver metastases, dubbed metastasis-like primary (MLP). Human PanNETs were independently classified into these same two subtypes, along with a third, specific gene mutation–enriched subtype. The MLP subtypes in human and mouse were similar to liver metastases in terms of miRNA and mRNA transcriptome profiles and signature genes. The human/mouse MLP subtypes also similarly expressed genes known to regulate early pancreas development, whereas the IT subtypes expressed genes characteristic of mature islet cells, suggesting different tumorigenesis pathways. In addition, these subtypes exhibit distinct metabolic profiles marked by differential pyruvate metabolism, substantiating the significance of their separate identities. SIGNIFICANCE This study involves a comprehensive cross-species integrated analysis of multi-omics profiles and histology to stratify PanNETs into subtypes with distinctive characteristics. We provide support for the RIP1-TAG2 mouse model as representative of its cognate human cancer with prospects to better understand PanNET heterogeneity and consider future applications of personalized cancer therapy. PMID:26446169
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Sadanandam, Anguraj; Wullschleger, Stephan; Lyssiotis, Costas A; Grötzinger, Carsten; Barbi, Stefano; Bersani, Samantha; Körner, Jan; Wafy, Ismael; Mafficini, Andrea; Lawlor, Rita T; Simbolo, Michele; Asara, John M; Bläker, Hendrik; Cantley, Lewis C; Wiedenmann, Bertram; Scarpa, Aldo; Hanahan, Douglas
2015-12-01
Seeking to assess the representative and instructive value of an engineered mouse model of pancreatic neuroendocrine tumors (PanNET) for its cognate human cancer, we profiled and compared mRNA and miRNA transcriptomes of tumors from both. Mouse PanNET tumors could be classified into two distinctive subtypes, well-differentiated islet/insulinoma tumors (IT) and poorly differentiated tumors associated with liver metastases, dubbed metastasis-like primary (MLP). Human PanNETs were independently classified into these same two subtypes, along with a third, specific gene mutation-enriched subtype. The MLP subtypes in human and mouse were similar to liver metastases in terms of miRNA and mRNA transcriptome profiles and signature genes. The human/mouse MLP subtypes also similarly expressed genes known to regulate early pancreas development, whereas the IT subtypes expressed genes characteristic of mature islet cells, suggesting different tumorigenesis pathways. In addition, these subtypes exhibit distinct metabolic profiles marked by differential pyruvate metabolism, substantiating the significance of their separate identities. This study involves a comprehensive cross-species integrated analysis of multi-omics profiles and histology to stratify PanNETs into subtypes with distinctive characteristics. We provide support for the RIP1-TAG2 mouse model as representative of its cognate human cancer with prospects to better understand PanNET heterogeneity and consider future applications of personalized cancer therapy. ©2015 American Association for Cancer Research.
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Lourenço, Anália; Ferreira, Andreia; Veiga, Nuno; Machado, Idalina; Pereira, Maria Olivia; Azevedo, Nuno F
2012-01-01
Consortia of microorganisms, commonly known as biofilms, are attracting much attention from the scientific community due to their impact in human activity. As biofilm research grows to be a data-intensive discipline, the need for suitable bioinformatics approaches becomes compelling to manage and validate individual experiments, and also execute inter-laboratory large-scale comparisons. However, biofilm data is widespread across ad hoc, non-standardized individual files and, thus, data interchange among researchers, or any attempt of cross-laboratory experimentation or analysis, is hardly possible or even attempted. This paper presents BiofOmics, the first publicly accessible Web platform specialized in the management and analysis of data derived from biofilm high-throughput studies. The aim is to promote data interchange across laboratories, implementing collaborative experiments, and enable the development of bioinformatics tools in support of the processing and analysis of the increasing volumes of experimental biofilm data that are being generated. BiofOmics' data deposition facility enforces data structuring and standardization, supported by controlled vocabulary. Researchers are responsible for the description of the experiments, their results and conclusions. BiofOmics' curators interact with submitters only to enforce data structuring and the use of controlled vocabulary. Then, BiofOmics' search facility makes publicly available the profile and data associated with a submitted study so that any researcher can profit from these standardization efforts to compare similar studies, generate new hypotheses to be tested or even extend the conditions experimented in the study. BiofOmics' novelty lies in its support to standardized data deposition, the availability of computerizable data files and the free-of-charge dissemination of biofilm studies across the community. Hopefully, this will open promising research possibilities, namely the comparison of results between different laboratories, the reproducibility of methods within and between laboratories, and the development of guidelines and standardized protocols for biofilm formation operating procedures and analytical methods.
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Ghan, Ryan; Van Sluyter, Steven C; Hochberg, Uri; Degu, Asfaw; Hopper, Daniel W; Tillet, Richard L; Schlauch, Karen A; Haynes, Paul A; Fait, Aaron; Cramer, Grant R
2015-11-16
Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity (°Brix-to-titratable acidity ratio) from the same experimental vineyard. The cultivars were exposed to a mild, seasonal water-deficit treatment from fruit set until harvest in 2011. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNAseq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNAseq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNAseq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. The mild water deficit treatment did not significantly alter the abundance of proteins or metabolites measured in the five cultivars, but did have a small effect on gene expression. The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species.
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The application of 'omics tools to biologically based monitoring and surveillance of aquatic environments shows considerable promise for complementing chemical monitoring in ecological risk assessments. However, few of the current approaches offer the ability to sample ecological...
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Big data: the next frontier for innovation in therapeutics and healthcare.
Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan
2014-05-01
Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.
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Yamamoto, Naoki; Suzuki, Tomohiro; Kobayashi, Masaaki; Dohra, Hideo; Sasaki, Yohei; Hirai, Hirofumi; Yokoyama, Koji; Kawagishi, Hirokazu; Yano, Kentaro
2014-12-03
The angel's wing oyster mushroom (Pleurocybella porrigens, Sugihiratake) is a well-known delicacy. However, its potential risk in acute encephalopathy was recently revealed by a food poisoning incident. To disclose the genes underlying the accident and provide mechanistic insight, we seek to develop an information infrastructure containing omics data. In our previous work, we sequenced the genome and transcriptome using next-generation sequencing techniques. The next step in achieving our goal is to develop a web database to facilitate the efficient mining of large-scale omics data and identification of genes specifically expressed in the mushroom. This paper introduces a web database A-WINGS (http://bioinf.mind.meiji.ac.jp/a-wings/) that provides integrated genomic and transcriptomic information for the angel's wing oyster mushroom. The database contains structure and functional annotations of transcripts and gene expressions. Functional annotations contain information on homologous sequences from NCBI nr and UniProt, Gene Ontology, and KEGG Orthology. Digital gene expression profiles were derived from RNA sequencing (RNA-seq) analysis in the fruiting bodies and mycelia. The omics information stored in the database is freely accessible through interactive and graphical interfaces by search functions that include 'GO TREE VIEW' browsing, keyword searches, and BLAST searches. The A-WINGS database will accelerate omics studies on specific aspects of the angel's wing oyster mushroom and the family Tricholomataceae.
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Omics databases on kidney disease: where they can be found and how to benefit from them.
Papadopoulos, Theofilos; Krochmal, Magdalena; Cisek, Katryna; Fernandes, Marco; Husi, Holger; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost P; Klein, Julie
2016-06-01
In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.
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Yeoman, Carl J.; Thomas, Susan M.; Miller, Margret E. Berg; Ulanov, Alexander V.; Torralba, Manolito; Lucas, Sarah; Gillis, Marcus; Cregger, Melissa; Gomez, Andres; Ho, Mengfei; Leigh, Steven R.; Stumpf, Rebecca; Creedon, Douglas J.; Smith, Michael A.; Weisbaum, Jon S.; Nelson, Karen E.; Wilson, Brenda A.; White, Bryan A.
2013-01-01
Background Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms. Principal Findings 16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp. Conclusions The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV. PMID:23405259
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Yeoman, Carl J; Thomas, Susan M; Miller, Margret E Berg; Ulanov, Alexander V; Torralba, Manolito; Lucas, Sarah; Gillis, Marcus; Cregger, Melissa; Gomez, Andres; Ho, Mengfei; Leigh, Steven R; Stumpf, Rebecca; Creedon, Douglas J; Smith, Michael A; Weisbaum, Jon S; Nelson, Karen E; Wilson, Brenda A; White, Bryan A
2013-01-01
Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms. 16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp. The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV.
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Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.
Kebschull, Moritz; Papapanou, Panos N
2017-01-01
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.
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Protein Interactome of Muscle Invasive Bladder Cancer
Bhat, Akshay; Heinzel, Andreas; Mayer, Bernd; Perco, Paul; Mühlberger, Irmgard; Husi, Holger; Merseburger, Axel S.; Zoidakis, Jerome; Vlahou, Antonia; Schanstra, Joost P.; Mischak, Harald; Jankowski, Vera
2015-01-01
Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder. PMID:25569276
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Clinical protein science developments for patient monitoring in hospital central laboratories.
Malm, Johan; Marko-Varga, György
2016-12-01
Patient care relies heavily on standardized tests performed in hospital laboratories, typically including clinical chemistry, pathology and microbiology. With the introduction of personalized medicine tremendous efforts have been made to identify new biomarkers of disease with various omics technologies, often including mass spectrometry. In order to validate new biomarkers and perform clinical studies high quality biobank samples are of key importance. In this editorial different aspects of mass spectrometry in future personalized medicine are discussed.
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Xia, Pu; Zhang, Xiaowei; Zhang, Hanxin; Wang, Pingping; Tian, Mingming; Yu, Hongxia
2017-08-15
One of the major challenges in environmental science is monitoring and assessing the risk of complex environmental mixtures. In vitro bioassays with limited key toxicological end points have been shown to be suitable to evaluate mixtures of organic pollutants in wastewater and recycled water. Omics approaches such as transcriptomics can monitor biological effects at the genome scale. However, few studies have applied omics approach in the assessment of mixtures of organic micropollutants. Here, an omics approach was developed for profiling bioactivity of 10 water samples ranging from wastewater to drinking water in human cells by a reduced human transcriptome (RHT) approach and dose-response modeling. Transcriptional expression of 1200 selected genes were measured by an Ampliseq technology in two cell lines, HepG2 and MCF7, that were exposed to eight serial dilutions of each sample. Concentration-effect models were used to identify differentially expressed genes (DEGs) and to calculate effect concentrations (ECs) of DEGs, which could be ranked to investigate low dose response. Furthermore, molecular pathways disrupted by different samples were evaluated by Gene Ontology (GO) enrichment analysis. The ability of RHT for representing bioactivity utilizing both HepG2 and MCF7 was shown to be comparable to the results of previous in vitro bioassays. Finally, the relative potencies of the mixtures indicated by RHT analysis were consistent with the chemical profiles of the samples. RHT analysis with human cells provides an efficient and cost-effective approach to benchmarking mixture of micropollutants and may offer novel insight into the assessment of mixture toxicity in water.
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Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health
Vernocchi, Pamela; Del Chierico, Federica; Putignani, Lorenza
2016-01-01
The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies. PMID:27507964
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Marzano, Valeria; Mancinelli, Livia; Bracaglia, Giorgia; Del Chierico, Federica; Vernocchi, Pamela; Di Girolamo, Francesco; Garrone, Stefano; Tchidjou Kuekou, Hyppolite; D'Argenio, Patrizia; Dallapiccola, Bruno; Urbani, Andrea; Putignani, Lorenza
2017-11-01
The human gut has been continuously exposed to a broad spectrum of intestinal organisms, including viruses, bacteria, fungi, and parasites (protozoa and worms), over millions of years of coevolution, and plays a central role in human health. The modern lifestyles of Western countries, such as the adoption of highly hygienic habits, the extensive use of antimicrobial drugs, and increasing globalisation, have dramatically altered the composition of the gut milieu, especially in terms of its eukaryotic "citizens." In the past few decades, numerous studies have highlighted the composition and role of human intestinal bacteria in physiological and pathological conditions, while few investigations exist on gut parasites and particularly on their coexistence and interaction with the intestinal microbiota. Studies of the gut "parasitome" through "omic" technologies, such as (meta)genomics, transcriptomics, proteomics, and metabolomics, are herein reviewed to better understand their role in the relationships between intestinal parasites, host, and resident prokaryotes, whether pathogens or commensals. Systems biology-based profiles of the gut "parasitome" under physiological and severe disease conditions can indeed contribute to the control of infectious diseases and offer a new perspective of omics-assisted tropical medicine.
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The microeconomics of personalized medicine: today's challenge and tomorrow's promise.
Davis, Jerel C; Furstenthal, Laura; Desai, Amar A; Norris, Troy; Sutaria, Saumya; Fleming, Edd; Ma, Philip
2009-04-01
'Personalized medicine' promises to increase the quality of clinical care and, in some cases, decrease health-care costs. Despite this, only a handful of diagnostic tests have made it to market, with mixed success. Historically, the challenges in this field were scientific. However, as discussed in this article, with the maturation of the '-omics' sciences, it now seems that the major barriers are increasingly related to economics. Overcoming the poor microeconomic alignment of incentives among key stakeholders is therefore crucial to catalysing the further development and adoption of personalized medicine, and we propose several actions that could help achieve this goal.
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Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George
2016-01-01
Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K-means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. PMID:27330233
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Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George
Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K -means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups.
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PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data.
Hernández-de-Diego, Rafael; Tarazona, Sonia; Martínez-Mira, Carlos; Balzano-Nogueira, Leandro; Furió-Tarí, Pedro; Pappas, Georgios J; Conesa, Ana
2018-05-25
The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.
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Integrated data analysis for genome-wide research.
Steinfath, Matthias; Repsilber, Dirk; Scholz, Matthias; Walther, Dirk; Selbig, Joachim
2007-01-01
Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.
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EU Framework 6 Project: Predictive Toxicology (PredTox)-overview and outcome
DOE Office of Scientific and Technical Information (OSTI.GOV)
Suter, Laura, E-mail: Laura.suter-dick@roche.com; Schroeder, Susanne; Meyer, Kirstin
2011-04-15
In this publication, we report the outcome of the integrated EU Framework 6 Project: Predictive Toxicology (PredTox), including methodological aspects and overall conclusions. Specific details including data analysis and interpretation are reported in separate articles in this issue. The project, partly funded by the EU, was carried out by a consortium of 15 pharmaceutical companies, 2 SMEs, and 3 universities. The effects of 16 test compounds were characterized using conventional toxicological parameters and 'omics' technologies. The three major observed toxicities, liver hypertrophy, bile duct necrosis and/or cholestasis, and kidney proximal tubular damage were analyzed in detail. The combined approach ofmore » 'omics' and conventional toxicology proved a useful tool for mechanistic investigations and the identification of putative biomarkers. In our hands and in combination with histopathological assessment, target organ transcriptomics was the most prolific approach for the generation of mechanistic hypotheses. Proteomics approaches were relatively time-consuming and required careful standardization. NMR-based metabolomics detected metabolite changes accompanying histopathological findings, providing limited additional mechanistic information. Conversely, targeted metabolite profiling with LC/GC-MS was very useful for the investigation of bile duct necrosis/cholestasis. In general, both proteomics and metabolomics were supportive of other findings. Thus, the outcome of this program indicates that 'omics' technologies can help toxicologists to make better informed decisions during exploratory toxicological studies. The data support that hypothesis on mode of action and discovery of putative biomarkers are tangible outcomes of integrated 'omics' analysis. Qualification of biomarkers remains challenging, in particular in terms of identification, mechanistic anchoring, appropriate specificity, and sensitivity.« less
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Culturomics: A New Kid on the Block of OMICS to Enable Personalized Medicine.
Kambouris, Manousos E; Pavlidis, Cristiana; Skoufas, Efthymios; Arabatzis, Michael; Kantzanou, Maria; Velegraki, Aristea; Patrinos, George P
2018-02-01
This innovation analysis highlights the underestimated and versatile potential of the new field of culturomics and examines its relation to other OMICS system sciences such as infectiomics, metabolomics, phenomics, and pharmacomicrobiomics. The advent of molecular biology, followed by the emergence of various disciplines of the genomics, and most importantly metagenomics, brought about the sharp decline of conventional microbiology methods. Emergence of culturomics has a natural synergy with therapeutic and clinical genomic approaches so as to realize personalized medicine. Notably, the concept of culturomics expands on that of phenomics and allows a reintroduction of the culture-based phenotypic characterization into the 21st century research repertoire, bolstered by robust technology for automated and massive execution, but its potential is largely unappreciated at present; the few available references show unenthusiastic pursuit and in narrow applications. This has not to be so: depending on the specific brand of culturomics, the scope of applications may extend to medicine, agriculture, environmental sciences, pharmacomicrobiomics, and biotechnology innovation. Moreover, culturomics may produce Big Data. This calls for a new generation of data scientists and innovative ways of harnessing and valorizing Big Data beyond classical genomics. Much more detailed and objective classification and identification of microbiota may soon be at hand through culturomics, thus enabling precision diagnosis toward truly personalized medicine. Culturomics may both widen the scope of microbiology and improve its contributions to diagnostics and personalized medicine, characterizing microbes and determining their associations with health and disease dynamics.
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Kraniotou, Christina; Karadima, Vasiliki; Bellos, George; Tsangaris, George Th
2018-03-05
The global incidence of metabolic disorders like type 2 diabetes mellitus (DM2) has assumed epidemic proportions, leading to adverse health and socio-economic impacts. It is therefore of critical importance the early diagnosis of DM2 patients and the detection of those at increased risk of disease. In this respect, Precision Medicine (PM) is an emerging approach that includes practices, tests, decisions and treatments adapted to the characteristics of each patient. With regard to DM2, PM manages a wealth of "omics" data (genomic, metabolic, proteomic, environmental, clinical and paraclinical) to increase the number of clinically validated biomarkers in order to identify patients in early stage even before the prediabetic phase. In this paper, we discuss the epidemic dimension of metabolic disorders like type 2 diabetes mellitus (DM2) and the urgent demand for novel biomarkers to reduce the incidence or even delay the onset of DM2. Recent research data produced by "multi-omics" technologies (genomics/epigenomics, transcriptomics, proteomics and metabolomics), suggest that many potential biomarkers might be helpful in the prediction and early diagnosis of DM2. Predictive, Preventive and Personalized Medicine (PPPM) manages and integrates these data to apply personalized, preventive, and therapeutic approaches. This is significant because there is an emerging need for establishing channels for communication and personalized consultation between systems research and precision medicine, as the medicine of the future. Copyright © 2018 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Kujawinski, E. B.; Longnecker, K.; Alexander, H.; Dyhrman, S.; Jenkins, B. D.; Rynearson, T. A.
2016-02-01
Phytoplankton blooms in coastal areas contribute a large fraction of primary production to the global oceans. Despite their central importance, there are fundamental unknowns in phytoplankton community metabolism, which limit the development of a more complete understanding of the carbon cycle. Within this complex setting, the tools of systems biology hold immense potential for profiling community metabolism and exploring links to the carbon cycle, but have rarely been applied together in this context. Here we focus on phytoplankton community samples collected from a model coastal system over a three-week period. At each sampling point, we combined two assessments of metabolic function: the meta-transcriptome, or the genes that are expressed by all organisms at each sampling point, and the metabolome, or the intracellular molecules produced during the community's metabolism. These datasets are inherently complementary, with gene expression likely to vary in concert with the concentrations of metabolic intermediates. Indeed, preliminary data show coherence in transcripts and metabolites associated with nutrient stress response and with fixed carbon oxidation. To date, these datasets are rarely integrated across their full complexity but together they provide unequivocal evidence of specific metabolic pathways by individual phytoplankton taxa, allowing a more comprehensive systems view of this dynamic environment. Future application of multi-omic profiling will facilitate a more complete understanding of metabolic reactions at the foundation of the carbon cycle.
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Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography.
Navarro-Reig, Meritxell; Bedia, Carmen; Tauler, Romà; Jaumot, Joaquim
2018-04-03
The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Study of cnidarian-algal symbiosis in the "omics" age.
Meyer, Eli; Weis, Virginia M
2012-08-01
The symbiotic associations between cnidarians and dinoflagellate algae (Symbiodinium) support productive and diverse ecosystems in coral reefs. Many aspects of this association, including the mechanistic basis of host-symbiont recognition and metabolic interaction, remain poorly understood. The first completed genome sequence for a symbiotic anthozoan is now available (the coral Acropora digitifera), and extensive expressed sequence tag resources are available for a variety of other symbiotic corals and anemones. These resources make it possible to profile gene expression, protein abundance, and protein localization associated with the symbiotic state. Here we review the history of "omics" studies of cnidarian-algal symbiosis and the current availability of sequence resources for corals and anemones, identifying genes putatively involved in symbiosis across 10 anthozoan species. The public availability of candidate symbiosis-associated genes leaves the field of cnidarian-algal symbiosis poised for in-depth comparative studies of sequence diversity and gene expression and for targeted functional studies of genes associated with symbiosis. Reviewing the progress to date suggests directions for future investigations of cnidarian-algal symbiosis that include (i) sequencing of Symbiodinium, (ii) proteomic analysis of the symbiosome membrane complex, (iii) glycomic analysis of Symbiodinium cell surfaces, and (iv) expression profiling of the gastrodermal cells hosting Symbiodinium.
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Integrative Analysis of Omics Big Data.
Yu, Xiang-Tian; Zeng, Tao
2018-01-01
The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.
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Huo, Zhiguang; Tseng, George
2017-01-01
Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency. PMID:28959370
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Huo, Zhiguang; Tseng, George
2017-06-01
Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.
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Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance
Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P
2013-01-01
The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103
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Information Commons for Rice (IC4R)
2016-01-01
Rice is the most important staple food for a large part of the world's human population and also a key model organism for plant research. Here, we present Information Commons for Rice (IC4R; http://ic4r.org), a rice knowledgebase featuring adoption of an extensible and sustainable architecture that integrates multiple omics data through community-contributed modules. Each module is developed and maintained by different committed groups, deals with data collection, processing and visualization, and delivers data on-demand via web services. In the current version, IC4R incorporates a variety of rice data through multiple committed modules, including genome-wide expression profiles derived entirely from RNA-Seq data, resequencing-based genomic variations obtained from re-sequencing data of thousands of rice varieties, plant homologous genes covering multiple diverse plant species, post-translational modifications, rice-related literatures and gene annotations contributed by the rice research community. Unlike extant related databases, IC4R is designed for scalability and sustainability and thus also features collaborative integration of rice data and low costs for database update and maintenance. Future directions of IC4R include incorporation of other omics data and association of multiple omics data with agronomically important traits, dedicating to build IC4R into a valuable knowledgebase for both basic and translational researches in rice. PMID:26519466
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Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M; Young, Vincent B; Jansson, Janet K; Fredricks, David N; Borenstein, Elhanan
2016-01-01
Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.
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Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.
2016-01-01
ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCE Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism. PMID:27239563
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An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.
Xu, Cheng; Liu, Jiamei; Yang, Weifeng; Shu, Yayun; Wei, Zhipeng; Zheng, Weiwei; Feng, Xin; Zhou, Fengfeng
2018-04-01
Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.
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[Introduction of translational research in omics science to clinical anesthesia].
Sugino, Shigekazu; Hayase, Tomo; Yamakage, Michiaki
2013-03-01
Much progress has been made in omics research following completion of the Human Genome Project. This comprehensive analysis produced a new discipline (i.e., bioinformatics), and its findings contributed to the clinical practice of anesthesiology. Genomes of patients show genetic variations and may predict the sensitivity to anesthetics and analgesics, incidence of adverse effects, and intensity of postsurgical pain. Changes in the transcriptomes of patients may also reflect anesthesia-related expression profiles of various types of neurons in the brain, and information on such changes may contribute to molecular targeted therapy in anesthetized patients. In addition, novel epigenome research may explain why environments change the phenotypes of clinical anesthesia. We currently hypothesize that female gender is associated with DNA methylation in pain-related and vomiting-related gene promoter regions at the genome-wide level and that epigenetic mechanisms are involved in gender differences in anesthesia practice.
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Palazzotto, Emilia; Weber, Tilmann
2018-04-12
Natural products produced by microorganisms represent the main source of bioactive molecules. The development of high-throughput (omics) techniques have importantly contributed to the renaissance of new antibiotic discovery increasing our understanding of complex mechanisms controlling the expression of biosynthetic gene clusters (BGCs) encoding secondary metabolites. In this context this review highlights recent progress in the use and integration of 'omics' approaches with focuses on genomics, transcriptomics, proteomics metabolomics meta-omics and combined omics as powerful strategy to discover new antibiotics. Copyright © 2018 Elsevier Ltd. All rights reserved.
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LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
Vasaikar, Suhas V; Straub, Peter; Wang, Jing; Zhang, Bing
2018-01-04
The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Taylor, Jacquelyn Y; Barcelona de Mendoza, Veronica
2018-01-01
The purpose of this article is to provide an overview of the role of nurse scientists in -omics-based research and to promote discussion around the conduct of -omics-based nursing research in minority communities. Nurses are advocates, educators, practitioners, scientists, and researchers, and are crucial to the design and successful implementation of -omics studies, particularly including minority communities. The contribution of nursing in this area of research is crucial to reducing health disparities. In this article, challenges in the conduct of -omics-based research in minority communities are discussed, and recommendations for improving diversity among nurse scientists, study participants, and utilization of training and continuing education programs in -omics are provided. Many opportunities exist for nurses to increase their knowledge in -omics and to continue to build the ranks of nurse scientists as leaders in -omics-based research. In order to work successfully with communities of color, nurse scientists must advocate for participation in the Precision Medicine Initiative, improve representation of nurse faculty of color, and increase utilization of training programs in -omics and lead such initiatives. All nursing care has the potential to be affected by the era of -omics and precision health. By taking an inclusive approach to diversity in nursing and -omics research, nurses will be well placed to be leaders in reducing health disparities through research, practice, and education. © 2017 Sigma Theta Tau International.
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Karathanasis, Nestoras; Tsamardinos, Ioannis
2016-01-01
Background The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes. Results We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability. Conclusions The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data. PMID:27812137
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Owolabi, Mayowa; Peprah, Emmanuel; Xu, Huichun; Akinyemi, Rufus; Tiwari, Hemant K; Irvin, Marguerite R; Wahab, Kolawole Wasiu; Arnett, Donna K; Ovbiagele, Bruce
2017-11-15
We systematically reviewed the genetic variants associated with stroke in genome-wide association studies (GWAS) and examined the emerging priorities and opportunities for rapidly advancing stroke research in the era of Trans-Omics science. Using the PRISMA guideline, we searched PubMed and NHGRI- EBI GWAS catalog for stroke studies from 2007 till May 2017. We included 31 studies. The major challenge is that the few validated variants could not account for the full genetic risk of stroke and have not been translated for clinical use. None of the studies included continental Africans. Genomic study of stroke among Africans presents a unique opportunity for the discovery, validation, functional annotation, Trans-Omics study and translation of genomic determinants of stroke with implications for global populations. This is because all humans originated from Africa, a continent with a unique genomic architecture and a distinctive epidemiology of stroke; as well as substantially higher heritability and resolution of fine mapping of stroke genes. Understanding the genomic determinants of stroke and the corresponding molecular mechanisms will revolutionize the development of a new set of precise biomarkers for stroke prediction, diagnosis and prognostic estimates as well as personalized interventions for reducing the global burden of stroke. Copyright © 2017 Elsevier B.V. All rights reserved.
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Jiang, Xiaoyu; Fuchs, Mathias
2017-01-01
As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility. PMID:28546826
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A practical data processing workflow for multi-OMICS projects.
Kohl, Michael; Megger, Dominik A; Trippler, Martin; Meckel, Hagen; Ahrens, Maike; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-Claudius; Baba, Hideo A; Sitek, Barbara; Schlaak, Jörg F; Meyer, Helmut E; Stephan, Christian; Eisenacher, Martin
2014-01-01
Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. Copyright © 2013 Elsevier B.V. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Burnum-Johnson, Kristin E.; Kyle, Jennifer E.; Eisfeld, Amie J.
The continued emergence and spread of infectious agents is of increasing concern due to increased population growth and the associated increased livestock production to meet food demands, increased urbanization and land-use changes, and greater travel. A systems biology approach to infectious disease research can significantly advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can only take place subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivationmore » and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. Partial inactivation was observed for pathogens without exposed lipid membranes including 99.99% inactivation of community-associated methicillin-resistant Staphylococcus aureus, 99.6% and >99% inactivation of Clostridium difficile spores and vegetative cells, respectively, and 50% inactivation of adenovirus type 5. To demonstrate that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses, we highlight select proteomics, metabolomics and lipidomics data from human epithelial lung cells infected with wild-type and mutant forms of influenza H7N9. We believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen.« less
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Beale, D J; Crosswell, J; Karpe, A V; Ahmed, W; Williams, M; Morrison, P D; Metcalfe, S; Staley, C; Sadowsky, M J; Palombo, E A; Steven, A D L
2017-12-31
The impact of anthropogenic factors arising from point and non-point pollution sources at a multi commodity marine port and its surrounding ecosystems were studied using sediment samples collected from a number of onshore (Gladstone Harbour and Facing Island) and offshore (Heron Island and Fitzroy Reefs) sites in Australia's Central Queensland. Sediment samples were analyzed for trace metals, organic carbon, polycyclic aromatic hydrocarbons (PAH), emerging chemicals of concern (ECC) and sterols. Similarly, the biological and biochemical interaction between the reef and its environment was analyzed by the multi-omic tools of next-generation sequencing characterization of the bacterial community and microbial community metabolic profiling. Overall, the trace elements were observed at the lower end of the Australian environmental guideline values at the offshore sites, while higher values were observed for the onshore locations Nickel and copper were observed above the high trigger value threshold at the onshore sites. The levels of PAH were below limits of detection across all sites. However, some of the ECC and sterols were observed at higher concentrations at both onshore and offshore locations, notably, the cholesterol family sterols and 17α-ethynylestradiol. Multi-omic analyses also indicated possible thermal and photo irradiation stressors on the bacterial communities at all the tested sites. The observed populations of γ-proteobacteria were found in combination with an increased pool of fatty acids that indicate fatty acid synthesis and utilisation of the intermediates of the shikimate pathways. This study demonstrates the value of applying a multi-omics approach for ecological assessments, in which a more detailed assessment of physical and chemical contaminants and their impact on the community bacterial biome is obtained. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.
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Integrative Exploratory Analysis of Two or More Genomic Datasets.
Meng, Chen; Culhane, Aedin
2016-01-01
Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.
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Burnum-Johnson, Kristin E; Kyle, Jennifer E; Eisfeld, Amie J; Casey, Cameron P; Stratton, Kelly G; Gonzalez, Juan F; Habyarimana, Fabien; Negretti, Nicholas M; Sims, Amy C; Chauhan, Sadhana; Thackray, Larissa B; Halfmann, Peter J; Walters, Kevin B; Kim, Young-Mo; Zink, Erika M; Nicora, Carrie D; Weitz, Karl K; Webb-Robertson, Bobbie-Jo M; Nakayasu, Ernesto S; Ahmer, Brian; Konkel, Michael E; Motin, Vladimir; Baric, Ralph S; Diamond, Michael S; Kawaoka, Yoshihiro; Waters, Katrina M; Smith, Richard D; Metz, Thomas O
2017-01-26
The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.
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Burnum-Johnson, Kristin E.; Kyle, Jennifer E.; Eisfeld, Amie J.; Casey, Cameron P.; Stratton, Kelly G.; Gonzalez, Juan F.; Habyarimana, Fabien; Negretti, Nicholas M.; Sims, Amy C.; Chauhan, Sadhana; Thackray, Larissa B.; Halfmann, Peter J.; Walters, Kevin B.; Kim, Young-Mo; Zink, Erika M.; Nicora, Carrie D.; Weitz, Karl K.; Webb-Robertson, Bobbie-Jo M.; Nakayasu, Ernesto S.; Ahmer, Brian; Konkel, Michael E.; Motin, Vladimir; Baric, Ralph S.; Diamond, Michael S.; Kawaoka, Yoshihiro; Waters, Katrina M.; Smith, Richard D.; Metz, Thomas O.
2017-01-01
The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes. PMID:28091625
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Saw, Woei-Yuh; Tantoso, Erwin; Begum, Husna; Zhou, Lihan; Zou, Ruiyang; He, Cheng; Chan, Sze Ling; Tan, Linda Wei-Lin; Wong, Lai-Ping; Xu, Wenting; Moong, Don Kyin Nwe; Lim, Yenly; Li, Bowen; Pillai, Nisha Esakimuthu; Peterson, Trevor A; Bielawny, Tomasz; Meikle, Peter J; Mundra, Piyushkumar A; Lim, Wei-Yen; Luo, Ma; Chia, Kee-Seng; Ong, Rick Twee-Hee; Brunham, Liam R; Khor, Chiea-Chuen; Too, Heng Phon; Soong, Richie; Wenk, Markus R; Little, Peter; Teo, Yik-Ying
2017-09-21
The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.The Singapore Genome Variation projects characterized the genetics of Singapore's Chinese, Malay, and Indian populations. The Singapore Integrative Omics Study introduced here goes further in providing multi-omic measurements in individuals from these populations, including genetic, transcriptome, lipidome, and lifestyle data, and will facilitate the study of common diseases in Asian communities.
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Personalized Medicine: What's in it for Rare Diseases?
Schee Genannt Halfmann, Sebastian; Mählmann, Laura; Leyens, Lada; Reumann, Matthias; Brand, Angela
2017-01-01
Personalised Medicine has become a reality over the last years. The emergence of 'omics' and big data has started revolutionizing healthcare. New 'omics' technologies lead to a better molecular characterization of diseases and a new understanding of the complexity of diseases. The approach of PM is already successfully applied in different healthcare areas such as oncology, cardiology, nutrition and for rare diseases. However, health systems across the EU are often still promoting the 'one-size fits all' approach, even if it is known that patients do greatly vary in their molecular characteristics and response to drugs and other interventions. To make use of the full potentials of PM in the next years ahead several challenges need to be addressed such as the integration of big data, patient empowerment, translation of basic to clinical research, bringing the innovation to the market and shaping sustainable healthcare systems.
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Caie, Peter D; Harrison, David J
2016-01-01
The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.
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Data mining in newt-omics, the repository for omics data from the newt.
Looso, Mario; Braun, Thomas
2015-01-01
Salamanders are an excellent model organism to study regenerative processes due to their unique ability to regenerate lost appendages or organs. Straightforward bioinformatics tools to analyze and take advantage of the growing number of "omics" studies performed in salamanders were lacking so far. To overcome this limitation, we have generated a comprehensive data repository for the red-spotted newt Notophthalmus viridescens, named newt-omics, merging omics style datasets on the transcriptome and proteome level including expression values and annotations. The resource is freely available via a user-friendly Web-based graphical user interface ( http://newt-omics.mpi-bn.mpg.de) that allows access and queries to the database without prior bioinformatical expertise. The repository is updated regularly, incorporating new published datasets from omics technologies.
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Allegri, Massimo; De Gregori, Manuela; Minella, Cristina E; Klersy, Catherine; Wang, Wei; Sim, Moira; Gieger, Christian; Manz, Judith; Pemberton, Iain K; MacDougall, Jane; Williams, Frances Mk; Van Zundert, Jan; Buyse, Klaas; Lauc, Gordan; Gudelj, Ivan; Primorac, Dragan; Skelin, Andrea; Aulchenko, Yurii S; Karssen, Lennart C; Kapural, Leonardo; Rauck, Richard; Fanelli, Guido
2016-10-19
Chronic low back pain (CLBP) produces considerable direct costs as well as indirect burdens for society, industry and health systems. CLBP is characterised by heterogeneity, inclusion of several pain syndromes, different underlying molecular pathologies and interaction with psychosocial factors that leads to a range of clinical manifestations. There is still much to understand in the underlying pathological processes and the non-psychosocial factors which account for differences in outcomes. Biomarkers that may be objectively used for diagnosis and personalised, targeted and cost-effective treatment are still lacking. Therefore, any data that may be obtained at the '-omics' level (glycomics, Activomics and genome-wide association studies-GWAS) may be helpful to use as dynamic biomarkers for elucidating CLBP pathogenesis and may ultimately provide prognostic information too. By means of a retrospective, observational, case-cohort, multicentre study, we aim to investigate new promising biomarkers potentially able to solve some of the issues related to CLBP. The study follows a two-phase, 1:2 case-control model. A total of 12 000 individuals (4000 cases and 8000 controls) will be enrolled; clinical data will be registered, with particular attention to pain characteristics and outcomes of pain treatments. Blood samples will be collected to perform -omics studies. The primary objective is to recognise genetic variants associated with CLBP; secondary objectives are to study glycomics and Activomics profiles associated with CLBP. The study is part of the PainOMICS project funded by European Community in the Seventh Framework Programme. The study has been approved from competent ethical bodies and copies of approvals were provided to the European Commission before starting the study. Results of the study will be reviewed by the Scientific Board and Ethical Committee of the PainOMICS Consortium. The scientific results will be disseminated through peer-reviewed journals. NCT02037789; Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
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Pirih, Nina; Kunej, Tanja
2018-05-01
The volume of publications and the type of research approaches used in omics system sciences are vast and continue to expand rapidly. This increased complexity and heterogeneity of omics data are challenging data extraction, sensemaking, analyses, knowledge translation, and interpretation. An extended and dynamic taxonomy for the classification and summary of omics studies are essential. We present an updated taxonomy for classification of omics research studies based on four criteria: (1) type and number of genomic loci in a research study, (2) number of species and biological samples, (3) the type of omics technology (e.g., genomics, transcriptomics, and proteomics) and omics technology application type (e.g., pharmacogenomics and nutrigenomics), and (4) phenotypes. In addition, we present a graphical summary approach that enables the researchers to define the main characteristics of their study in a single figure, and offers the readers to rapidly grasp the published study and omics data. We searched the PubMed and the Web of Science from 09/2002 to 02/2018, including research and review articles, and identified 90 scientific publications. We propose a call toward omics studies' standardization for reporting in scientific literature. We anticipate the proposed classification scheme will usefully contribute to improved classification of published reports in genomics and other omics fields, and help data extraction from publications for future multiomics data integration.
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-Omic and Electronic Health Record Big Data Analytics for Precision Medicine.
Wu, Po-Yen; Cheng, Chih-Wen; Kaddi, Chanchala D; Venugopalan, Janani; Hoffman, Ryan; Wang, May D
2017-02-01
Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine. Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
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[Progress in omics research of Aspergillus niger].
Sui, Yufei; Ouyang, Liming; Lu, Hongzhong; Zhuang, Yingping; Zhang, Siliang
2016-08-25
Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.
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Omics strategies for revealing Yersinia pestis virulence
Yang, Ruifu; Du, Zongmin; Han, Yanping; Zhou, Lei; Song, Yajun; Zhou, Dongsheng; Cui, Yujun
2012-01-01
Omics has remarkably changed the way we investigate and understand life. Omics differs from traditional hypothesis-driven research because it is a discovery-driven approach. Mass datasets produced from omics-based studies require experts from different fields to reveal the salient features behind these data. In this review, we summarize omics-driven studies to reveal the virulence features of Yersinia pestis through genomics, trascriptomics, proteomics, interactomics, etc. These studies serve as foundations for further hypothesis-driven research and help us gain insight into Y. pestis pathogenesis. PMID:23248778
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-Omic and Electronic Health Records Big Data Analytics for Precision Medicine
Wu, Po-Yen; Cheng, Chih-Wen; Kaddi, Chanchala D.; Venugopalan, Janani; Hoffman, Ryan; Wang, May D.
2017-01-01
Objective Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. Methods In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. Results To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Conclusion Big data analytics is able to address –omic and EHR data challenges for paradigm shift towards precision medicine. Significance Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact. PMID:27740470
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Balliu, Brunilda; Tsonaka, Roula; Boehringer, Stefan; Houwing-Duistermaat, Jeanine
2015-03-01
Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data. © 2015 Wiley Periodicals, Inc.
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Rager, Julia E; Auerbach, Scott S; Chappell, Grace A; Martin, Elizabeth; Thompson, Chad M; Fry, Rebecca C
2017-10-16
Prenatal inorganic arsenic (iAs) exposure influences the expression of critical genes and proteins associated with adverse outcomes in newborns, in part through epigenetic mediators. The doses at which these genomic and epigenomic changes occur have yet to be evaluated in the context of dose-response modeling. The goal of the present study was to estimate iAs doses that correspond to changes in transcriptomic, proteomic, epigenomic, and integrated multi-omic signatures in human cord blood through benchmark dose (BMD) modeling. Genome-wide DNA methylation, microRNA expression, mRNA expression, and protein expression levels in cord blood were modeled against total urinary arsenic (U-tAs) levels from pregnant women exposed to varying levels of iAs. Dose-response relationships were modeled in BMDExpress, and BMDs representing 10% response levels were estimated. Overall, DNA methylation changes were estimated to occur at lower exposure concentrations in comparison to other molecular endpoints. Multi-omic module eigengenes were derived through weighted gene co-expression network analysis, representing co-modulated signatures across transcriptomic, proteomic, and epigenomic profiles. One module eigengene was associated with decreased gestational age occurring alongside increased iAs exposure. Genes/proteins within this module eigengene showed enrichment for organismal development, including potassium voltage-gated channel subfamily Q member 1 (KCNQ1), an imprinted gene showing differential methylation and expression in response to iAs. Modeling of this prioritized multi-omic module eigengene resulted in a BMD(BMDL) of 58(45) μg/L U-tAs, which was estimated to correspond to drinking water arsenic concentrations of 51(40) μg/L. Results are in line with epidemiological evidence supporting effects of prenatal iAs occurring at levels <100 μg As/L urine. Together, findings present a variety of BMD measures to estimate doses at which prenatal iAs exposure influences neonatal outcome-relevant transcriptomic, proteomic, and epigenomic profiles.
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Metabolomics: the apogee of the omic triology
Patti, Gary J; Yanes, Oscar; Siuzdak, Gary
2013-01-01
Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and shaping our understanding of cell biology, physiology, and medicine. PMID:22436749
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha
ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community.more » Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCEStudies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.« less
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Costa, Fabricio F
2014-04-01
The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.
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CircadiOmics: circadian omic web portal.
Ceglia, Nicholas; Liu, Yu; Chen, Siwei; Agostinelli, Forest; Eckel-Mahan, Kristin; Sassone-Corsi, Paolo; Baldi, Pierre
2018-06-15
Circadian rhythms play a fundamental role at all levels of biological organization. Understanding the mechanisms and implications of circadian oscillations continues to be the focus of intense research. However, there has been no comprehensive and integrated way for accessing and mining all circadian omic datasets. The latest release of CircadiOmics (http://circadiomics.ics.uci.edu) fills this gap for providing the most comprehensive web server for studying circadian data. The newly updated version contains high-throughput 227 omic datasets corresponding to over 74 million measurements sampled over 24 h cycles. Users can visualize and compare oscillatory trajectories across species, tissues and conditions. Periodicity statistics (e.g. period, amplitude, phase, P-value, q-value etc.) obtained from BIO_CYCLE and other methods are provided for all samples in the repository and can easily be downloaded in the form of publication-ready figures and tables. New features and substantial improvements in performance and data volume make CircadiOmics a powerful web portal for integrated analysis of circadian omic data.
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Advances in Omics and Bioinformatics Tools for Systems Analyses of Plant Functions
Mochida, Keiichi; Shinozaki, Kazuo
2011-01-01
Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances. PMID:22156726
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Omics analysis of mouse brain models of human diseases.
Paban, Véronique; Loriod, Béatrice; Villard, Claude; Buee, Luc; Blum, David; Pietropaolo, Susanna; Cho, Yoon H; Gory-Faure, Sylvie; Mansour, Elodie; Gharbi, Ali; Alescio-Lautier, Béatrice
2017-02-05
The identification of common gene/protein profiles related to brain alterations, if they exist, may indicate the convergence of the pathogenic mechanisms driving brain disorders. Six genetically engineered mouse lines modelling neurodegenerative diseases and neuropsychiatric disorders were considered. Omics approaches, including transcriptomic and proteomic methods, were used. The gene/protein lists were used for inter-disease comparisons and further functional and network investigations. When the inter-disease comparison was performed using the gene symbol identifiers, the number of genes/proteins involved in multiple diseases decreased rapidly. Thus, no genes/proteins were shared by all 6 mouse models. Only one gene/protein (Gfap) was shared among 4 disorders, providing strong evidence that a common molecular signature does not exist among brain diseases. The inter-disease comparison of functional processes showed the involvement of a few major biological processes indicating that brain diseases of diverse aetiologies might utilize common biological pathways in the nervous system, without necessarily involving similar molecules. Copyright © 2016 Elsevier B.V. All rights reserved.
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Multilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population
Wu, Yibo; Williams, Evan G.; Dubuis, Sébastien; Mottis, Adrienne; Jovaisaite, Virginija; Houten, Sander M.; Argmann, Carmen A.; Faridi, Pouya; Wolski, Witold; Kutalik, Zoltán; Zamboni, Nicola; Auwerx, Johan; Aebersold, Ruedi
2014-01-01
SUMMARY The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome—a subset of the metabolome—and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPRmt). UPRmt shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes. PMID:25215496
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An Integrative Bioinformatics Approach for Knowledge Discovery
NASA Astrophysics Data System (ADS)
Peña-Castillo, Lourdes; Phan, Sieu; Famili, Fazel
The vast amount of data being generated by large scale omics projects and the computational approaches developed to deal with this data have the potential to accelerate the advancement of our understanding of the molecular basis of genetic diseases. This better understanding may have profound clinical implications and transform the medical practice; for instance, therapeutic management could be prescribed based on the patient’s genetic profile instead of being based on aggregate data. Current efforts have established the feasibility and utility of integrating and analysing heterogeneous genomic data to identify molecular associations to pathogenesis. However, since these initiatives are data-centric, they either restrict the research community to specific data sets or to a certain application domain, or force researchers to develop their own analysis tools. To fully exploit the potential of omics technologies, robust computational approaches need to be developed and made available to the community. This research addresses such challenge and proposes an integrative approach to facilitate knowledge discovery from diverse datasets and contribute to the advancement of genomic medicine.
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Maga-Nteve, Christoniki; Vasilopoulou, Catherine G; Constantinou, Caterina; Margarity, Marigoula; Klapa, Maria I
2017-01-15
A systematic data quality validation and normalization strategy is an important component of the omic profile meta-analysis, ensuring comparability of the profiles and exclusion of experimental biases from the derived biological conclusions. In this study, we present the normalization methodology applied on the sets of cerebellum gas chromatography-mass spectrometry metabolic profiles of 124days old male and female animals in an adult-onset-hypothyroidism (AOH) mouse model before combining them into a sex-comparative analysis. The employed AOH model concerns the monitoring of the brain physiology of Balb/cJ mice after eight-week administration of 1%w/v KClO 4 in the drinking water, initiated on the 60th day of their life. While originating from the same animal study, the tissues of the two sexes were processed and their profiles acquired and analyzed at different time periods. Hence, the previously published profile set of male mice was first re-annotated based on the presently available resources. Then, after being validated as acquired under the same analytical conditions, both profiles sets were corrected for derivatization biases and filtered for low-confidence measurements based on the same criteria. The final normalized 73-metabolite profiles contribute to the currently few available omic datasets of the AOH effect on brain molecular physiology, especially with respect to sex differentiation. Multivariate statistical analysis indicated one (unknown) and three (succinate, benzoate, myristate) metabolites with significantly higher and lower, respectively, cerebellum concentration in the hypothyroid compared to the euthyroid female mice. The respective numbers for the males were two and 24. Comparison of the euthyroid cerebellum metabolic profiles between the two sexes indicated 36 metabolites, including glucose, myo- and scyllo-inositol, with significantly lower concentration in the females versus the males. This implies that the female mouse cerebellum has been conditioned to smaller changes in its metabolic activity with respect to the pathways involving these metabolites compared to the male animals. In conclusion, our study indicated a much subtler AOH effect on the cerebellum metabolic activity of the female compared to the male mice. The leaner metabolic profile of the female mouse cerebellum was suggested as a potential factor contributing to this phenomenon. Copyright © 2016 Elsevier B.V. All rights reserved.
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Framework for the quality assurance of 'omics technologies considering GLP requirements.
Kauffmann, Hans-Martin; Kamp, Hennicke; Fuchs, Regine; Chorley, Brian N; Deferme, Lize; Ebbels, Timothy; Hackermüller, Jörg; Perdichizzi, Stefania; Poole, Alan; Sauer, Ursula G; Tollefsen, Knut E; Tralau, Tewes; Yauk, Carole; van Ravenzwaay, Ben
2017-12-01
'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.
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Next generation sequencing and its applications in HPVassociated cancers
Tuna, Musaffe; Amos, Christopher I.
2017-01-01
Approximately 18% of all human cancers have a viral etiology, and human papillomavirus (HPV) has been identified as one of the most prevalent viruses that plays causative role in nearly all cervical cancers and, in addition, in subset of head and neck, anal, penile and vulvar cancers. The recent introduction of next generation sequencing (NGS) and other omics approaches have resulted in comprehensive knowledge on the pathogenesis of HPV-driven tumors. Specifically, these approaches have provided detailed information on genomic HPV integration sites, disrupted genes and pathways, and common and distinct genetic and epigenetic alterations in different human HPV-associated cancers. This review focuses on HPV integration sites, its concomitantly disrupted genes and pathways and its functional consequences in both cervical and head and neck cancers. Integration of NGS data with other omics and clinical data is crucial to better understand the pathophysiology of each individual malignancy and, based on this, to select targets and to design effective personalized treatment options. PMID:27784002
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Aguirre-Guillén, William Alejandro; Angeles-Floriano, Tania; López-Martínez, Briceida; Reyes-Morales, Hortensia; Zlotnik, Albert; Valle-Rios, Ricardo
Acute lymphoblastic leukemia (ALL) affects the quality of life of many children in the world and particularly in Mexico, where a high incidence has been reported. With a proper financial investment and with well-organized institutions caring for those patients, together with solid platforms to perform high-throughput analyses, we propose the creation of a Mexican repository system of serum and cells from bone marrow and blood samples derived from tissues of pediatric patients with ALL diagnosis. This resource, in combination with omics technologies, particularly proteomics and metabolomics, would allow longitudinal studies, offering an opportunity to design and apply personalized ALL treatments. Importantly, it would accelerate the development of translational science and will lead us to further discoveries, including the identification of new biomarkers for the early detection of leukemia. Copyright © 2017 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.
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Next generation sequencing and its applications in HPV-associated cancers.
Tuna, Musaffe; Amos, Christopher I
2017-01-31
Approximately 18% of all human cancers have a viral etiology, and human papillomavirus (HPV) has been identified as one of the most prevalent viruses that plays causative role in nearly all cervical cancers and, in addition, in subset of head and neck, anal, penile and vulvar cancers. The recent introduction of next generation sequencing (NGS) and other 'omics' approaches have resulted in comprehensive knowledge on the pathogenesis of HPV-driven tumors. Specifically, these approaches have provided detailed information on genomic HPV integration sites, disrupted genes and pathways, and common and distinct genetic and epigenetic alterations in different human HPV-associated cancers. This review focuses on HPV integration sites, its concomitantly disrupted genes and pathways and its functional consequences in both cervical and head and neck cancers. Integration of NGS data with other 'omics' and clinical data is crucial to better understand the pathophysiology of each individual malignancy and, based on this, to select targets and to design effective personalized treatment options.
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Li, Tongtong; Long, Meng; Li, Huan; Gatesoupe, François-Joël; Zhang, Xujie; Zhang, Qianqian; Feng, Dongyue; Li, Aihua
2017-01-01
Gut microbiota play key roles in host nutrition and metabolism. However, little is known about the relationship between host genetics, gut microbiota and metabolic profiles. Here, we used high-throughput sequencing and gas chromatography/mass spectrometry approaches to characterize the microbiota composition and the metabolite profiles in the gut of five cyprinid fish species with three different feeding habits raised under identical husbandry conditions. Our results showed that host species and feeding habits significantly affect not only gut microbiota composition but also metabolite profiles (ANOSIM, p ≤ 0.05). Mantel test demonstrated that host phylogeny, gut microbiota, and metabolite profiles were significantly related to each other (p ≤ 0.05). Additionally, the carps with the same feeding habits had more similarity in gut microbiota composition and metabolite profiles. Various metabolites were correlated positively with bacterial taxa involved in food degradation. Our results shed new light on the microbiome and metabolite profiles in the gut content of cyprinid fishes, and highlighted the correlations between host genotype, fish gut microbiome and putative functions, and gut metabolite profiles. PMID:28367147
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Uncovering Hidden Layers of Cell Cycle Regulation through Integrative Multi-omic Analysis
Aviner, Ranen; Shenoy, Anjana; Elroy-Stein, Orna; Geiger, Tamar
2015-01-01
Studying the complex relationship between transcription, translation and protein degradation is essential to our understanding of biological processes in health and disease. The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates. In this work, we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle through side-by-side analysis of mRNA, translation and protein levels. Our analysis sheds new light on the significant contribution of both protein synthesis and degradation to the variance in protein expression. Furthermore, we find that translation regulation plays an important role at S-phase, while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability. Specific molecular functions are found to be co-regulated and share similar patterns of mRNA, translation and protein expression along the cell cycle. Notably, these include genes and entire pathways not previously implicated in cell cycle progression, demonstrating the potential of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling. Through this three-level analysis, we characterize different mechanisms of gene expression, discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression. PMID:26439921
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Panczyk, Mariusz
2013-01-01
Nowadays nutrigenetics and nutrigenomics are perceived as one of the most important research areas ensuring better understanding of an impact of nutrition on human health. Since such researches are interdisciplinary in type, there is a problem with their widespread acceptance and practical clinical application of obtained results. Understanding the new ideas and hypotheses published in researches on nutrigenetics/nutrigenomics requires some knowledge of genetics, biochemistry, molecular biology, and capabilities and limitations that are associated with the use of statistical and bioinformatic analysis, and above all omics research technologies (genomics, transcriptomics, proteomics, metabolomics). Highly efficient genome and proteome analysis techniques allow to obtain data necessary for profiling of an individual patient. The main problem is still our insufficient knowledge of cell physiology and biochemistry. The vast amount of information is obtained with the use of omics technologies what makes it difficult to interpret and infer. An unquestionable advantage of this type of research is the possibility to utilize system analysis (system biology) which is important in the context of a holistic interpretation of biological phenomena. This review is an attempt to present the main hypotheses and objectives which are carried out by researchers in nutrigenetics/nutrigenomics. This article describes the most important directions of research and anticipated results that are related to the practical use of nutritional genomics as well as the critical assessment of the possible impact of future developments on public health.
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Omics studies of citrus, grape and rosaceae fruit trees
Shiratake, Katsuhiro; Suzuki, Mami
2016-01-01
Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted. PMID:27069397
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Omics studies of citrus, grape and rosaceae fruit trees.
Shiratake, Katsuhiro; Suzuki, Mami
2016-01-01
Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted.
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The rise of genomic profiling in ovarian cancer
Previs, Rebecca A.; Sood, Anil K.; Mills, Gordon B.; Westin, Shannon N.
2017-01-01
Introduction Next-generation sequencing and advances in ‘omics technology have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers. Areas covered Once characterized only by histologic appearance and clinical behavior, we now understand many of the molecular phenotypes that underlie the different ovarian cancer subtypes. While the current approach to treatment involves standard cytotoxic therapies after cytoreductive surgery for all ovarian cancers regardless of histologic or molecular characteristics, focus has shifted beyond a ‘one size fits all’ approach to ovarian cancer. Expert commentary Genomic profiling offers potentially ‘actionable’ opportunities for development of targeted therapies and a more individualized approach to treatment with concomitant improved outcomes and decreased toxicity. PMID:27828713
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From big data analysis to personalized medicine for all: challenges and opportunities.
Alyass, Akram; Turcotte, Michelle; Meyre, David
2015-06-27
Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.
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Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine.
Pecak, Matija; Korošec, Peter; Kunej, Tanja
2018-06-01
Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.
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Environmental OMICS: Current Status and Future Directions.
Objectives: Applications of OMICS to high throughput studies of changes of genes, RNAs, proteins and metabolites, and their associated functions in cells or organisms exposed to environmental chemicals has led to the emergence of a very active research field: environmental OMICS....
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From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms
Redenšek, Sara; Dolžan, Vita
2018-01-01
Abstract Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics–DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context. PMID:29356624
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Kolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R; Chen, Rui; Choiniere, John; Dearth, Stephen P; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W; Kohane, Isaac S; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; Macnealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H; Warnich, Louise; Wilhelm, Steven W; Yandl, Gregory
2014-01-01
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Kolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R; Chen, Rui; Choiniere, John; Dearth, Stephen P; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W; Kohane, Isaac S; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H; Warnich, Louise; Wilhelm, Steven W; Yandl, Gregory
2013-12-01
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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"Omics" of maize stress response for sustainable food production: opportunities and challenges.
Gong, Fangping; Yang, Le; Tai, Fuju; Hu, Xiuli; Wang, Wei
2014-12-01
Maize originated in the highlands of Mexico approximately 8700 years ago and is one of the most commonly grown cereal crops worldwide, followed by wheat and rice. Abiotic stresses (primarily drought, salinity, and high and low temperatures), together with biotic stresses (primarily fungi, viruses, and pests), negatively affect maize growth, development, and eventually production. To understand the response of maize to abiotic and biotic stresses and its mechanism of stress tolerance, high-throughput omics approaches have been used in maize stress studies. Integrated omics approaches are crucial for dissecting the temporal and spatial system-level changes that occur in maize under various stresses. In this comprehensive analysis, we review the primary types of stresses that threaten sustainable maize production; underscore the recent advances in maize stress omics, especially proteomics; and discuss the opportunities, challenges, and future directions of maize stress omics, with a view to sustainable food production. The knowledge gained from studying maize stress omics is instrumental for improving maize to cope with various stresses and to meet the food demands of the exponentially growing global population. Omics systems science offers actionable potential solutions for sustainable food production, and we present maize as a notable case study.
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Incorporating zebrafish omics into chemical biology and toxicology.
Sukardi, Hendrian; Ung, Choong Yong; Gong, Zhiyuan; Lam, Siew Hong
2010-03-01
In this communication, we describe the general aspects of omics approaches for analyses of transcriptome, proteome, and metabolome, and how they can be strategically incorporated into chemical screening and perturbation studies using the zebrafish system. Pharmacological efficacy and selectivity of chemicals can be evaluated based on chemical-induced phenotypic effects; however, phenotypic observation has limitations in identifying mechanistic action of chemicals. We suggest adapting gene-expression-based high-throughput screening as a complementary strategy to zebrafish-phenotype-based screening for mechanistic insights about the mode of action and toxicity of a chemical, large-scale predictive applications and comparative analysis of chemical-induced omics signatures, which are useful to identify conserved biological responses, signaling pathways, and biomarkers. The potential mechanistic, predictive, and comparative applications of omics approaches can be implemented in the zebrafish system. Examples of these using the omics approaches in zebrafish, including data of ours and others, are presented and discussed. Omics also facilitates the translatability of zebrafish studies across species through comparison of conserved chemical-induced responses. This review is intended to update interested readers with the current omics approaches that have been applied in chemical studies on zebrafish and their potential in enhancing discovery in chemical biology.
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Newt-omics: a comprehensive repository for omics data from the newt Notophthalmus viridescens
Bruckskotten, Marc; Looso, Mario; Reinhardt, Richard; Braun, Thomas; Borchardt, Thilo
2012-01-01
Notophthalmus viridescens, a member of the salamander family is an excellent model organism to study regenerative processes due to its unique ability to replace lost appendages and to repair internal organs. Molecular insights into regenerative events have been severely hampered by the lack of genomic, transcriptomic and proteomic data, as well as an appropriate database to store such novel information. Here, we describe ‘Newt-omics’ (http://newt-omics.mpi-bn.mpg.de), a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centred database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ∼50 000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13 810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise. PMID:22039101
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Schaumberg, Andrew
The Omics Tools package provides several small trivial tools for work in genomics. This single portable package, the omics.jar file, is a toolbox that works in any Java-based environment, including PCs, Macs, and supercomputers. The number of tools is expected to grow. One tool (called cmsearch.hadoop or cmsearch.local), calls the external cmsearch program to predict non-coding RNA in a genome. The cmsearch program is part of the third-party Infernal package. Omics Tools does not contain Infernal. Infernal may be installed separately. The cmsearch.hadoop subtool requires Apache Hadoop and runs on a supercomputer, though cmsearch.local does not and runs on amore » server. Omics Tools does not contain Hadoop. Hadoop mat be installed separartely The other tools (cmgbk, cmgff, fastats, pal, randgrp, randgrpr, randsub) do not interface with third-party tools. Omics Tools is written in Java and Scala programming languages. Invoking the help command shows currently available tools, as shown below: schaumbe@gpint06:~/proj/omics$ java -jar omics.jar help Known commands are: cmgbk : compare cmsearch and GenBank Infernal hits cmgff : compare hits among two GFF (version 3) files cmsearch.hadoop : find Infernal hits in a genome, on your supercomputer cmsearch.local : find Infernal hits in a genome, on your workstation fastats : FASTA stats, e.g. # bases, GC content pal : stem-loop motif detection by palindromic sequence search (code stub) randgrp : random subsample without replacement, of groups randgrpr : random subsample with replacement, of groups (fast) randsub : random subsample without replacement, of file lines For more help regarding a particular command, use: java -jar omics.jar command help Usage: java -jar omics.jar command args« less
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Challenges of metabolomics in human gut microbiota research.
Smirnov, Kirill S; Maier, Tanja V; Walker, Alesia; Heinzmann, Silke S; Forcisi, Sara; Martinez, Inés; Walter, Jens; Schmitt-Kopplin, Philippe
2016-08-01
The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine. Copyright © 2016 Elsevier GmbH. All rights reserved.
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Population Neuroscience: Dementia Epidemiology Serving Precision Medicine and Population Health.
Ganguli, Mary; Albanese, Emiliano; Seshadri, Sudha; Bennett, David A; Lyketsos, Constantine; Kukull, Walter A; Skoog, Ingmar; Hendrie, Hugh C
2018-01-01
Over recent decades, epidemiology has made significant contributions to our understanding of dementia, translating scientific discoveries into population health. Here, we propose reframing dementia epidemiology as "population neuroscience," blending techniques and models from contemporary neuroscience with those of epidemiology and biostatistics. On the basis of emerging evidence and newer paradigms and methods, population neuroscience will minimize the bias typical of traditional clinical research, identify the relatively homogenous subgroups that comprise the general population, and investigate broader and denser phenotypes of dementia and cognitive impairment. Long-term follow-up of sufficiently large study cohorts will allow the identification of cohort effects and critical windows of exposure. Molecular epidemiology and omics will allow us to unravel the key distinctions within and among subgroups and better understand individuals' risk profiles. Interventional epidemiology will allow us to identify the different subgroups that respond to different treatment/prevention strategies. These strategies will inform precision medicine. In addition, insights into interactions between disease biology, personal and environmental factors, and social determinants of health will allow us to measure and track disease in communities and improve population health. By placing neuroscience within a real-world context, population neuroscience can fulfill its potential to serve both precision medicine and population health.
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Pharmacogenetics of asthma: toward precision medicine.
Kersten, Elin T G; Koppelman, Gerard H
2017-01-01
Although currently available drugs to treat asthma are effective in most patients, a proportion of patients do not respond or experience side-effects; which is partly genetically determined. Pharmacogenetics is the study of how genetic variations influence drug response. In this review, we summarize prior results and recent studies in pharmacogenetics to determine if we can use genetic profiles for personalized treatment of asthma. The field of pharmacogenetics has moved from candidate gene studies in single populations toward genome-wide association studies and meta-analysis of multiple studies. New technologies have been used to enrich results, and an expanding number of genetic loci have been associated with therapeutic responses to asthma drugs. Prospective, genotype-stratified treatment studies have been conducted for β2-agonists, showing attenuated response in children carrying the Arg16 variant in the β2-adrenoreceptor gene. Although there has been much progress, many findings have not been replicated and currently known genetic loci only account for a fraction of variability in drug response. More research is necessary to translate into clinical practice. A polygenic predictive approach integrated in complex networks with other 'omics' technologies could aid to achieve this goal. Finally, to change clinical practice, studies that compare precision medicine with traditional medicine are needed.
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Rai, Vineeta; Karthikaichamy, Anbarasu; Das, Debasish; Noronha, Santosh; Wangikar, Pramod P; Srivastava, Sanjeeva
2016-07-01
Current momentum of microalgal research rests extensively in tapping the potential of multi-omics methodologies in regard to sustainable biofuels. Microalgal biomass is fermented to bioethanol; while lipids, particularly triacylglycerides (TAGs), are transesterified to biodiesels. Biodiesel has emerged as an ideal biofuel candidate; hence, its commercialization and use are increasingly being emphasized. Abiotic stresses exaggerate TAG accumulation, but the precise mechanisms are yet to be known. More recently, comprehensive multi-omics studies in microalgae have emerged from the biofuel perspective. Genomics and transcriptomics of microalgae have provided crucial leads and basic understanding toward lipid biosynthesis. Proteomics and metabolomics are now complementing "algal omics" and offer precise functional insights into the attendant static and dynamic physiological contexts. Indeed, the field has progressed from shotgun to targeted approaches. Notably, targeted proteomics studies in microalga are not yet reported. Several multi-omics tools and technologies that may be used to dig deeper into the microalgal physiology are examined and highlighted in this review. The article therefore aims to both introduce various available high-throughput biotechnologies and applications of "omics" in microalgae, and enlists a compendium of the emerging cutting edge literature. We suggest that a strategic and thoughtful combination of data streams from different omics platforms can provide a system-wide overview. The algal omics warrants closer attention in the future, with a view to technical, economic, and societal impacts that are anticipated in the current postgenomics era.
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There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will...
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Omics methods for probing the mode of action of natural phytotoxins
USDA-ARS?s Scientific Manuscript database
For a little over a decade, omics methods (transcriptomics, proteomics, metabolomics, and physionomics) have been used to discover and probe the mode of action of both synthetic and natural phytotoxins. For mode of action discovery, the strategy for each of these approaches is to generate an omics...
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Knowledge and information generated using new tools/methods collectively called "Omics" technologies could have a profound effect on qualitative and quantitative characterizations of human health risk assessments.
The suffix "Omics" is a descriptor used for a series of e... -
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kolker, Eugene; Ozdemir, Vural; Martens , Lennart
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies omics studies are becoming increasingly prevalent yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research,. These three essential steps require consistent generation, capture, and distribution of the metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologiesmore » and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. This omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.« less
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Tebani, Abdellah; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya
2016-01-01
The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era. PMID:27649151
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Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R.; Chen, Rui; Choiniere, John; Dearth, Stephen P.; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H.; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W.; Kohane, Isaac S.; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V.; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H.; Warnich, Louise; Wilhelm, Steven W.; Yandl, Gregory
2014-01-01
Abstract Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement. PMID:24456465
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Tebani, Abdellah; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya
2016-09-14
The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.
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Comparative Transcriptomes and EVO-DEVO Studies Depending on Next Generation Sequencing.
Liu, Tiancheng; Yu, Lin; Liu, Lei; Li, Hong; Li, Yixue
2015-01-01
High throughput technology has prompted the progressive omics studies, including genomics and transcriptomics. We have reviewed the improvement of comparative omic studies, which are attributed to the high throughput measurement of next generation sequencing technology. Comparative genomics have been successfully applied to evolution analysis while comparative transcriptomics are adopted in comparison of expression profile from two subjects by differential expression or differential coexpression, which enables their application in evolutionary developmental biology (EVO-DEVO) studies. EVO-DEVO studies focus on the evolutionary pressure affecting the morphogenesis of development and previous works have been conducted to illustrate the most conserved stages during embryonic development. Old measurements of these studies are based on the morphological similarity from macro view and new technology enables the micro detection of similarity in molecular mechanism. Evolutionary model of embryo development, which includes the "funnel-like" model and the "hourglass" model, has been evaluated by combination of these new comparative transcriptomic methods with prior comparative genomic information. Although the technology has promoted the EVO-DEVO studies into a new era, technological and material limitation still exist and further investigations require more subtle study design and procedure.
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Multi-omic profiling to assess the effect of iron starvation in Streptococcus pneumoniae TIGR4
Jiménez-Munguía, Irene; Calderón-Santiago, Mónica; Rodríguez-Franco, Antonio; Priego-Capote, Feliciano
2018-01-01
We applied multi-omics approaches (transcriptomics, proteomics and metabolomics) to study the effect of iron starvation on the Gram-positive human pathogen Streptococcus pneumoniae to elucidate global changes in the bacterium in a condition similar to what can be found in the host during an infectious episode. We treated the reference strain TIGR4 with the iron chelator deferoxamine mesylate. DNA microarrays revealed changes in the expression of operons involved in multiple biological processes, with a prevalence of genes coding for ion binding proteins. We also studied the changes in protein abundance by 2-DE followed by MALDI-TOF/TOF analysis of total cell extracts and secretome fractions. The main proteomic changes were found in proteins related to the primary and amino sugar metabolism, especially in enzymes with divalent cations as cofactors. Finally, the metabolomic analysis of intracellular metabolites showed altered levels of amino sugars involved in the cell wall peptidoglycan metabolism. This work shows the utility of multi-perspective studies that can provide complementary results for the comprehension of how a given condition can influence global physiological changes in microorganisms.
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NASA Technical Reports Server (NTRS)
2008-01-01
Regulatory control in biological systems is exerted at all levels within the central dogma of biology. Metabolites are the end products of all cellular regulatory processes and reflect the ultimate outcome of potential changes suggested by genomics and proteomics caused by an environmental stimulus or genetic modification. Following on the heels of genomics, transcriptomics, and proteomics, metabolomics has become an inevitable part of complete-system biology because none of the lower "-omics" alone provide direct information about how changes in mRNA or protein are coupled to changes in biological function. The challenges are much greater than those encountered in genomics because of the greater number of metabolites and the greater diversity of their chemical structures and properties. To meet these challenges, much developmental work is needed, including (1) methodologies for unbiased extraction of metabolites and subsequent quantification, (2) algorithms for systematic identification of metabolites, (3) expertise and competency in handling a large amount of information (data set), and (4) integration of metabolomics with other "omics" and data mining (implication of the information). This article reviews the project accomplishments.
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TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.
Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini
2018-05-14
Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.
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Cordero, Chiara; Kiefl, Johannes; Schieberle, Peter; Reichenbach, Stephen E; Bicchi, Carlo
2015-01-01
Modern omics disciplines dealing with food flavor focus the analytical efforts on the elucidation of sensory-active compounds, including all possible stimuli of multimodal perception (aroma, taste, texture, etc.) by means of a comprehensive, integrated treatment of sample constituents, such as physicochemical properties, concentration in the matrix, and sensory properties (odor/taste quality, perception threshold). Such analyses require detailed profiling of known bioactive components as well as advanced fingerprinting techniques to catalog sample constituents comprehensively, quantitatively, and comparably across samples. Multidimensional analytical platforms support comprehensive investigations required for flavor analysis by combining information on analytes' identities, physicochemical behaviors (volatility, polarity, partition coefficient, and solubility), concentration, and odor quality. Unlike other omics, flavor metabolomics and sensomics include the final output of the biological phenomenon (i.e., sensory perceptions) as an additional analytical dimension, which is specifically and exclusively triggered by the chemicals analyzed. However, advanced omics platforms, which are multidimensional by definition, pose challenging issues not only in terms of coupling with detection systems and sample preparation, but also in terms of data elaboration and processing. The large number of variables collected during each analytical run provides a high level of information, but requires appropriate strategies to exploit fully this potential. This review focuses on advances in comprehensive two-dimensional gas chromatography and analytical platforms combining two-dimensional gas chromatography with olfactometry, chemometrics, and quantitative assays for food sensory analysis to assess the quality of a given product. We review instrumental advances and couplings, automation in sample preparation, data elaboration, and a selection of applications.
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Identifying novel glioma associated pathways based on systems biology level meta-analysis.
Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong
2013-01-01
With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.
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Computational Tools for Parsimony Phylogenetic Analysis of Omics Data
Salazar, Jose; Amri, Hakima; Noursi, David
2015-01-01
Abstract High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value (“0” for normal states and “1” for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis. PMID:26230532
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Eke, Iris; Makinde, Adeola Y; Aryankalayil, Molykutty J; Ahmed, Mansoor M; Coleman, C Norman
2016-11-01
New technologies enabling the analysis of various molecules, including DNA, RNA, proteins and small metabolites, can aid in understanding the complex molecular processes in cancer cells. In particular, for the use of novel targeted therapeutics, elucidation of the mechanisms leading to cell death or survival is crucial to eliminate tumor resistance and optimize therapeutic efficacy. While some techniques, such as genomic analysis for identifying specific gene mutations or epigenetic testing of promoter methylation, are already in clinical use, other "omics-based" assays are still evolving. Here, we provide an overview of the current status of molecular profiling methods, including promising research strategies, as well as possible challenges, and their emerging role in radiation oncology. Published by Elsevier Ireland Ltd.
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Application of omics data in regulatory toxicology: report of an international BfR expert workshop.
Marx-Stoelting, P; Braeuning, A; Buhrke, T; Lampen, A; Niemann, L; Oelgeschlaeger, M; Rieke, S; Schmidt, F; Heise, T; Pfeil, R; Solecki, R
2015-11-01
Advances in omics techniques and molecular toxicology are necessary to provide new perspectives for regulatory toxicology. By the application of modern molecular techniques, more mechanistic information should be gained to support standard toxicity studies and to contribute to a reduction and refinement of animal experiments required for certain regulatory purposes. The relevance and applicability of data obtained by omics methods to regulatory purposes such as grouping of chemicals, mode of action analysis or classification and labelling needs further improvement, defined validation and cautious expert judgment. Based on the results of an international expert workshop organized 2014 by the Federal Institute for Risk Assessment in Berlin, this paper is aimed to provide a critical overview of the regulatory relevance and reliability of omics methods, basic requirements on data quality and validation, as well as regulatory criteria to decide which effects observed by omics methods should be considered adverse or non-adverse. As a way forward, it was concluded that the inclusion of omics data can facilitate a more flexible approach for regulatory risk assessment and may help to reduce or refine animal testing.
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Novel insights, challenges and practical implications of DOHaD-omics research.
Hodyl, Nicolette A; Muhlhausler, Beverly
2016-02-15
Research investigating the developmental origins of health and disease (DOHaD) has never had the technology to investigate physiology in such a data-rich capacity and at such a microlevel as it does now. A symposium at the inaugural meeting of the DOHaD Society of Australia and New Zealand outlined the advantages and challenges of using "-omics" technologies in DOHaD research. DOHaD studies with -omics approaches to generate large, rich datasets were discussed. We discuss implications for policy and practice and make recommendations to facilitate successful translation of results of future DOHaD-omics studies.
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NMR and MS Methods for Metabolomics.
Amberg, Alexander; Riefke, Björn; Schlotterbeck, Götz; Ross, Alfred; Senn, Hans; Dieterle, Frank; Keck, Matthias
2017-01-01
Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.
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NMR and MS methods for metabonomics.
Dieterle, Frank; Riefke, Björn; Schlotterbeck, Götz; Ross, Alfred; Senn, Hans; Amberg, Alexander
2011-01-01
Metabonomics, also often referred to as "metabolomics" or "metabolic profiling," is the systematic profiling of metabolites in bio-fluids or tissues of organisms and their temporal changes. In the last decade, metabonomics has become increasingly popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabonomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabonomics, i.e., NMR, LC-MS, UPLC-MS, and GC-MS have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabonomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation, to determining the measurement details of all analytical platforms, and finally, to discussing the corresponding specific steps of data analysis.
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Post-genomics nanotechnology is gaining momentum: nanoproteomics and applications in life sciences.
Kobeissy, Firas H; Gulbakan, Basri; Alawieh, Ali; Karam, Pierre; Zhang, Zhiqun; Guingab-Cagmat, Joy D; Mondello, Stefania; Tan, Weihong; Anagli, John; Wang, Kevin
2014-02-01
The post-genomics era has brought about new Omics biotechnologies, such as proteomics and metabolomics, as well as their novel applications to personal genomics and the quantified self. These advances are now also catalyzing other and newer post-genomics innovations, leading to convergences between Omics and nanotechnology. In this work, we systematically contextualize and exemplify an emerging strand of post-genomics life sciences, namely, nanoproteomics and its applications in health and integrative biological systems. Nanotechnology has been utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures. Those applications, though still in their infancy, have led to several highly sensitive diagnostics and new methods of drug delivery and targeted therapy for clinical use. The present article differs from previous analyses of nanoproteomics in that it offers an in-depth and comparative evaluation of the attendant biotechnology portfolio and their applications as seen through the lens of post-genomics life sciences and biomedicine. These include: (1) immunosensors for inflammatory, pathogenic, and autoimmune markers for infectious and autoimmune diseases, (2) amplified immunoassays for detection of cancer biomarkers, and (3) methods for targeted therapy and automatically adjusted drug delivery such as in experimental stroke and brain injury studies. As nanoproteomics becomes available both to the clinician at the bedside and the citizens who are increasingly interested in access to novel post-genomics diagnostics through initiatives such as the quantified self, we anticipate further breakthroughs in personalized and targeted medicine.
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Imaging and the completion of the omics paradigm in breast cancer.
Leithner, D; Horvat, J V; Ochoa-Albiztegui, R E; Thakur, S; Wengert, G; Morris, E A; Helbich, T H; Pinker, K
2018-06-08
Within the field of oncology, "omics" strategies-genomics, transcriptomics, proteomics, metabolomics-have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with "omics" data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology-pathology correlation from the anatomical-histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.
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Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin
2015-12-01
One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C.
2016-05-03
ABSTRACT Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. Themetabolite,protein, andlipidextraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of thismore » protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental,in vitro, and clinical). IMPORTANCEIn systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.« less
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'Omics analysis of low dose acetaminophen intake demonstrates novel response pathways in humans
DOE Office of Scientific and Technical Information (OSTI.GOV)
Jetten, Marlon J.A.; Gaj, Stan; Ruiz-Aracama, Ainhoa
2012-03-15
Acetaminophen is the primary cause of acute liver toxicity in Europe/USA, which led the FDA to reconsider recommendations concerning safe acetaminophen dosage/use. Unfortunately, the current tests for liver toxicity are no ideal predictive markers for liver injury, i.e. they only measure acetaminophen exposure after profound liver toxicity has already occurred. Furthermore, these tests do not provide mechanistic information. Here, 'omics techniques (global analysis of metabolomic/gene-expression responses) may provide additional insight. To better understand acetaminophen-induced responses at low doses, we evaluated the effects of (sub-)therapeutic acetaminophen doses on metabolite formation and global gene-expression changes (including, for the first time, full-genome humanmore » miRNA expression changes) in blood/urine samples from healthy human volunteers. Many known and several new acetaminophen-metabolites were detected, in particular in relation to hepatotoxicity-linked, oxidative metabolism of acetaminophen. Transcriptomic changes indicated immune-modulating effects (2 g dose) and oxidative stress responses (4 g dose). For the first time, effects of acetaminophen on full-genome human miRNA expression have been considered and confirmed the findings on mRNA level. 'Omics techniques outperformed clinical chemistry tests and revealed novel response pathways to acetaminophen in humans. Although no definitive conclusion about potential immunotoxic effects of acetaminophen can be drawn from this study, there are clear indications that the immune system is triggered even after intake of low doses of acetaminophen. Also, oxidative stress-related gene responses, similar to those seen after high dose acetaminophen exposure, suggest the occurrence of possible pre-toxic effects of therapeutic acetaminophen doses. Possibly, these effects are related to dose-dependent increases in levels of hepatotoxicity-related metabolites. -- Highlights: ► 'Omics techniques outperformed classic clinical chemistry tests. ► Metabolomic analyses led to the detection of five new acetaminophen metabolites. ► Low dose APAP changed immune and oxidative stress related gene expression in blood. ► APAP-induced full-genome human blood miRNA profiles were assessed for the first time.« less
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Wallace, Robert J; Snelling, Timothy J; McCartney, Christine A; Tapio, Ilma; Strozzi, Francesco
2017-01-16
Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all shown promising results. Rumen microbial amino acid metabolism lies at the root of excessive nitrogen emissions from ruminants, yet only indirect inferences for nitrogen emissions can be drawn from meta-omics studies published so far. Annotation of meta-omics data depends on databases that are generally weak in rumen microbial entries. The Hungate 1000 project and Global Rumen Census initiatives are therefore essential to improve the interpretation of sequence/metabolic information.
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West, Allyson A; Caudill, Marie A
2014-08-01
Nutritional genomics, defined as the study of reciprocal interactions among nutrients, metabolic intermediates, and the genome, along with other closely related nutritional -omic fields (eg, epigenomics, transcriptomics, and metabolomics) have become vital areas of nutrition study and knowledge. Utilizing results from human metabolic research on the essential nutrient choline, this article illustrates how nutrigenetic, nutrigenomic, and inter-related -omic research has provided new insights into choline metabolism and its effect on physiologic processes. Findings from highlighted choline research are also discussed in the context of translation to clinical and public health nutrition applications. Overall, this article underscores the utility of -omic research methods in elucidating nutrient metabolism as well as the potential for nutritional -omic concepts and discoveries to be broadly applied in nutritional practice. Copyright © 2014 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.
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Integrated omics dissection of proteome dynamics during cardiac remodeling.
Lau, Edward; Cao, Quan; Lam, Maggie P Y; Wang, Jie; Ng, Dominic C M; Bleakley, Brian J; Lee, Jessica M; Liem, David A; Wang, Ding; Hermjakob, Henning; Ping, Peipei
2018-01-09
Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.
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Making the Most of Omics for Symbiosis Research
Chaston, J.; Douglas, A.E.
2012-01-01
Omics, including genomics, proteomics and metabolomics, enable us to explain symbioses in terms of the underlying molecules and their interactions. The central task is to transform molecular catalogs of genes, metabolites etc. into a dynamic understanding of symbiosis function. We review four exemplars of omics studies that achieve this goal, through defined biological questions relating to metabolic integration and regulation of animal-microbial symbioses, the genetic autonomy of bacterial symbionts, and symbiotic protection of animal hosts from pathogens. As omic datasets become increasingly complex, computationally-sophisticated downstream analyses are essential to reveal interactions not evident to visual inspection of the data. We discuss two approaches, phylogenomics and transcriptional clustering, that can divide the primary output of omics studies – long lists of factors – into manageable subsets, and we describe how they have been applied to analyze large datasets and generate testable hypotheses. PMID:22983030
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Techniques for integrating ‐omics data
Akula, Siva Prasad; Miriyala, Raghava Naidu; Thota, Hanuman; Rao, Allam Appa; Gedela, Srinubabu
2009-01-01
The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present ‐omics community, because ‐omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data. PMID:19255651
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Techniques for integrating -omics data.
Akula, Siva Prasad; Miriyala, Raghava Naidu; Thota, Hanuman; Rao, Allam Appa; Gedela, Srinubabu
2009-01-01
The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.
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Omics Workshop Videocast Available | Office of Cancer Clinical Proteomics Research
The Omics Integration in Biology and Medicine Workshop, held on June 19th and 20th is now available for viewing on NIH Videocast: Day 1 and Day 2. The workshop focused on the emerging field of integrating disparate omic data from genomics, proteomics, glycomics, etc. in order to better understand key biological processes and also improve clinical practice.
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From data to knowledge: The future of multi-omics data analysis for the rhizosphere
DOE Office of Scientific and Technical Information (OSTI.GOV)
Allen White, Richard; Borkum, Mark I.; Rivas-Ubach, Albert
The rhizosphere is the interface between a plant's roots and its surrounding soil. The rhizosphere microbiome, a complex microbial ecosystem, nourishes the terrestrial biosphere. Integrated multi-omics is a modern approach to systems biology that analyzes and interprets the datasets of multiple -omes of both individual organisms and multi-organism communities and consortia. The successful usage and application of integrated multi-omics to rhizospheric science is predicated upon the availability of rhizosphere-specific data, metadata and software. This review analyzes the availability of multi-omics data, metadata and software for rhizospheric science, identifying potential issues, challenges and opportunities.
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Alzheimer's disease in the omics era.
Sancesario, Giulia M; Bernardini, Sergio
2018-06-18
Recent progresses in high-throughput technologies have led to a new scenario in investigating pathologies, named the "Omics era", which integrate the opportunity to collect large amounts of data and information at the molecular and protein levels together with the development of novel computational and statistical tools that are able to analyze and filter such data. Subsequently, advances in genotyping arrays, next generation sequencing, mass spectrometry technology, and bioinformatics allowed for the simultaneous large-scale study of thousands of genes (genomics), epigenetics factors (epigenomics), RNA (transcriptomics), metabolites (metabolomics) and proteins(proteomics), with the possibility of integrating multiple types of omics data ("multi -omics"). All of these technological innovations have modified the approach to the study of complex diseases, such as Alzheimer's Disease (AD), thus representing a promising tool to investigate the relationship between several molecular pathways in AD as well as other pathologies. This review focuses on the current knowledge on the pathology of AD, the recent findings from Omics sciences, and the challenge of the use of Big Data. We then focus on future perspectives for Omics sciences, such as the discovery of novel diagnostic biomarkers or drugs. Copyright © 2018. Published by Elsevier Inc.
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Omics Data Complementarity Underlines Functional Cross-Communication in Yeast.
Malod-Dognin, Noël; Pržulj, Nataša
2017-06-10
Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.
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“Omics” of Maize Stress Response for Sustainable Food Production: Opportunities and Challenges
Gong, Fangping; Yang, Le; Tai, Fuju; Hu, Xiuli
2014-01-01
Abstract Maize originated in the highlands of Mexico approximately 8700 years ago and is one of the most commonly grown cereal crops worldwide, followed by wheat and rice. Abiotic stresses (primarily drought, salinity, and high and low temperatures), together with biotic stresses (primarily fungi, viruses, and pests), negatively affect maize growth, development, and eventually production. To understand the response of maize to abiotic and biotic stresses and its mechanism of stress tolerance, high-throughput omics approaches have been used in maize stress studies. Integrated omics approaches are crucial for dissecting the temporal and spatial system-level changes that occur in maize under various stresses. In this comprehensive analysis, we review the primary types of stresses that threaten sustainable maize production; underscore the recent advances in maize stress omics, especially proteomics; and discuss the opportunities, challenges, and future directions of maize stress omics, with a view to sustainable food production. The knowledge gained from studying maize stress omics is instrumental for improving maize to cope with various stresses and to meet the food demands of the exponentially growing global population. Omics systems science offers actionable potential solutions for sustainable food production, and we present maize as a notable case study. PMID:25401749
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MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.
Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk
2016-03-18
Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .
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Personalized nutrition and obesity.
Qi, Lu
2014-08-01
The past few decades have witnessed a rapid rise in nutrition-related disorders such as obesity in the United States and over the world. Traditional nutrition research has associated various foods and nutrients with obesity. Recent advances in genomics have led to identification of the genetic variants determining body weight and related dietary factors such as intakes of energy and macronutrients. In addition, compelling evidence has lent support to interactions between genetic variations and dietary factors in relation to obesity and weight change. Moreover, recently emerging data from other 'omics' studies such as epigenomics and metabolomics suggest that more complex interplays between the global features of human body and dietary factors may exist at multiple tiers in affecting individuals' susceptibility to obesity; and a concept of 'personalized nutrition' has been proposed to integrate this novel knowledge with traditional nutrition research, with the hope ultimately to endorse person-centric diet intervention to mitigate obesity and related disorders.
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Nagel, Zachary D; Engelward, Bevin P; Brenner, David J; Begley, Thomas J; Sobol, Robert W; Bielas, Jason H; Stambrook, Peter J; Wei, Qingyi; Hu, Jennifer J; Terry, Mary Beth; Dilworth, Caroline; McAllister, Kimberly A; Reinlib, Les; Worth, Leroy; Shaughnessy, Daniel T
2017-08-01
The rise of advanced technologies for characterizing human populations at the molecular level, from sequence to function, is shifting disease prevention paradigms toward personalized strategies. Because minimization of adverse outcomes is a key driver for treatment decisions for diseased populations, developing personalized therapy strategies represent an important dimension of both precision medicine and personalized prevention. In this commentary, we highlight recently developed enabling technologies in the field of DNA damage, DNA repair, and mutagenesis. We propose that omics approaches and functional assays can be integrated into population studies that fuse basic, translational and clinical research with commercial expertise in order to accelerate personalized prevention and treatment of cancer and other diseases linked to aberrant responses to DNA damage. This collaborative approach is generally applicable to efforts to develop data-driven, individualized prevention and treatment strategies for other diseases. We also recommend strategies for maximizing the use of biological samples for epidemiological studies, and for applying emerging technologies to clinical applications. Copyright © 2017 Elsevier B.V. All rights reserved.
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Nagel, Zachary D.; Engelward, Bevin P.; Brenner, David J.; Begley, Thomas J.; Sobol, Robert W.; Bielas, Jason H.; Stambrook, Peter J.; Wei, Qingyi; Hu, Jennifer J.; Terry, Mary Beth; Dilworth, Caroline; McAllister, Kimberly A.; Reinlib, Les; Worth, Leroy; Shaughnessy, Daniel T.
2018-01-01
The rise of advanced technologies for characterizing human populations at the molecular level, from sequence to function, is shifting disease prevention paradigms toward personalized strategies. Because minimization of adverse outcomes is a key driver for treatment decisions for diseased populations, developing personalized therapy strategies represent an important dimension of both precision medicine and personalized prevention. In this commentary, we highlight recently developed enabling technologies in the field of DNA damage, DNA repair, and mutagenesis. We propose that omics approaches and functional assays can be integrated into population studies that fuse basic, translational and clinical research with commercial expertise in order to accelerate personalized prevention and treatment of cancer and other diseases linked to aberrant responses to DNA damage. This collaborative approach is generally applicable to efforts to develop data-driven, individualized prevention and treatment strategies for other diseases. We also recommend strategies for maximizing the use of biological samples for epidemiological studies, and for applying emerging technologies to clinical applications. PMID:28458064
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Data integration in the era of omics: current and future challenges
2014-01-01
To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community. PMID:25032990
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Metabolomics - A wide-open door to personalized treatment in chronic heart failure?
Marcinkiewicz-Siemion, M; Ciborowski, M; Kretowski, A; Musial, W J; Kaminski, K A
2016-09-15
Heart failure (HF) is a complex syndrome representing a final stage of various cardiovascular diseases. Despite significant improvement in the diagnosis and treatment (e.g. ACE-inhibitors, β-blockers, aldosterone antagonists, cardiac resynchronization therapy) of the disease, prognosis of optimally treated patients remains very serious and HF mortality is still unacceptably high. Therefore there is a strong need for further exploration of novel analytical methods, predictive and prognostic biomarkers and more personalized treatment. The metabolism of the failing heart being significantly impaired from its baseline state may be a future target not only for biomarker discovery but also for the pharmacologic intervention. However, an assessment of a particular, isolated metabolite or protein cannot be fully informative and makes a correct interpretation difficult. On the other hand, metabolites profile analysis may greatly assist investigator in an interpretation of the altered pathway dynamics, especially when combined with other lines of evidence (e.g. metabolites from the same pathway, transcriptomics, proteomics). Despite many prior studies on metabolism, the knowledge of peripheral and cardiac pathophysiological mechanisms responsible for the metabolic imbalance and progression of the disease is still insufficient. Metabolomics enabling comprehensive characterization of low molecular weight metabolites (e.g. lipids, sugars, organic acids, amino acids) that reflects the complete metabolic phenotype seems to be the key for further potential improvement in HF treatment (diet-based or biochemical-based). Will this -omics technique one day open a door to easy patients identification before they have a heart failure onset or its decompensation? Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine.
Ziegelstein, Roy C
2017-10-16
Clinical practice guidelines have been developed for many common conditions based on data from randomized controlled trials. When medicine is informed solely by clinical practice guidelines, however, the patient is not treated as an individual, but rather a member of a group. Precision medicine, as defined herein, characterizes unique biological characteristics of the individual or of specimens obtained from an individual to tailor diagnostics and therapeutics to a specific patient. These unique biological characteristics are defined by the tools of precision medicine: genomics, proteomics, metabolomics, epigenomics, pharmacogenomics, and other "-omics." Personalized medicine, as defined herein, uses additional information about the individual derived from knowing the patient as a person. These unique personal characteristics are defined by tools known as personomics which takes into account an individual's personality, preferences, values, goals, health beliefs, social support network, financial resources, and unique life circumstances that affect how and when a given health condition will manifest in that person and how that condition will respond to treatment. In this paradigm, precision medicine may be considered a necessary step in the evolution of medical care to personalized medicine, with personomics as the missing link.
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Campos, Pollyanna Fernandes; Andrade-Silva, Débora; Zelanis, André; Paes Leme, Adriana Franco; Rocha, Marisa Maria Teixeira; Menezes, Milene Cristina; Serrano, Solange M.T.; Junqueira-de-Azevedo, Inácio de Loiola Meirelles
2016-01-01
Only few studies on snake venoms were dedicated to deeply characterize the toxin secretion of animals from the Colubridae family, despite the fact that they represent the majority of snake diversity. As a consequence, some evolutionary trends observed in venom proteins that underpinned the evolutionary histories of snake toxins were based on data from a minor parcel of the clade. Here, we investigated the proteins of the totally unknown venom from Phalotris mertensi (Dipsadinae subfamily), in order to obtain a detailed profile of its toxins and to appreciate evolutionary tendencies occurring in colubrid venoms. By means of integrated omics and functional approaches, including RNAseq, Sanger sequencing, high-resolution proteomics, recombinant protein production, and enzymatic tests, we verified an active toxic secretion containing up to 21 types of proteins. A high content of Kunitz-type proteins and C-type lectins were observed, although several enzymatic components such as metalloproteinases and an L-amino acid oxidase were also present in the venom. Interestingly, an arguable venom component of other species was demonstrated as a true venom protein and named svLIPA (snake venom acid lipase). This finding indicates the importance of checking the actual protein occurrence across species before rejecting genes suggested to code for toxins, which are relevant for the discussion about the early evolution of reptile venoms. Moreover, trends in the evolution of some toxin classes, such as simplification of metalloproteinases and rearrangements of Kunitz and Wap domains, parallel similar phenomena observed in other venomous snake families and provide a broader picture of toxin evolution. PMID:27412610
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Omics Integration in Biology and Medicine Workshop | Office of Cancer Clinical Proteomics Research
The focus of this meeting will be on the emerging field of integrating disparate omic data from genomics, proteomics, glycomics, etc. in order to better understand key biological processes and also improve clinical practice. Discussants will focus on identifying the technical and biological barriers in omic integration, with solutions to build a consensus towards data integration in bioscience and to better define phenotypes.
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Integration of multi-omics data for integrative gene regulatory network inference.
Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon
2017-01-01
Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.
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Educating future nursing scientists: Recommendations for integrating omics content in PhD programs.
Conley, Yvette P; Heitkemper, Margaret; McCarthy, Donna; Anderson, Cindy M; Corwin, Elizabeth J; Daack-Hirsch, Sandra; Dorsey, Susan G; Gregory, Katherine E; Groer, Maureen W; Henly, Susan J; Landers, Timothy; Lyon, Debra E; Taylor, Jacquelyn Y; Voss, Joachim
2015-01-01
Preparing the next generation of nursing scientists to conduct high-impact, competitive, sustainable, innovative, and interdisciplinary programs of research requires that the curricula for PhD programs keep pace with emerging areas of knowledge and health care/biomedical science. A field of inquiry that holds great potential to influence our understanding of the underlying biology and mechanisms of health and disease is omics. For the purpose of this article, omics refers to genomics, transcriptomics, proteomics, epigenomics, exposomics, microbiomics, and metabolomics. Traditionally, most PhD programs in schools of nursing do not incorporate this content into their core curricula. As part of the Council for the Advancement of Nursing Science's Idea Festival for Nursing Science Education, a work group charged with addressing omics preparation for the next generation of nursing scientists was convened. The purpose of this article is to describe key findings and recommendations from the work group that unanimously and enthusiastically support the incorporation of omics content into the curricula of PhD programs in nursing. The work group also calls to action faculty in schools of nursing to develop strategies to enable students needing immersion in omics science and methods to execute their research goals. Copyright © 2015 Elsevier Inc. All rights reserved.
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MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.
Maes, Alexandre; Martinez, Xavier; Druart, Karen; Laurent, Benoist; Guégan, Sean; Marchand, Christophe H; Lemaire, Stéphane D; Baaden, Marc
2018-06-21
Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.
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Omicseq: a web-based search engine for exploring omics datasets
Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng
2017-01-01
Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462
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Integration of multi-omics data for integrative gene regulatory network inference
Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun
2017-01-01
Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189
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Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio
2017-01-01
Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094
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A cross-omics toxicological evaluation of drinking water treated with different processes.
Shi, Peng; Jia, Shuyu; Zhang, Xu-Xiang; Zhao, Fuzheng; Chen, Yajun; Zhou, Qing; Cheng, Shupei; Li, Ai-Min
2014-04-30
Cross-omics profiling and phenotypic analysis were conducted to comprehensively assess the toxicities of source of drinking water (SDW), effluent of conventional treatment (ECT) and effluent of advanced treatment (EAT) in a water treatment plant. SDW feeding increased body weight, and relative liver and kidney weights of mice. Hepatic histopathological damages and serum biochemical alterations were observed in the mice fed with SDW and ECT, but EAT feeding showed no obvious effects. Transcriptomic analysis demonstrated that exposure to water samples caused differential expression of hundreds of genes in livers. Cluster analysis of the differentially expressed genes which generated by both microarrays and digital gene expression showed similar grouping patterns. Proteomic and metabolomics analyses indicated that drinking SDW, ECT and EAT generated 59, 145 and 41 significantly altered proteins in livers and 8, 2 and 0 altered metabolites in serum, respectively. SDW was found to affect several metabolic pathways including metabolism of xenobiotics by cytochrome P450 and fatty acid metabolism. SDW and ECT might induce molecular toxicities to mice, but the advanced treatment process can reduce the potential health risk by effectively removing toxic chemicals in drinking water. Copyright © 2014 Elsevier B.V. All rights reserved.
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Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.
Tranchevent, Léon-Charles; Nazarov, Petr V; Kaoma, Tony; Schmartz, Georges P; Muller, Arnaud; Kim, Sang-Yoon; Rajapakse, Jagath C; Azuaje, Francisco
2018-06-07
One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.
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Analytical methods in sphingolipidomics: Quantitative and profiling approaches in food analysis.
Canela, Núria; Herrero, Pol; Mariné, Sílvia; Nadal, Pedro; Ras, Maria Rosa; Rodríguez, Miguel Ángel; Arola, Lluís
2016-01-08
In recent years, sphingolipidomics has emerged as an interesting omic science that encompasses the study of the full sphingolipidome characterization, content, structure and activity in cells, tissues or organisms. Like other omics, it has the potential to impact biomarker discovery, drug development and systems biology knowledge. Concretely, dietary food sphingolipids have gained considerable importance due to their extensively reported bioactivity. Because of the complexity of this lipid family and their diversity among foods, powerful analytical methodologies are needed for their study. The analytical tools developed in the past have been improved with the enormous advances made in recent years in mass spectrometry (MS) and chromatography, which allow the convenient and sensitive identification and quantitation of sphingolipid classes and form the basis of current sphingolipidomics methodologies. In addition, novel hyphenated nuclear magnetic resonance (NMR) strategies, new ionization strategies, and MS imaging are outlined as promising technologies to shape the future of sphingolipid analyses. This review traces the analytical methods of sphingolipidomics in food analysis concerning sample extraction, chromatographic separation, the identification and quantification of sphingolipids by MS and their structural elucidation by NMR. Copyright © 2015 Elsevier B.V. All rights reserved.
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Kim, Young-Mo; Schmidt, Brian J.; Kidwai, Afshan S.; Jones, Marcus B.; Deatherage Kaiser, Brooke L.; Brewer, Heather M.; Mitchell, Hugh D.; Palsson, Bernhard O.; McDermott, Jason E.; Heffron, Fred; Smith, Richard D.; Peterson, Scott N.; Ansong, Charles; Hyduke, Daniel R.; Metz, Thomas O.; Adkins, Joshua N.
2013-01-01
Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake. PMID:23559334
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Kratochwill, Klaus; Bender, Thorsten O; Lichtenauer, Anton M; Herzog, Rebecca; Tarantino, Silvia; Bialas, Katarzyna; Jörres, Achim; Aufricht, Christoph
2015-01-01
Recent research suggests that cytoprotective responses, such as expression of heat-shock proteins, might be inadequately induced in mesothelial cells by heat-sterilized peritoneal dialysis (PD) fluids. This study compares transcriptome data and multiple protein expression profiles for providing new insight into regulatory mechanisms. Two-dimensional difference gel electrophoresis (2D-DIGE) based proteomics and topic defined gene expression microarray-based transcriptomics techniques were used to evaluate stress responses in human omental peritoneal mesothelial cells in response to heat- or filter-sterilized PD fluids. Data from selected heat-shock proteins were validated by 2D western-blot analysis. Comparison of proteomics and transcriptomics data discriminated differentially regulated protein abundance into groups depending on correlating or noncorrelating transcripts. Inadequate abundance of several heat-shock proteins following exposure to heat-sterilized PD fluids is not reflected on the mRNA level indicating interference beyond transcriptional regulation. For the first time, this study describes evidence for posttranscriptional inadequacy of heat-shock protein expression by heat-sterilized PD fluids as a novel cytotoxic property. Cross-omics technologies introduce a novel way of understanding PDF bioincompatibility and searching for new interventions to reestablish adequate cytoprotective responses.
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An OMIC approach to elaborate the antibacterial mechanisms of different alkaloids.
Avci, Fatma Gizem; Sayar, Nihat Alpagu; Sariyar Akbulut, Berna
2018-05-01
Plant-derived substances have regained interest in the fight against antibiotic resistance owing to their distinct antimicrobial mechanisms and multi-target properties. With the recent advances in instrumentation and analysis techniques, OMIC approaches are extensively used for target identification and elucidation of the mechanism of phytochemicals in drug discovery. In the current study, RNA sequencing based transcriptional profiling together with global differential protein expression analysis was used to comparatively elaborate the activities and the effects of the plant alkaloids boldine, bulbocapnine, and roemerine along with the well-known antimicrobial alkaloid berberine in Bacillus subtilis cells. The transcriptomic findings were validated by qPCR. Images from scanning electron microscope were obtained to visualize the effects on the whole-cells. The results showed that among the three selected alkaloids, only roemerine possessed antibacterial activity. Unlike berberine, which is susceptible to efflux through multidrug resistance pumps, roemerine accumulated in the cells. This in turn resulted in oxidative stress and building up of reactive oxygen species, which eventually deregulated various pathways such as iron uptake. Treatment with boldine or bulbocapnine slightly affected various metabolic pathways but has not changed the growth patterns at all. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Rawle, Rachel A; Hamerly, Timothy; Tripet, Brian P; Giannone, Richard J; Wurch, Louie; Hettich, Robert L; Podar, Mircea; Copié, Valerie; Bothner, Brian
2017-09-01
Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted 'omics' analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized using interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis-N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. This multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis-N. equitans association. Our study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies. Copyright © 2017 Elsevier B.V. All rights reserved.
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Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C.; Burnum-Johnson, Kristin E.; Kim, Young-Mo; Kyle, Jennifer E.; Matzke, Melissa M.; Shukla, Anil K.; Chu, Rosalie K.; Schepmoes, Athena A.; Jacobs, Jon M.; Baric, Ralph S.; Webb-Robertson, Bobbie-Jo; Smith, Richard D.
2016-01-01
ABSTRACT Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available. PMID:27822525
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Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh
2017-12-19
Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.
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Operating room fires in periocular surgery.
Connor, Michael A; Menke, Anne M; Vrcek, Ivan; Shore, John W
2018-06-01
A survey of ophthalmic plastic and reconstructive surgeons as well as seven-year data regarding claims made to the Ophthalmic Mutual Insurance Company (OMIC) is used to discuss operating room fires in periocular surgery. A retrospective review of all closed claim operating room fires submitted to OMIC was performed. A survey soliciting personal experiences with operating room fires was distributed to all American Society of Oculoplastic and Reconstructive Surgeons. Over the last 2 decades, OMIC managed 7 lawsuits resulting from an operating room fire during periocular surgery. The mean settlement per lawsuit was $145,285 (range $10,000-474,994). All six patients suffered burns to the face, and three required admission to a burn unit. One hundred and sixty-eight surgeons participated in the online survey. Approximately 44% of survey respondents have experienced at least one operating room fire. Supplemental oxygen was administered in 88% of these cases. Most surgical fires reported occurred in a hospital-based operating room (59%) under monitored anesthesia care (79%). Monopolar cautery (41%) and thermal, high-temperature cautery (41%) were most commonly reported as the inciting agents. Almost half of the patients involved in a surgical fire experienced a complication from the fire (48%). Sixty-nine percent of hospital operating rooms and 66% of ambulatory surgery centers maintain an operating room fire prevention policy. An intraoperative fire can be costly for both the patient and the surgeon. Ophthalmic surgeons operate in an oxygen rich and therefore flammable environment. Proactive measures can be undertaken to reduce the incidence of surgical fires periocular surgery; however, a fire can occur at any time and the entire operating room team must be constantly vigilant to prevent and manage operating room fires.
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The evolution of analytical chemistry methods in foodomics.
Gallo, Monica; Ferranti, Pasquale
2016-01-08
The methodologies of food analysis have greatly evolved over the past 100 years, from basic assays based on solution chemistry to those relying on the modern instrumental platforms. Today, the development and optimization of integrated analytical approaches based on different techniques to study at molecular level the chemical composition of a food may allow to define a 'food fingerprint', valuable to assess nutritional value, safety and quality, authenticity and security of foods. This comprehensive strategy, defined foodomics, includes emerging work areas such as food chemistry, phytochemistry, advanced analytical techniques, biosensors and bioinformatics. Integrated approaches can help to elucidate some critical issues in food analysis, but also to face the new challenges of a globalized world: security, sustainability and food productions in response to environmental world-wide changes. They include the development of powerful analytical methods to ensure the origin and quality of food, as well as the discovery of biomarkers to identify potential food safety problems. In the area of nutrition, the future challenge is to identify, through specific biomarkers, individual peculiarities that allow early diagnosis and then a personalized prognosis and diet for patients with food-related disorders. Far from the aim of an exhaustive review of the abundant literature dedicated to the applications of omic sciences in food analysis, we will explore how classical approaches, such as those used in chemistry and biochemistry, have evolved to intersect with the new omics technologies to produce a progress in our understanding of the complexity of foods. Perhaps most importantly, a key objective of the review will be to explore the development of simple and robust methods for a fully applied use of omics data in food science. Copyright © 2015 Elsevier B.V. All rights reserved.
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McIntyre, Roger S; Cha, Danielle S; Jerrell, Jeanette M; Swardfager, Walter; Kim, Rachael D; Costa, Leonardo G; Baskaran, Anusha; Soczynska, Joanna K; Woldeyohannes, Hanna O; Mansur, Rodrigo B; Brietzke, Elisa; Powell, Alissa M; Gallaugher, Ashley; Kudlow, Paul; Kaidanovich-Beilin, Oksana; Alsuwaidan, Mohammad
2014-08-01
To provide a strategic framework for the prevention of bipolar disorder (BD) that incorporates a 'Big Data' approach to risk assessment for BD. Computerized databases (e.g., Pubmed, PsychInfo, and MedlinePlus) were used to access English-language articles published between 1966 and 2012 with the search terms bipolar disorder, prodrome, 'Big Data', and biomarkers cross-referenced with genomics/genetics, transcriptomics, proteomics, metabolomics, inflammation, oxidative stress, neurotrophic factors, cytokines, cognition, neurocognition, and neuroimaging. Papers were selected from the initial search if the primary outcome(s) of interest was (were) categorized in any of the following domains: (i) 'omics' (e.g., genomics), (ii) molecular, (iii) neuroimaging, and (iv) neurocognitive. The current strategic approach to identifying individuals at risk for BD, with an emphasis on phenotypic information and family history, has insufficient predictive validity and is clinically inadequate. The heterogeneous clinical presentation of BD, as well as its pathoetiological complexity, suggests that it is unlikely that a single biomarker (or an exclusive biomarker approach) will sufficiently augment currently inadequate phenotypic-centric prediction models. We propose a 'Big Data'- bioinformatics approach that integrates vast and complex phenotypic, anamnestic, behavioral, family, and personal 'omics' profiling. Bioinformatic processing approaches, utilizing cloud- and grid-enabled computing, are now capable of analyzing data on the order of tera-, peta-, and exabytes, providing hitherto unheard of opportunities to fundamentally revolutionize how psychiatric disorders are predicted, prevented, and treated. High-throughput networks dedicated to research on, and the treatment of, BD, integrating both adult and younger populations, will be essential to sufficiently enroll adequate samples of individuals across the neurodevelopmental trajectory in studies to enable the characterization and prevention of this heterogeneous disorder. Advances in bioinformatics using a 'Big Data' approach provide an opportunity for novel insights regarding the pathoetiology of BD. The coordinated integration of research centers, inclusive of mixed-age populations, is a promising strategic direction for advancing this line of neuropsychiatric research. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Establishing a Southern Swedish Malignant Melanoma OMICS and biobank clinical capability
2013-01-01
Background The objectives and goals of the Southern Swedish Malignant Melanoma (SSMM) are to develop, build and utilize cutting edge biobanks and OMICS platforms to better understand disease pathology and drug mechanisms. The SSMM research team is a truly cross-functional group with members from oncology, surgery, bioinformatics, proteomics, and genomics initiatives. Within the research team there are members who daily diagnose patients with suspect melanomas, do follow-ups on malignant melanoma patients and remove primary or metastatic lesions by surgery. This inter-disciplinary clinical patient care ensures a competence build as well as a best practice procedure where the patient benefits. Methods Clinical materials from patients before, during and after treatments with clinical end points are being collected. Tissue samples as well as bio-fluid samples such as blood fractions, plasma, serum and whole blood will be archived in 384-high density sample tube formats. Standardized approaches for patient selections, patient sampling, sample-processing and analysis platforms with dedicated protein assays and genomics platforms that will hold value for the research community are used. The patient biobank archives are fully automated with novel ultralow temperature biobank storage units and used as clinical resources. Results An IT-infrastructure using a laboratory information management system (LIMS) has been established, that is the key interface for the research teams in order to share and explore data generated within the project. The cross-site data repository in Lund forms the basis for sample processing, together with biological samples in southern Sweden, including blood fractions and tumor tissues. Clinical registries are associated with the biobank materials, including pathology reports on disease diagnosis on the malignant melanoma (MM) patients. Conclusions We provide data on the developments of protein profiling and targeted protein assays on isolated melanoma tumors, as well as reference blood standards that is used by the team members in the respective laboratories. These pilot data show biobank access and feasibility of performing quantitative proteomics in MM biobank repositories collected in southern Sweden. The scientific outcomes further strengthen the build of healthcare benefit in the complex challenges of malignant melanoma pathophysiology that is addressed by the novel personalized medicines entering the market. PMID:23445834
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Establishing a Southern Swedish Malignant Melanoma OMICS and biobank clinical capability.
Welinder, Charlotte; Jönsson, Göran; Ingvar, Christian; Lundgren, Lotta; Olsson, Håkan; Breslin, Thomas; Végvári, Akos; Laurell, Thomas; Rezeli, Melinda; Jansson, Bo; Baldetorp, Bo; Marko-Varga, György
2013-02-27
The objectives and goals of the Southern Swedish Malignant Melanoma (SSMM) are to develop, build and utilize cutting edge biobanks and OMICS platforms to better understand disease pathology and drug mechanisms. The SSMM research team is a truly cross-functional group with members from oncology, surgery, bioinformatics, proteomics, and genomics initiatives. Within the research team there are members who daily diagnose patients with suspect melanomas, do follow-ups on malignant melanoma patients and remove primary or metastatic lesions by surgery. This inter-disciplinary clinical patient care ensures a competence build as well as a best practice procedure where the patient benefits. Clinical materials from patients before, during and after treatments with clinical end points are being collected. Tissue samples as well as bio-fluid samples such as blood fractions, plasma, serum and whole blood will be archived in 384-high density sample tube formats. Standardized approaches for patient selections, patient sampling, sample-processing and analysis platforms with dedicated protein assays and genomics platforms that will hold value for the research community are used. The patient biobank archives are fully automated with novel ultralow temperature biobank storage units and used as clinical resources. An IT-infrastructure using a laboratory information management system (LIMS) has been established, that is the key interface for the research teams in order to share and explore data generated within the project. The cross-site data repository in Lund forms the basis for sample processing, together with biological samples in southern Sweden, including blood fractions and tumor tissues. Clinical registries are associated with the biobank materials, including pathology reports on disease diagnosis on the malignant melanoma (MM) patients. We provide data on the developments of protein profiling and targeted protein assays on isolated melanoma tumors, as well as reference blood standards that is used by the team members in the respective laboratories. These pilot data show biobank access and feasibility of performing quantitative proteomics in MM biobank repositories collected in southern Sweden. The scientific outcomes further strengthen the build of healthcare benefit in the complex challenges of malignant melanoma pathophysiology that is addressed by the novel personalized medicines entering the market.
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NASA Technical Reports Server (NTRS)
Berrios, Daniel C.; Thompson, Terri G.
2015-01-01
NASA GeneLab is expected to capture and distribute omics data and experimental and process conditions most relevant to research community in their statistical and theoretical analysis of NASAs omics data.
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Genome Sequencing Technologies and Nursing: What Are the Roles of Nurses and Nurse Scientists?
Taylor, Jacquelyn Y; Wright, Michelle L; Hickey, Kathleen T; Housman, David E
Advances in DNA sequencing technology have resulted in an abundance of personalized data with challenging clinical utility and meaning for clinicians. This wealth of data has potential to dramatically impact the quality of healthcare. Nurses are at the focal point in educating patients regarding relevant healthcare needs; therefore, an understanding of sequencing technology and utilizing these data are critical. The objective of this study was to explicate the role of nurses and nurse scientists as integral members of healthcare teams in improving understanding of DNA sequencing data and translational genomics for patients. A history of the nurse role in newborn screening is used as an exemplar. This study serves as an exemplar on how genome sequencing has been utilized in nursing science and incorporates linkages of other omics approaches used by nurses that are included in this special issue. This special issue showcased nurse scientists conducting multi-omic research from various methods, including targeted candidate genes, pharmacogenomics, proteomics, epigenomics, and the microbiome. From this vantage point, we provide an overview of the roles of nurse scientists in genome sequencing research and provide recommendations for the best utilization of nurses and nurse scientists related to genome sequencing.
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Taking Bioinformatics to Systems Medicine.
van Kampen, Antoine H C; Moerland, Perry D
2016-01-01
Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.
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Post-Genomics Nanotechnology Is Gaining Momentum: Nanoproteomics and Applications in Life Sciences
Kobeissy, Firas H.; Gulbakan, Basri; Alawieh, Ali; Karam, Pierre; Zhang, Zhiqun; Guingab-Cagmat, Joy D.; Mondello, Stefania; Tan, Weihong; Anagli, John
2014-01-01
Abstract The post-genomics era has brought about new Omics biotechnologies, such as proteomics and metabolomics, as well as their novel applications to personal genomics and the quantified self. These advances are now also catalyzing other and newer post-genomics innovations, leading to convergences between Omics and nanotechnology. In this work, we systematically contextualize and exemplify an emerging strand of post-genomics life sciences, namely, nanoproteomics and its applications in health and integrative biological systems. Nanotechnology has been utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures. Those applications, though still in their infancy, have led to several highly sensitive diagnostics and new methods of drug delivery and targeted therapy for clinical use. The present article differs from previous analyses of nanoproteomics in that it offers an in-depth and comparative evaluation of the attendant biotechnology portfolio and their applications as seen through the lens of post-genomics life sciences and biomedicine. These include: (1) immunosensors for inflammatory, pathogenic, and autoimmune markers for infectious and autoimmune diseases, (2) amplified immunoassays for detection of cancer biomarkers, and (3) methods for targeted therapy and automatically adjusted drug delivery such as in experimental stroke and brain injury studies. As nanoproteomics becomes available both to the clinician at the bedside and the citizens who are increasingly interested in access to novel post-genomics diagnostics through initiatives such as the quantified self, we anticipate further breakthroughs in personalized and targeted medicine. PMID:24410486
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Kangas, Antti J; Soininen, Pasi; Lawlor, Debbie A; Davey Smith, George; Ala-Korpela, Mika
2017-01-01
Abstract Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings. PMID:29106475
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Safety assessment of plant varieties using transcriptomics profiling and a one-class classifier.
van Dijk, Jeroen P; de Mello, Carla Souza; Voorhuijzen, Marleen M; Hutten, Ronald C B; Arisi, Ana Carolina Maisonnave; Jansen, Jeroen J; Buydens, Lutgarde M C; van der Voet, Hilko; Kok, Esther J
2014-10-01
An important part of the current hazard identification of novel plant varieties is comparative targeted analysis of the novel and reference varieties. Comparative analysis will become much more informative with unbiased analytical approaches, e.g. omics profiling. Data analysis estimating the similarity of new varieties to a reference baseline class of known safe varieties would subsequently greatly facilitate hazard identification. Further biological and eventually toxicological analysis would then only be necessary for varieties that fall outside this reference class. For this purpose, a one-class classifier tool was explored to assess and classify transcriptome profiles of potato (Solanum tuberosum) varieties in a model study. Profiles of six different varieties, two locations of growth, two year of harvest and including biological and technical replication were used to build the model. Two scenarios were applied representing evaluation of a 'different' variety and a 'similar' variety. Within the model higher class distances resulted for the 'different' test set compared with the 'similar' test set. The present study may contribute to a more global hazard identification of novel plant varieties. Copyright © 2014 Elsevier Inc. All rights reserved.
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Sarcoidosis Related Novel Candidate Genes Identified by Multi-Omics Integrative Analyses.
Hočevar, Keli; Maver, Aleš; Kunej, Tanja; Peterlin, Borut
2018-05-01
Sarcoidosis is a multifactorial systemic disease characterized by granulomatous inflammation and greatly impacting on global public health. The etiology and mechanisms of sarcoidosis are not fully understood. Recent high-throughput biological research has generated vast amounts of multi-omics big data on sarcoidosis, but their significance remains to be determined. We sought to identify novel candidate regions, and genes consistently altered in heterogeneous omics studies so as to reveal the underlying molecular mechanisms. We conducted a comprehensive integrative literature analysis on global data on sarcoidosis, including genomic, transcriptomic, proteomic, and phenomic studies. We performed positional integration analysis of 38 eligible datasets originating from 17 different biological layers. Using the integration interval length of 50 kb, we identified 54 regions reaching significance value p ≤ 0.0001 and 15 regions with significance value p ≤ 0.00001, when applying more stringent criteria. Secondary literature analysis of the top 20 regions, with the most significant accumulation of signals, revealed several novel candidate genes for which associations with sarcoidosis have not yet been established, but have considerable support for their involvement based on omic data. These new plausible candidate genes include NELFE, CFB, EGFL7, AGPAT2, FKBPL, NRC3, and NEU1. Furthermore, annotated data were prepared to enable custom visualization and browsing of these sarcoidosis related omics evidence in the University of California Santa Cruz (UCSC) Genome Browser. Further multi-omics approaches are called for sarcoidosis biomarkers and diagnostic and therapeutic innovation. Our approach for harnessing multi-omics data and the findings presented herein reflect important steps toward understanding the etiology and underlying pathological mechanisms of sarcoidosis.
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Omicseq: a web-based search engine for exploring omics datasets.
Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S
2017-07-03
The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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The MPLEx Protocol for Multi-omic Analyses of Soil Samples
DOE Office of Scientific and Technical Information (OSTI.GOV)
Nicora, Carrie D.; Burnum-Johnson, Kristin E.; Nakayasu, Ernesto S.
Mass spectrometry (MS)-based integrated metaproteomic, metabolomic and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for simultaneous metabolite, protein, and lipid extraction (MPLEx) from the exact same soil sample was created bymore » adapting a solvent-based approach. This MPLEx protocol has proven to be simple yet robust for many sample types and even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.« less
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Czaplicki, Lauren M.; Gunsch, Claudia K.
2017-01-01
Bioremediation is generally viewed as a cost effective and sustainable technology because it relies on microbes to transform pollutants into benign compounds. Advances in molecular biological analyses allow unprecedented microbial detection and are increasingly incorporated into bioremediation. Throughout history, state-of-the-art techniques have informed bioremediation strategies. However, the insights those techniques provided were not as in depth as those provided by recently developed omics tools. Advances in next generation sequencing (NGS) have now placed metagenomics and metatranscriptomics within reach of environmental engineers. As NGS costs decrease, metagenomics and metatranscriptomics have become increasingly feasible options to rapidly scan sites for specific degradative functions and identify microorganisms important in pollutant degradation. These omic techniques are capable of revolutionizing biological treatment in environmental engineering by allowing highly sensitive characterization of previously uncultured microorganisms. Omics enables the discovery of novel microorganisms for use in bioaugmentation and supports systematic optimization of biostimulation strategies. This review describes the omics journey from roots in biology and medicine to its current status in environmental engineering including potential future directions in commercial application. PMID:28348455
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Holtfreter, Silva; Kolata, Julia; Stentzel, Sebastian; Bauerfeind, Stephanie; Schmidt, Frank; Sundaramoorthy, Nandakumar; Bröker, Barbara M.
2016-01-01
Staphylococcus aureus is a dangerous pathogen both in hospitals and in the community. Due to the crisis of antibiotic resistance, there is an urgent need for new strategies to combat S. aureus infections, such as vaccination. Increasing our knowledge about the mechanisms of protection will be key for the successful prevention or treatment of S. aureus invasion. Omics technologies generate a comprehensive picture of the physiological and pathophysiological processes within cells, tissues, organs, organisms and even populations. This review provides an overview of the contribution of genomics, transcriptomics, proteomics, metabolomics and immunoproteomics to the current understanding of S. aureus‑host interaction, with a focus on the adaptive immune response to the microorganism. While antibody responses during colonization and infection have been analyzed in detail using immunoproteomics, the full potential of omics technologies has not been tapped yet in terms of T-cells. Omics technologies promise to speed up vaccine development by enabling reverse vaccinology approaches. In consequence, omics technologies are powerful tools for deepening our understanding of the “superbug” S. aureus and for improving its control. PMID:28248221
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[Applications of meta-analysis in multi-omics].
Han, Mingfei; Zhu, Yunping
2014-07-01
As a statistical method integrating multi-features and multi-data, meta-analysis was introduced to the field of life science in the 1990s. With the rapid advances in high-throughput technologies, life omics, the core of which are genomics, transcriptomics and proteomics, is becoming the new hot spot of life science. Although the fast output of massive data has promoted the development of omics study, it results in excessive data that are difficult to integrate systematically. In this case, meta-analysis is frequently applied to analyze different types of data and is improved continuously. Here, we first summarize the representative meta-analysis methods systematically, and then study the current applications of meta-analysis in various omics fields, finally we discuss the still-existing problems and the future development of meta-analysis.
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Leveraging algal omics to reveal potential targets for augmenting TAG accumulation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Arora, Neha; Pienkos, Philip T.; Pruthi, Vikas
Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. Here, this review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and informmore » future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less
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Leveraging Algal Omics to Reveal Potential Targets for Augmenting TAG Accumulation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Guarnieri, Michael T; Pienkos, Philip T; Arora, Neha
2018-04-18
Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform futuremore » metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less
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Resource recovery from wastewater: application of meta-omics to phosphorus and carbon management.
Sales, Christopher M; Lee, Patrick K H
2015-06-01
A growing trend at wastewater treatment plants is the recovery of resources and energy from wastewater. Enhanced biological phosphorus removal and anaerobic digestion are two established biotechnology approaches for the recovery of phosphorus and carbon, respectively. Meta-omics approaches (meta-genomics, transcriptomics, proteomics, and metabolomics) are providing novel biological insights into these complex biological systems. In particular, genome-centric metagenomics analyses are revealing the function and physiology of individual community members. Querying transcripts, proteins and metabolites are emerging techniques that can inform the cellular responses under different conditions. Overall, meta-omics approaches are shedding light into complex microbial communities once regarded as 'blackboxes', but challenges remain to integrate information from meta-omics into engineering design and operation guidelines. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Leveraging algal omics to reveal potential targets for augmenting TAG accumulation
Arora, Neha; Pienkos, Philip T.; Pruthi, Vikas; ...
2018-04-18
Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. Here, this review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and informmore » future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less
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Leveraging algal omics to reveal potential targets for augmenting TAG accumulation.
Arora, Neha; Pienkos, Philip T; Pruthi, Vikas; Poluri, Krishna Mohan; Guarnieri, Michael T
2018-04-18
Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy. Copyright © 2018. Published by Elsevier Inc.
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GeneLab: Open Science For Exploration
NASA Technical Reports Server (NTRS)
Galazka, Jonathan
2018-01-01
The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. The GeneLab project houses spaceflight and spaceflight-relevant multi-omics data in a publicly accessible data commons, and collaborates with NASA-funded principal investigators to maximize the omics data from spaceflight and spaceflight-relevant experiments. I will discuss the current status of GeneLab and give specific examples of how the GeneLab data system has been used to gain insight into how biology responds to spaceflight conditions.
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Multi-omics analysis has grown in popularity among biomedical researchers given the comprehensive characterization of thousands of molecular attributes in addition to clinical attributes. Several data portals have been devised to make these datasets directly available to the cancer research community. However, none of the existing data portals allow systematic exploration and interpretation of the complex relationships between the vast amount of clinical and molecular attributes. CPTAC investigator Dr.
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Molecular profiling--a tool for addressing emerging gaps in the comparative risk assessment of GMOs.
Heinemann, Jack A; Kurenbach, Brigitta; Quist, David
2011-10-01
Assessing the risks of genetically modified organisms (GMOs) is required by both international agreement and domestic legislation. Many view the use of the "omics" tools for profiling classes of molecules as useful in risk assessment, but no consensus has formed on the need or value of these techniques for assessing the risks of all GMOs. In this and many other cases, experts support case-by-case use of molecular profiling techniques for risk assessment. We review the latest research on the applicability and usefulness of molecular profiling techniques for GMO risk assessment. As more and more kinds of GMOs and traits are developed, broader use of molecular profiling in a risk assessment may be required to supplement the comparative approach to risk assessment. The literature-based discussions on the use of profiling appear to have settled on two findings: 1. profiling techniques are reliable and relevant, at least no less so than other techniques used in risk assessment; and 2. although not required routinely, regulators should be aware of when they are needed. The dismissal of routine molecular profiling may be confusing to regulators who then lack guidance on when molecular profiling might be worthwhile. Molecular profiling is an important way to increase confidence in risk assessments if the profiles are properly designed to address relevant risks and are applied at the correct stage of the assessment. Copyright © 2011 Elsevier Ltd. All rights reserved.
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A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kuo, Wen-Lin; Das, Debopriya; Ziyad, Safiyyah
2009-11-14
Polyamines regulate important cellular functions and polyamine dysregulation frequently occurs in cancer. The objective of this study was to use a systems approach to study the relative effects of PG-11047, a polyamine analogue, across breast cancer cells derived from different patients and to identify genetic markers associated with differential cytotoxicity. A panel of 48 breast cell lines that mirror many transcriptional and genomic features present in primary human breast tumours were used to study the antiproliferative activity of PG-11047. Sensitive cell lines were further examined for cell cycle distribution and apoptotic response. Cell line responses, quantified by the GI50 (dosemore » required for 50% relative growth inhibition) were correlated with the omic profiles of the cell lines to identify markers that predict response and cellular functions associated with drug sensitivity. The concentrations of PG-11047 needed to inhibit growth of members of the panel of breast cell lines varied over a wide range, with basal-like cell lines being inhibited at lower concentrations than the luminal cell lines. Sensitive cell lines showed a significant decrease in S phase fraction at doses that produced little apoptosis. Correlation of the GI50 values with the omic profiles of the cell lines identified genomic, transcriptional and proteomic variables associated with response. A 13-gene transcriptional marker set was developed as a predictor of response to PG-11047 that warrants clinical evaluation. Analyses of the pathways, networks and genes associated with response to PG-11047 suggest that response may be influenced by interferon signaling and differential inhibition of aspects of motility and epithelial to mesenchymal transition.« less
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Global metabolite profiling analysis of lipotoxicity in HER2/neu-positive breast cancer cells.
Baumann, Jan; Kokabee, Mostafa; Wong, Jason; Balasubramaniyam, Rakshika; Sun, Yan; Conklin, Douglas S
2018-06-05
Recent work has shown that HER2/neu-positive breast cancer cells rely on a unique Warburg-like metabolism for survival and aggressive behavior. These cells are dependent on fatty acid (FA) synthesis, show markedly increased levels of stored fats and disruption of the synthetic process results in apoptosis. In this study, we used global metabolite profiling and a multi-omics network analysis approach to model the metabolic changes in this physiology under palmitate-supplemented growth conditions to gain insights into the molecular mechanism and its relevance to disease prevention and treatment. Computational analyses were used to define pathway enrichment based on the dataset of significantly altered metabolites and to integrate metabolomics and transcriptomics data in a multi-omics network analysis. Network-predicted changes and functional relationships were tested with cell assays in vitro . Palmitate-supplemented growth conditions induce distinct metabolic alterations. Growth of HER2-normal MCF7 cells is unaffected under these conditions whereas HER2/neu-positive cells display unchanged neutral lipid content, AMPK activation, inhibition of fatty acid synthesis and significantly altered glutamine, glucose and serine/glycine metabolism. The predominant upregulated lipid species is the novel bioactive lipid N-palmitoylglycine, which is non-toxic to these cells. Limiting the availability of glutamine significantly ameliorates the lipotoxic effects of palmitate, reduces CHOP and XBP1(s) induction and restores the expression levels of HER2 and HER3. The study shows that HER2/neu-positive breast cancer cells change their metabolic phenotype in the presence of palmitate. Palmitate induces AMPK activation and inhibition of fatty acid synthesis that feeds back into glycolysis as well as anaplerotic glutamine metabolism.
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Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V
2017-04-27
Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.
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Path to Personalized Medicine for Type 2 Diabetes Mellitus: Reality and Hope.
Aghaei Meybodi, Hamid Reza; Hasanzad, Mandana; Larijani, Bagher
2017-03-01
Type 2 diabetes mellitus (T2DM) is recognized as a public health problem and increasingly prevalent illness. Key elements of the guideline for diabetes care are based on evidence-based medicine approach and apply for population, not individuals. However, individualized care can improve diabetes management. Personalized medicine is otherwise called precision medicine tries to find better prediction, prevention, and intervention for T2DM individuals. Precision medicine in diabetes refers to the utility of genomics data of a patient with diabetes to provide the most effective diagnosis strategies and treatment plans. Over 100 genetic loci influence susceptibility to T2DM. Genomics data together with the potential of other "Omics" and clinical evidence-based data will lead to diabetes care improvement in the context of personalized medicine in the near future. Breakthrough of technologies enables much greater improvements in the understanding of individual variations that may alter the T2DM outcome. This article represents a comprehensive review of current knowledge on the impact of personalized medicine in T2DM.
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Kolker, Eugene; Sansone, Susanna
2018-01-15
The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Eugene Kolker from Seattle Children's Hospital briefly discusses "The Encyclopedia of Systems Biology and OMICS," followed by Susanna Sansone from the EBI on "The ISA Infrastructure for multi-omics data" at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, CA. on Sept. 11, 2009.
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Kebschull, Moritz; Fittler, Melanie Julia; Demmer, Ryan T; Papapanou, Panos N
2017-01-01
Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences.In this chapter, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data generated using microarrays or next-generation sequencing technology using open-source tools. Starting with quality control measures and necessary preprocessing steps for data originating from different -omics technologies, we next outline a differential expression analysis pipeline that can be used for data from both microarray and sequencing experiments, and offers the possibility to account for random or fixed effects. Finally, we present an overview of the possibilities for a functional analysis of the obtained data.
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Rebollar, Eria A; Antwis, Rachael E; Becker, Matthew H; Belden, Lisa K; Bletz, Molly C; Brucker, Robert M; Harrison, Xavier A; Hughey, Myra C; Kueneman, Jordan G; Loudon, Andrew H; McKenzie, Valerie; Medina, Daniel; Minbiole, Kevin P C; Rollins-Smith, Louise A; Walke, Jenifer B; Weiss, Sophie; Woodhams, Douglas C; Harris, Reid N
2016-01-01
Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.
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An inference method from multi-layered structure of biomedical data.
Kim, Myungjun; Nam, Yonghyun; Shin, Hyunjung
2017-05-18
Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.
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Precision medicine in acute myeloid leukemia: Hope, hype or both?
Prasad, Vinay; Gale, Robert Peter
2016-09-01
Precision medicine is interchangeably used with personalized medicine, genomic medicine and individualized medicine. Collectively, these terms refer to at least 5 distinct concepts in the context of AML. 1st, using molecular or omics data (e.g. genomics, epigenomics, transcriptomics, proteomics) to delineate or define subtypes of AML. 2nd, using these data to select the best therapy for someone with an AML subtype, such as a person with a FLT3-mutation. 3rd, using these data to monitor therapy-response such as measurable residual disease [MRD]-testing. 4th, using results of MRD-testing to select from amongst therapy-options such as additional chemotherapy or a haematopoietic cell transplant. And 5th, using these data to identify persons with hereditary forms of AML with potential therapy and surveillance implications. Here, we review these 5 conceptions and delineate where precision medicine is likely to afford greatest hope and where instead our rhetoric may constitute hype. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Nikolova, Olga; Moser, Russell; Kemp, Christopher; Gönen, Mehmet; Margolin, Adam A
2017-05-01
In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas. : The code for this work is available at https://github.com/olganikolova/gbgfa. : nikolova@ohsu.edu or margolin@ohsu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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Kel, AlexanderE
2017-02-01
Computational analysis of master regulators through the search for transcription factor binding sites followed by analysis of signal transduction networks of a cell is a new approach of causal analysis of multi-omics data. This paper contains results on analysis of multi-omics data that include transcriptomics, proteomics and epigenomics data of methotrexate (MTX) resistant colon cancer cell line. The data were used for analysis of mechanisms of resistance and for prediction of potential drug targets and promising compounds for reverting the MTX resistance of these cancer cells. We present all results of the analysis including the lists of identified transcription factors and their binding sites in genome and the list of predicted master regulators - potential drug targets. This data was generated in the study recently published in the article "Multi-omics "Upstream Analysis" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer" (Kel et al., 2016) [4]. These data are of interest for researchers from the field of multi-omics data analysis and for biologists who are interested in identification of novel drug targets against NTX resistance.
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Shao, Yaping; Ye, Guozhu; Ren, Shancheng; Piao, Hai-Long; Zhao, Xinjie; Lu, Xin; Wang, Fubo; Ma, Wang; Li, Jia; Yin, Peiyuan; Xia, Tian; Xu, Chuanliang; Yu, Jane J; Sun, Yinghao; Xu, Guowang
2018-07-15
Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies. © 2018 UICC.
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2015-01-01
Background microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. Methods In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. Results Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. Conclusions Our prediction models have strong potential for the diagnosis of pancreatic cancer. PMID:26328610
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"Omics" in Human Colostrum and Mature Milk: Looking to Old Data with New Eyes.
Bardanzellu, Flaminia; Fanos, Vassilios; Reali, Alessandra
2017-08-07
Human Milk (HM) is the best source for newborn nutrition until at least six months; it exerts anti-inflammatory and anti-infective functions, promotes immune system formation and supports organ development. Breastfeeding could also protect from obesity, diabetes and cardiovascular disease. Furthermore, human colostrum (HC) presents a peculiar role in newborn support as a protective effect against allergic and chronic diseases, in addition to long-term metabolic benefits. In this review, we discuss the recent literature regarding "omics" technologies and growth factors (GF) in HC and the effects of pasteurization on its composition. Our aim was to provide new evidence in terms of transcriptomics, proteomics, metabolomics, and microbiomics, also in relation to maternal metabolic diseases and/or fetal anomalies and to underline the functions of GF. Since HC results are so precious, particularly for the vulnerable pre-terms category, we also discuss the importance of HM pasteurization to ensure donated HC even to neonates whose mothers are unable to provide. To the best of our knowledge, this is the first review analyzing in detail the molecular pattern, microbiota, bioactive factors, and dynamic profile of HC, finding clinical correlations of such mediators with their possible in vivo effects and with the consequent impact on neonatal outcomes.
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Recent progress in the use of ‘omics technologies in brassicaceous vegetables
Witzel, Katja; Neugart, Susanne; Ruppel, Silke; Schreiner, Monika; Wiesner, Melanie; Baldermann, Susanne
2015-01-01
Continuing advances in ‘omics methodologies and instrumentation is enhancing the understanding of how plants cope with the dynamic nature of their growing environment. ‘Omics platforms have been only recently extended to cover horticultural crop species. Many of the most widely cultivated vegetable crops belong to the genus Brassica: these include plants grown for their root (turnip, rutabaga/swede), their swollen stem base (kohlrabi), their leaves (cabbage, kale, pak choi) and their inflorescence (cauliflower, broccoli). Characterization at the genome, transcript, protein and metabolite levels has illustrated the complexity of the cellular response to a whole series of environmental stresses, including nutrient deficiency, pathogen attack, heavy metal toxicity, cold acclimation, and excessive and sub-optimal irradiation. This review covers recent applications of ‘omics technologies to the brassicaceous vegetables, and discusses future scenarios in achieving improvements in crop end-use quality. PMID:25926843
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Misra, Namrata; Panda, Prasanna Kumar; Parida, Bikram Kumar
2013-11-01
Microalgal biofuels offer great promise in contributing to the growing global demand for alternative sources of renewable energy. However, to make algae-based fuels cost competitive with petroleum, lipid production capabilities of microalgae need to improve substantially. Recent progress in algal genomics, in conjunction with other "omic" approaches, has accelerated the ability to identify metabolic pathways and genes that are potential targets in the development of genetically engineered microalgal strains with optimum lipid content. In this review, we summarize the current bioeconomic status of global biofuel feedstocks with particular reference to the role of "omics" in optimizing sustainable biofuel production. We also provide an overview of the various databases and bioinformatics resources available to gain a more complete understanding of lipid metabolism across algal species, along with the recent contributions of "omic" approaches in the metabolic pathway studies for microalgal biofuel production.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Kolker, Eugene; Sansone, Susanna
2011-09-11
The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Eugene Kolker from Seattle Children's Hospital briefly discusses "The Encyclopedia of Systems Biology and OMICS," followed by Susanna Sansone from the EBI on "Themore » ISA Infrastructure for multi-omics data" at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, CA. on Sept. 11, 2009.« less
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Integration, warehousing, and analysis strategies of Omics data.
Gedela, Srinubabu
2011-01-01
"-Omics" is a current suffix for numerous types of large-scale biological data generation procedures, which naturally demand the development of novel algorithms for data storage and analysis. With next generation genome sequencing burgeoning, it is pivotal to decipher a coding site on the genome, a gene's function, and information on transcripts next to the pure availability of sequence information. To explore a genome and downstream molecular processes, we need umpteen results at the various levels of cellular organization by utilizing different experimental designs, data analysis strategies and methodologies. Here comes the need for controlled vocabularies and data integration to annotate, store, and update the flow of experimental data. This chapter explores key methodologies to merge Omics data by semantic data carriers, discusses controlled vocabularies as eXtensible Markup Languages (XML), and provides practical guidance, databases, and software links supporting the integration of Omics data.
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Modelling plankton ecosystems in the meta-omics era. Are we ready?
Stec, Krzysztof Franciszek; Caputi, Luigi; Buttigieg, Pier Luigi; D'Alelio, Domenico; Ibarbalz, Federico Matias; Sullivan, Matthew B; Chaffron, Samuel; Bowler, Chris; Ribera d'Alcalà, Maurizio; Iudicone, Daniele
2017-04-01
Recent progress in applying meta-omics approaches to the study of marine ecosystems potentially allows scientists to study the genetic and functional diversity of plankton at an unprecedented depth and with enhanced precision. However, while a range of persistent technical issues still need to be resolved, a much greater obstacle currently preventing a complete and integrated view of the marine ecosystem is the absence of a clear conceptual framework. Herein, we discuss the knowledge that has thus far been derived from conceptual and statistical modelling of marine plankton ecosystems, and illustrate the potential power of integrated meta-omics approaches in the field. We then propose the use of a semantic framework is necessary to support integrative ecological modelling in the meta-omics era, particularly when having to face the increased interdisciplinarity needed to address global issues related to climate change. Copyright © 2017. Published by Elsevier B.V.
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Mass Spectrometry-Based Metabolomics to Elucidate Functions in Marine Organisms and Ecosystems
Goulitquer, Sophie; Potin, Philippe; Tonon, Thierry
2012-01-01
Marine systems are very diverse and recognized as being sources of a wide range of biomolecules. This review provides an overview of metabolite profiling based on mass spectrometry (MS) approaches in marine organisms and their environments, focusing on recent advances in the field. We also point out some of the technical challenges that need to be overcome in order to increase applications of metabolomics in marine systems, including extraction of chemical compounds from different matrices and data management. Metabolites being important links between genotype and phenotype, we describe added value provided by integration of data from metabolite profiling with other layers of omics, as well as their importance for the development of systems biology approaches in marine systems to study several biological processes, and to analyze interactions between organisms within communities. The growing importance of MS-based metabolomics in chemical ecology studies in marine ecosystems is also illustrated. PMID:22690147
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Enabling individualized therapy through nanotechnology.
Sakamoto, Jason H; van de Ven, Anne L; Godin, Biana; Blanco, Elvin; Serda, Rita E; Grattoni, Alessandro; Ziemys, Arturas; Bouamrani, Ali; Hu, Tony; Ranganathan, Shivakumar I; De Rosa, Enrica; Martinez, Jonathan O; Smid, Christine A; Buchanan, Rachel M; Lee, Sei-Young; Srinivasan, Srimeenakshi; Landry, Matthew; Meyn, Anne; Tasciotti, Ennio; Liu, Xuewu; Decuzzi, Paolo; Ferrari, Mauro
2010-08-01
Individualized medicine is the healthcare strategy that rebukes the idiomatic dogma of 'losing sight of the forest for the trees'. We are entering a new era of healthcare where it is no longer acceptable to develop and market a drug that is effective for only 80% of the patient population. The emergence of "-omic" technologies (e.g. genomics, transcriptomics, proteomics, metabolomics) and advances in systems biology are magnifying the deficiencies of standardized therapy, which often provide little treatment latitude for accommodating patient physiologic idiosyncrasies. A personalized approach to medicine is not a novel concept. Ever since the scientific community began unraveling the mysteries of the genome, the promise of discarding generic treatment regimens in favor of patient-specific therapies became more feasible and realistic. One of the major scientific impediments of this movement towards personalized medicine has been the need for technological enablement. Nanotechnology is projected to play a critical role in patient-specific therapy; however, this transition will depend heavily upon the evolutionary development of a systems biology approach to clinical medicine based upon "-omic" technology analysis and integration. This manuscript provides a forward looking assessment of the promise of nanomedicine as it pertains to individualized medicine and establishes a technology "snapshot" of the current state of nano-based products over a vast array of clinical indications and range of patient specificity. Other issues such as market driven hurdles and regulatory compliance reform are anticipated to "self-correct" in accordance to scientific advancement and healthcare demand. These peripheral, non-scientific concerns are not addressed at length in this manuscript; however they do exist, and their impact to the paradigm shifting healthcare transformation towards individualized medicine will be critical for its success. Copyright 2010 Elsevier Ltd. All rights reserved.
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Rawle, Rachel A.; Hamerly, Timothy; Tripet, Brian P.; ...
2017-06-04
Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted ‘omics’ analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized usingmore » interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis–N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. In conclusion, this multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis–N. equitans association. This study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies.« less
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Kambouris, Manousos E; Manoussopoulos, Yiannis; Kantzanou, Maria; Velegraki, Aristea; Gaitanis, Georgios; Arabatzis, Michalis; Patrinos, George P
2018-01-01
Global Catastrophic Biological Risks (GCBRs) refer to biological events-natural, deliberate, and accidental-of a global and lasting impact. This challenges the life scientists to raise their game on two hitherto neglected innovation frontiers: a veritable "futures" thinking to "think the unthinkable," and "systems thinking" so as to see both the trees and the forest when it comes to GCBRs. This innovation analysis article outlines the promise of Omics systems science biotechnologies, for example, to deploy rapid fire diagnostics for health security crises at GCBR level, possibly involving neopathogens and/or incurring epidemics (e.g., severe acute respiratory syndrome [SARS] and Ebola) that collectively threaten the lives of global society and interdependent biological ecosystems. Moreover, Omics encourages thinking beyond immediacy and in long-term strategies for biopreparedness and response innovation when the timelines are aggressive and compressed in response to crises such as GCBRs, but also to non-global but surging, multiple threats occurring as successive, overlapping, or distinct events, rather than as distinct entities-a prospect enforcing a reboot in Bioresilience. We define Next-Generation Bioresilience as "a systems approach against natural, accidental and perpetrated GCBRs using Omics technologies, and a shift in mentality, whereby the systems approach is expanded to include multiple plausible futures and expose unchecked assumptions attendant to risks, beyond technological determinism." In sum, it is time to think about the realistic potential of Omics biotechnologies beyond clinical practice and precision medicine so as to harness the opportunities and address the uncertainties associated not only with GCBRs but also with other emerging Omics applications in health and society.
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Urinary metallomics as a novel biomarker discovery platform: Breast cancer as a case study.
Burton, Casey; Dan, Yongbo; Donovan, Ariel; Liu, Kun; Shi, Honglan; Ma, Yinfa; Bosnak, Cynthia P
2016-01-15
Urinary metallomics is presented here as a new "omics" approach that aims to facilitate personalized cancer screening and prevention by improving our understanding of urinary metals in disease. Twenty-two urinary metals were examined with inductively-coupled plasma-mass spectrometry in 138 women newly diagnosed with breast cancer and benign conditions. Urinary metals from spot urine samples were adjusted to renal dilution using urine specific gravity. Two urinary metals, copper (P-value=0.036) and lead (P-value=0.003), were significantly increased in the urine of breast cancer patients. A multivariate model that comprised copper, lead, and patient age afforded encouraging discriminatory power (AUC: 0.728, P-value<0.0005), while univariate models of copper (61.7% sensitivity, 50.0% specificity) and lead (76.6% sensitivity, 51.2% specificity) at optimized cutoff thresholds compared favorably with other breast cancer diagnostic modalities such as mammography. Correlations found among various metals suggested potential geographic and dietary influences on the urine metallome that warrant further investigation. This proof-of-concept work introduces urinary metallomics as a noninvasive, potentially transformative "omics" approach to early cancer detection. Urinary copper and lead have also been preliminarily identified as potential breast cancer biomarkers. Copyright © 2015 Elsevier B.V. All rights reserved.
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Genome Sequencing Technologies and Nursing: What Are the Roles of Nurses and Nurse Scientists?
Taylor, Jacquelyn Y.; Wright, Michelle L.; Hickey, Kathleen T.; Housman, David
2016-01-01
Background Advances in DNA sequencing technology have resulted in an abundance of personalized data with challenging clinical utility and meaning for clinicians. This wealth of data has potential to dramatically impact the quality of healthcare. Nurses are at the focal point in educating patients regarding relevant healthcare needs; therefore, an understanding of sequencing technology and utilizing these data are critical. Aim The objective of this paper is to explicate the role of nurses and nurse scientists as integral members of healthcare teams in improving understanding of DNA sequencing data and translational genomics for patients. Approach A history of the nurse role in newborn screening is used as an exemplar. Discussion This paper serves as an exemplar on how genome sequencing has been utilized in nursing science and incorporates linkages of other omics approaches used by nurses that are included in this special issue. This special issue showcased nurse scientists conducting multi-omic research from various methods, including targeted candidate genes, pharmacogenomics, proteomics, epigenomics and the microbiome. From this vantage point, we provide an overview of the roles of nurse scientists in genome sequencing research and provide recommendations for the best utilization of nurses and nurse scientists related to genome sequencing. PMID:28252579
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Grady, Sarah L; Malfatti, Stephanie A; Gunasekera, Thusitha S; Dalley, Brian K; Lyman, Matt G; Striebich, Richard C; Mayhew, Michael B; Zhou, Carol L; Ruiz, Oscar N; Dugan, Larry C
2017-04-28
Examination of complex biological systems has long been achieved through methodical investigation of the system's individual components. While informative, this strategy often leads to inappropriate conclusions about the system as a whole. With the advent of high-throughput "omic" technologies, however, researchers can now simultaneously analyze an entire system at the level of molecule (DNA, RNA, protein, metabolite) and process (transcription, translation, enzyme catalysis). This strategy reduces the likelihood of improper conclusions, provides a framework for elucidation of genotype-phenotype relationships, and brings finer resolution to comparative genomic experiments. Here, we apply a multi-omic approach to analyze the gene expression profiles of two closely related Pseudomonas aeruginosa strains grown in n-alkanes or glycerol. The environmental P. aeruginosa isolate ATCC 33988 consumed medium-length (C 10 -C 16 ) n-alkanes more rapidly than the laboratory strain PAO1, despite high genome sequence identity (average nucleotide identity >99%). Our data shows that ATCC 33988 induces a characteristic set of genes at the transcriptional, translational and post-translational levels during growth on alkanes, many of which differ from those expressed by PAO1. Of particular interest was the lack of expression from the rhl operon of the quorum sensing (QS) system, resulting in no measurable rhamnolipid production by ATCC 33988. Further examination showed that ATCC 33988 lacked the entire lasI/lasR arm of the QS response. Instead of promoting expression of QS genes, ATCC 33988 up-regulates a small subset of its genome, including operons responsible for specific alkaline proteases and sphingosine metabolism. This work represents the first time results from RNA-seq, microarray, ribosome footprinting, proteomics, and small molecule LC-MS experiments have been integrated to compare gene expression in bacteria. Together, these data provide insights as to why strain ATCC 33988 is better adapted for growth and survival on n-alkanes.
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Recent Advances in Prostate Cancer Treatment and Drug Discovery.
Nevedomskaya, Ekaterina; Baumgart, Simon J; Haendler, Bernard
2018-05-04
Novel drugs, drug sequences and combinations have improved the outcome of prostate cancer in recent years. The latest approvals include abiraterone acetate, enzalutamide and apalutamide which target androgen receptor (AR) signaling, radium-223 dichloride for reduction of bone metastases, sipuleucel-T immunotherapy and taxane-based chemotherapy. Adding abiraterone acetate to androgen deprivation therapy (ADT) in order to achieve complete androgen blockade has proven highly beneficial for treatment of locally advanced prostate cancer and metastatic hormone-sensitive prostate cancer (mHSPC). Also, ADT together with docetaxel treatment showed significant benefit in mHSPC. Ongoing clinical trials for different subgroups of prostate cancer patients include the evaluation of the second-generation AR antagonists enzalutamide, apalutamide and darolutamide, of inhibitors of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway, of inhibitors of DNA damage response, of targeted alpha therapy and of prostate-specific membrane antigen (PSMA) targeting approaches. Advanced clinical studies with immune checkpoint inhibitors have shown limited benefits in prostate cancer and more trials are needed to demonstrate efficacy. The identification of improved, personalized treatments will be much supported by the major progress recently made in the molecular characterization of early- and late-stage prostate cancer using “omics” technologies. This has already led to novel classifications of prostate tumors based on gene expression profiles and mutation status, and should greatly help in the choice of novel targeted therapies best tailored to the needs of patients.
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Reproducibility and Transparency of Omics Research - Impacts on Human Health Risk Assessment
Omics technologies are becoming more widely used in toxicology, necessitating their consideration in human health hazard and risk assessment programs. Today, risk assessors in the United States Environmental Protection Agency’s Integrated Risk Information System (IRIS) Toxicologi...
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Integrated omics analysis of specialized metabolism in medicinal plants.
Rai, Amit; Saito, Kazuki; Yamazaki, Mami
2017-05-01
Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.
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OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space.
Zhou, Guangyan; Xia, Jianguo
2018-06-07
Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.
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Neuroblastoma treatment in the post-genomic era.
Esposito, Maria Rosaria; Aveic, Sanja; Seydel, Anke; Tonini, Gian Paolo
2017-02-08
Neuroblastoma is an embryonic malignancy of early childhood originating from neural crest cells and showing heterogeneous biological, morphological, genetic and clinical characteristics. The correct stratification of neuroblastoma patients within risk groups (low, intermediate, high and ultra-high) is critical for the adequate treatment of the patients.High-throughput technologies in the Omics disciplines are leading to significant insights into the molecular pathogenesis of neuroblastoma. Nonetheless, further study of Omics data is necessary to better characterise neuroblastoma tumour biology. In the present review, we report an update of compounds that are used in preclinical tests and/or in Phase I-II trials for neuroblastoma. Furthermore, we recapitulate a number of compounds targeting proteins associated to neuroblastoma: MYCN (direct and indirect inhibitors) and downstream targets, Trk, ALK and its downstream signalling pathways. In particular, for the latter, given the frequency of ALK gene deregulation in neuroblastoma patients, we discuss on second-generation ALK inhibitors in preclinical or clinical phases developed for the treatment of neuroblastoma patients resistant to crizotinib.We summarise how Omics drive clinical trials for neuroblastoma treatment and how much the research of biological targets is useful for personalised medicine. Finally, we give an overview of the most recent druggable targets selected by Omics investigation and discuss how the Omics results can provide us additional advantages for overcoming tumour drug resistance.
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Wang, Peng; Chen, Zhen
2013-09-01
Traditional Chinese medicine (TCM) is a comprehensive system of medical practice that has been used to diagnose, treat, and prevent illnesses for more than 3000 years. ZHENG (also known as "syndrome") differentiation remains the essence of TCM. In China, TCM shares equal status, and integrated with Western medicine in the healthcare system to treat many types of diseases. Yet, compared to biomolecular science and Western medicine, the ZHENG/TCM approach to diagnostics might appear unobjective, but offers at the same time long-standing clinical and phenotypic-rich insights. With the current globalization of life sciences and the arrival of "Big Data" research and development, these two silos of medical lore are rapidly coalescing. The applications of multi-omics strategies to TCM have begun to provide novel insights into the essence and molecular basis of TCM ZHENG. We searched the Chinese electronic databases and PubMed for published articles related to "Omics" and "TCM ZHENG" and observed a dramatic increase in studies over the past few years. In this article, we provide a timely synthesis of the lessons learned, and the emerging applications of omics science in TCM ZHENG research. We suggest that the global health scholarship and the field of "developing world Omics" can usefully draw from TCM, and vice versa.
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'Omic' genetic technologies for herbal medicines in psychiatry.
Sarris, Jerome; Ng, Chee Hong; Schweitzer, Isaac
2012-04-01
The field of genetics, which includes the use of 'omic' technologies, is an evolving area of science that has emerging application in phytotherapy. Omic studies include pharmacogenomics, proteomics and metabolomics. Herbal medicines, as monotherapies, or complex formulations such as traditional Chinese herbal prescriptions, may benefit from omic studies, and this new field may be termed 'herbomics'. Applying herbomics in the field of psychiatry may provide answers about which herbal interventions may be effective for individuals, which genetic processes are triggered, and the subsequent neurochemical pathways of activity. The use of proteomic technology can explore the differing epigenetic effects on neurochemical gene expression between individual herbs, isolated constituents and complex formulae. The possibilities of side effects or insufficient response to the herb can also be assessed via pharmacogenomic analysis of polymorphisms of cytochrome P450 liver enzymes or P-glycoprotein. While another novel application of omic technology is for the validation of the concept of synergy in individual herbal extracts and prescriptive formulations. Chronic administration of psychotropic herbal medicines may discover important effects on chromatin remodelling via modification of histone and DNA methylation. This paper focuses on the emerging field of herbomics, and is to our knowledge the first publication to explore this in the area of psychiatry. Copyright © 2011 John Wiley & Sons, Ltd.
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Systems biology for nursing in the era of big data and precision health.
Founds, Sandra
2017-12-02
The systems biology framework was previously synthesized with the person-environment-health-nursing metaparadigm. The purpose of this paper is to present a nursing discipline-specific perspective of the association of systems biology with big data and precision health. The fields of systems biology, big data, and precision health are now overviewed, from origins through expansions, with examples of what is being done by nurses in each area of science. Technological advances continue to expand omics and other varieties of big data that inform the person's phenotype and health outcomes for precision care. Meanwhile, millions of participants in the United States are being recruited for health-care research initiatives aimed at building the information commons of digital health data. Implications and opportunities abound via conceptualizing the integration of these fields through the nursing metaparadigm. Copyright © 2017 Elsevier Inc. All rights reserved.
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[New perspectives in monitoring of exposures to carcinogens].
Pavanello, Sofia; Lotti, Marcello
2011-01-01
Biomonitoring occupational and environmental exposures to carcinogens is a common practice and several biomarkers have been developed for risk assessment. However, in particular, because of the lack of prospective studies, the place of these biomarkers within the complex scenario of the gene-environment interactions leading to cancer cannot be defined. New opportunities and suggestions for biomonitoring exposures to carcinogens could derive from exploring the exposome, from the results of genomewide association and omic studies. Based on these premises it is possible to envisage personalized biomonitoring procedures, as those already actuated in nutrition and clinical oncology, allowing a better predictivity of biomarkers in the preventive settings.
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Target identification of small molecules based on chemical biology approaches.
Futamura, Yushi; Muroi, Makoto; Osada, Hiroyuki
2013-05-01
Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.
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The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment.
Brockmeier, Erica K; Hodges, Geoff; Hutchinson, Thomas H; Butler, Emma; Hecker, Markus; Tollefsen, Knut Erik; Garcia-Reyero, Natalia; Kille, Peter; Becker, Dörthe; Chipman, Kevin; Colbourne, John; Collette, Timothy W; Cossins, Andrew; Cronin, Mark; Graystock, Peter; Gutsell, Steve; Knapen, Dries; Katsiadaki, Ioanna; Lange, Anke; Marshall, Stuart; Owen, Stewart F; Perkins, Edward J; Plaistow, Stewart; Schroeder, Anthony; Taylor, Daisy; Viant, Mark; Ankley, Gerald; Falciani, Francesco
2017-08-01
In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology.
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The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment
Brockmeier, Erica K.; Hodges, Geoff; Hutchinson, Thomas H.; Butler, Emma; Hecker, Markus; Tollefsen, Knut Erik; Garcia-Reyero, Natalia; Kille, Peter; Becker, Dörthe; Chipman, Kevin; Colbourne, John; Collette, Timothy W.; Cossins, Andrew; Cronin, Mark; Graystock, Peter; Gutsell, Steve; Knapen, Dries; Katsiadaki, Ioanna; Lange, Anke; Marshall, Stuart; Owen, Stewart F.; Perkins, Edward J.; Plaistow, Stewart; Schroeder, Anthony; Taylor, Daisy; Viant, Mark; Ankley, Gerald; Falciani, Francesco
2017-01-01
Abstract In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework. PMID:28525648
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ERIC Educational Resources Information Center
Stupperich, Alexandra; Ihm, Helga; Strack, Micha
2009-01-01
Concerning the discussion about the connection of personality traits, personality disorders, and mental illness, this study focused on the personality profiles of male forensic patients, prison inmates, and young men without criminal reports. The main topic centered on group-specific personality profiles and identifying personality facets…
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Omics-based biomarkers: current status and potential use in the clinic.
Quezada, Héctor; Guzmán-Ortiz, Ana Laura; Díaz-Sánchez, Hugo; Valle-Rios, Ricardo; Aguirre-Hernández, Jesús
In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. Copyright © 2017 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.
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Wiggins, Robert E; Gold, Robert S; Menke, Anne M
2015-12-01
To summarize the claims statistics of the Ophthalmic Mutual Insurance Company (OMIC) in the field of pediatric ophthalmology and strabismus (POS). Internal OMIC case summaries and defense counsel case evaluations of all claims in the field of POS closed between December 1, 1988, and February 19, 2013 were retrospectively analyzed. A total of 140 claims were closed over the 25-year study period, of which 44 were closed with an indemnity payment. Claims related to strabismus and retinopathy of prematurity (ROP) were most common, and claims related to ROP resulted in the highest indemnity and expense payments. Issues related to follow-up represented the most significant risk factor among system-related claims. Claims in pediatric ophthalmology and strabismus were infrequent but associated with three times higher average indemnity payments relative to all claims paid by OMIC during the course of the study. Copyright © 2015 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.
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The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies
White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.; ...
2017-05-06
Over the past century, the significance of the rhizosphere has been increasingly recognized by the scientific community. Furthermore, this complex biological system is comprised of vast interconnected networks of microbial organisms that interact directly with their plant hosts, including archaea, bacteria, fungi, picoeukaryotes, and viruses. The rhizosphere provides a nutritional base to the terrestrial biosphere, and is integral to plant growth, crop production, and ecosystem health. There is little mechanistic understanding of the rhizosphere, but, and that constitutes a critical knowledge gap. It inhibits our ability to predict and control the terrestrial ecosystem to achieve desirable outcomes, such as bioenergymore » production, crop yield maximization, and soil-based carbon sequestration. Multi-omics have the potential to significantly advance our knowledge of rhizospheric science. Our review covers multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and the challenges to be addressed during this century of rhizospheric science.« less
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The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies
DOE Office of Scientific and Technical Information (OSTI.GOV)
White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.
Over the past century, the significance of the rhizosphere has been increasingly recognized by the scientific community. Furthermore, this complex biological system is comprised of vast interconnected networks of microbial organisms that interact directly with their plant hosts, including archaea, bacteria, fungi, picoeukaryotes, and viruses. The rhizosphere provides a nutritional base to the terrestrial biosphere, and is integral to plant growth, crop production, and ecosystem health. There is little mechanistic understanding of the rhizosphere, but, and that constitutes a critical knowledge gap. It inhibits our ability to predict and control the terrestrial ecosystem to achieve desirable outcomes, such as bioenergymore » production, crop yield maximization, and soil-based carbon sequestration. Multi-omics have the potential to significantly advance our knowledge of rhizospheric science. Our review covers multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and the challenges to be addressed during this century of rhizospheric science.« less
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The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies
DOE Office of Scientific and Technical Information (OSTI.GOV)
White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.
Over the past century, the significance of the rhizosphere as a complex, biological system, comprised of vast, interconnected networks of microbial organisms that interact directly with their plant hosts (e.g., archæa, bacteria, fungi, eukaryotes, and viruses) has been increasingly recognized by the scientific community. Providing a nutritional base to the terrestrial biosphere, the rhizosphere is integral to plant growth, crop production and ecosystem health. Lack of mechanistic understanding of the rhizosphere constitutes a critical knowledge gap, inhibiting our ability to predict and control the terrestrial ecosystem in order to achieve desirable outcomes (e.g., bioenergy production, crop yield maximization, and soilbasedmore » carbon sequestration). Application of multi-omics has the potential to significantly advance our knowledge of rhizospheric science. This review covers: cutting- and bleeding-edge, multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and, challenges to be addressed during this century of rhizospheric science.« less
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Eley, Diann S; Leung, Janni; Hong, Barry A; Cloninger, Kevin M; Cloninger, C Robert
2016-01-01
There is a high prevalence of stress, depression, and burn-out in medical students. Medical students differ widely in personality traits, self-perceptions, and values that may have an impact on their well-being. This study aimed to investigate variability in their personality profiles in relation to their potential for well-being and resilience. Participants were 808 medical students from The University of Queensland. An online questionnaire collected socio-demographics and the Temperament and Character Inventory to assess personality traits. Latent profile analyses identified students' trait profiles. Two distinct personality profiles were identified. Profile 1 ("Resilient") characterized 60% of the sample and was distinguished by low Harm Avoidance combined with very high Persistence, Self-Directedness and Cooperativeness compared to Profile 2 ("Conscientious"). Both Profiles had average levels of Reward Dependence and Novelty Seeking and low levels of Self-Transcendence. Profiles did not differ by age, gender, or country of birth, but rural background students were more likely to have Profile 1. While both Profiles indicate mature and healthy personalities, the combination of traits in Profile 1 is more strongly indicative of well-being and resilience. Finding two distinct profiles of personality highlights the importance of considering combinations of traits and how they may interact with medical students' potential for well-being. Although both profiles of students show healthy personalities, many may lack the resilience to maintain well-being over years of medical training. Programs that develop character and personality self-awareness would enhance their well-being and prepare them to promote the health of their patients.
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Leung, Janni; Hong, Barry A.; Cloninger, Kevin M.; Cloninger, C. Robert
2016-01-01
Purpose There is a high prevalence of stress, depression, and burn-out in medical students. Medical students differ widely in personality traits, self-perceptions, and values that may have an impact on their well-being. This study aimed to investigate variability in their personality profiles in relation to their potential for well-being and resilience. Method Participants were 808 medical students from The University of Queensland. An online questionnaire collected socio-demographics and the Temperament and Character Inventory to assess personality traits. Latent profile analyses identified students’ trait profiles. Results Two distinct personality profiles were identified. Profile 1 (“Resilient”) characterized 60% of the sample and was distinguished by low Harm Avoidance combined with very high Persistence, Self-Directedness and Cooperativeness compared to Profile 2 ("Conscientious"). Both Profiles had average levels of Reward Dependence and Novelty Seeking and low levels of Self-Transcendence. Profiles did not differ by age, gender, or country of birth, but rural background students were more likely to have Profile 1. While both Profiles indicate mature and healthy personalities, the combination of traits in Profile 1 is more strongly indicative of well-being and resilience. Conclusions Finding two distinct profiles of personality highlights the importance of considering combinations of traits and how they may interact with medical students’ potential for well-being. Although both profiles of students show healthy personalities, many may lack the resilience to maintain well-being over years of medical training. Programs that develop character and personality self-awareness would enhance their well-being and prepare them to promote the health of their patients. PMID:27494401
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Influenza-Omics and the Host Response: Recent Advances and Future Prospects
Powell, Joshua D.; Waters, Katrina M.
2017-01-01
Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. The various-omics infection systems that include but are not limited to ferrets, mice, pigs, and even the controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infection outcomes. PMID:28604586
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Influenza-Omics and the Host Response: Recent Advances and Future Prospects
DOE Office of Scientific and Technical Information (OSTI.GOV)
Powell, Joshua D.; Waters, Katrina M.
Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. Here, the various –omics infection systems that include but are not limited to ferrets, mice, pigs and even controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infectionmore » outcomes.« less
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Mildew-Omics: How Global Analyses Aid the Understanding of Life and Evolution of Powdery Mildews.
Bindschedler, Laurence V; Panstruga, Ralph; Spanu, Pietro D
2016-01-01
The common powdery mildew plant diseases are caused by ascomycete fungi of the order Erysiphales. Their characteristic life style as obligate biotrophs renders functional analyses in these species challenging, mainly because of experimental constraints to genetic manipulation. Global large-scale ("-omics") approaches are thus particularly valuable and insightful for the characterisation of the life and evolution of powdery mildews. Here we review the knowledge obtained so far from genomic, transcriptomic and proteomic studies in these fungi. We consider current limitations and challenges regarding these surveys and provide an outlook on desired future investigations on the basis of the various -omics technologies.
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Influenza-Omics and the Host Response: Recent Advances and Future Prospects
Powell, Joshua D.; Waters, Katrina M.
2017-06-10
Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. Here, the various –omics infection systems that include but are not limited to ferrets, mice, pigs and even controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infectionmore » outcomes.« less
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Smith, Todd
2017-12-22
Todd Smith of the PerkinElmer Omics Laboratory gives a talk about his lab and its work at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.
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In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the UK Natural Environment Research Council (NERC) with an objective of moving omic technologies into chemical risk assessment and environmental monitoring. Objectiv...
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Background: In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the UK Natural Environment Research Council (NERC) with a remit of moving omic technologies into chemical risk assessment and environmental monitoring. Obj...
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Smith, Todd
2012-06-01
Todd Smith of the PerkinElmer Omics Laboratory gives a talk about his lab and its work at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.
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Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T
2015-01-01
MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.
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Zeng, Irene Sui Lan; Lumley, Thomas
2018-01-01
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
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Computational dynamic approaches for temporal omics data with applications to systems medicine.
Liang, Yulan; Kelemen, Arpad
2017-01-01
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
BIRKEL, GARRETT; GARCIA MARTIN, HECTOR; MORRELL, WILLIAM
"Arrowland" is a web-based software application primarily for mapping, integrating and visualizing a variety of metabolism data of living organisms, including but not limited to metabolomics, proteomics, transcriptomics and fluxomics. This software application makes multi-omics data analysis intuitive and interactive. It improves data sharing and communication by enabling users to visualize their omics data using a web browser (on a PC or mobile device). It increases user's productivity by simplifying multi-omics data analysis using well developed maps as a guide. Users using this tool can gain insights into their data sets that would be difficult or even impossible to teasemore » out by looking at raw number, or using their currently existing toolchains to generate static single-use maps. Arrowland helps users save time by visualizing relative changes in different conditions or over time, and helps users to produce more significant insights faster. Preexisting maps decrease the learning curve for beginners in the omics field. Sets of multi-omics data are presented in the browser, as a two-dimensional flowchart resembling a map, with varying levels of detail information, based on the scaling of the map. Users can pan and zoom to explore different maps, compare maps, upload their own research data sets onto desired maps, alter map appearance in ways that facilitate interpretation, visualization and analysis of the given data, and export data, reports and actionable items to help the user initiative.« less
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Van Dijk, Fiona E; Mostert, Jeannette; Glennon, Jeffrey; Onnink, Marten; Dammers, Janneke; Vasquez, Alejandro Arias; Kan, Cornelis; Verkes, Robbert Jan; Hoogman, Martine; Franke, Barbara; Buitelaar, Jan K
2017-12-01
Deficits in multiple neuropsychological domains and specific personality profiles have been observed in attention-deficit/hyperactivity disorder (ADHD). In this study we investigated whether personality traits are related to neurocognitive profiles in adults with ADHD. Neuropsychological performance and Five Factor Model (FFM) personality traits were measured in adults with ADHD (n = 133) and healthy controls (n = 132). Three neuropsychological profiles, derived from previous community detection analyses, were investigated for personality trait differences. Irrespective of cognitive profile, participants with ADHD showed significantly higher Neuroticism and lower Extraversion, Agreeableness, and Conscientiousness than healthy controls. Only the FFM personality factor Openness differed significantly between the three profiles. Higher Openness was more common in those with aberrant attention and inhibition than those with increased delay discounting and atypical working memory / verbal fluency. The results suggest that the personality trait Openness, but not any other FFM factor, is linked to neurocognitive profiles in ADHD. ADHD symptoms rather than profiles of cognitive impairment have associations with personality traits. Copyright © 2017 Elsevier B.V. All rights reserved.
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The role of omics in the application of adverse outcome pathways for chemical risk assessment
In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (UK) in September 2014, we held a workshop to bring together toxicology and regulatory science experts from academia, government and industry. The pur...
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Takai-Igarashi, Takako; Kinoshita, Kengo; Nagasaki, Masao; Ogishima, Soichi; Nakamura, Naoki; Nagase, Sachiko; Nagaie, Satoshi; Saito, Tomo; Nagami, Fuji; Minegishi, Naoko; Suzuki, Yoichi; Suzuki, Kichiya; Hashizume, Hiroaki; Kuriyama, Shinichi; Hozawa, Atsushi; Yaegashi, Nobuo; Kure, Shigeo; Tamiya, Gen; Kawaguchi, Yoshio; Tanaka, Hiroshi; Yamamoto, Masayuki
2017-07-06
With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement. Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis. We established the T ohoku M edical M egabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections. The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.
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C. elegans network biology: a beginning.
Piano, Fabio; Gunsalus, Kristin C; Hill, David E; Vidal, Marc
2006-01-01
The architecture and dynamics of molecular networks can provide an understanding of complex biological processes complementary to that obtained from the in-depth study of single genes and proteins. With a completely sequenced and well-annotated genome, a fully characterized cell lineage, and powerful tools available to dissect development, Caenorhabditis elegans, among metazoans, provides an optimal system to bridge cellular and organismal biology with the global properties of macromolecular networks. This chapter considers omic technologies available for C. elegans to describe molecular networks--encompassing transcriptional and phenotypic profiling as well as physical interaction mapping--and discusses how their individual and integrated applications are paving the way for a network-level understanding of C. elegans biology. PMID:18050437
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Trespassing cancer cells: 'fingerprinting' invasive protrusions reveals metastatic culprits.
Klemke, Richard L
2012-10-01
Metastatic cancer cells produce invasive membrane protrusions called invadopodia and pseudopodia, which play a central role in driving cancer cell dissemination in the body. Malignant cells use these structures to attach to and degrade extracellular matrix proteins, generate force for cell locomotion, and to penetrate the vasculature. Recent work using unique subcellular fractionation methodologies combined with spatial genomic, proteomic, and phosphoproteomic profiling has provided insight into the invadopodiome and pseudopodiome signaling networks that control the protrusion of invasive membranes. Here I highlight how these powerful spatial 'omics' approaches reveal important signatures of metastatic cancer cells and possible new therapeutic targets aimed at treating metastatic disease. Copyright © 2012 Elsevier Ltd. All rights reserved.
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[The application of metabonomics in modern studies of Chinese materia medica].
Chen, Hai-Bin; Zhou, Hong-Guang; Yu, Xiao-Yi
2012-06-01
Metabonomics, a newly developing subject secondary to genomics, transcriptomics, and proteomics, is an important constituent part of systems biology. It is believed to be the final direction of the systems biology. It can be directly applied to understand the physiological and biochemical states by its "metabolome profile" as a whole. Therefore, it can provide a huge amount of information different from those originating from other "omics". In the modernization of Chinese materia medica research, the application of metabonomics methods and technologies has a broad potential for future development. Especially it is of important theoretical significance and application value in holistic efficacies evaluation, active ingredients studies, and safety research of Chinese materia medica.
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Breeding approaches and genomics technologies to increase crop yield under low-temperature stress.
Jha, Uday Chand; Bohra, Abhishek; Jha, Rintu
2017-01-01
Improved knowledge about plant cold stress tolerance offered by modern omics technologies will greatly inform future crop improvement strategies that aim to breed cultivars yielding substantially high under low-temperature conditions. Alarmingly rising temperature extremities present a substantial impediment to the projected target of 70% more food production by 2050. Low-temperature (LT) stress severely constrains crop production worldwide, thereby demanding an urgent yet sustainable solution. Considerable research progress has been achieved on this front. Here, we review the crucial cellular and metabolic alterations in plants that follow LT stress along with the signal transduction and the regulatory network describing the plant cold tolerance. The significance of plant genetic resources to expand the genetic base of breeding programmes with regard to cold tolerance is highlighted. Also, the genetic architecture of cold tolerance trait as elucidated by conventional QTL mapping and genome-wide association mapping is described. Further, global expression profiling techniques including RNA-Seq along with diverse omics platforms are briefly discussed to better understand the underlying mechanism and prioritize the candidate gene (s) for downstream applications. These latest additions to breeders' toolbox hold immense potential to support plant breeding schemes that seek development of LT-tolerant cultivars. High-yielding cultivars endowed with greater cold tolerance are urgently required to sustain the crop yield under conditions severely challenged by low-temperature.
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ICM: a web server for integrated clustering of multi-dimensional biomedical data.
He, Song; He, Haochen; Xu, Wenjian; Huang, Xin; Jiang, Shuai; Li, Fei; He, Fuchu; Bo, Xiaochen
2016-07-08
Large-scale efforts for parallel acquisition of multi-omics profiling continue to generate extensive amounts of multi-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multi-dimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Urich, Tim; Lanzén, Anders; Stokke, Runar; Pedersen, Rolf B; Bayer, Christoph; Thorseth, Ingunn H; Schleper, Christa; Steen, Ida H; Ovreas, Lise
2014-09-01
Deep-sea hydrothermal vents are unique environments on Earth, as they host chemosynthetic ecosystems fuelled by geochemical energy with chemolithoautotrophic microorganisms at the basis of the food webs. Whereas discrete high-temperature venting systems have been studied extensively, the microbiotas associated with low-temperature diffuse venting are not well understood. We analysed the structure and functioning of microbial communities in two diffuse venting sediments from the Jan Mayen vent fields in the Norwegian-Greenland Sea, applying an integrated 'omics' approach combining metatranscriptomics, metaproteomics and metagenomics. Polymerase chain reaction-independent three-domain community profiling showed that the two sediments hosted highly similar communities dominated by Epsilonproteobacteria, Deltaproteobacteria and Gammaproteobacteria, besides ciliates, nematodes and various archaeal taxa. Active metabolic pathways were identified through transcripts and peptides, with genes of sulphur and methane oxidation, and carbon fixation pathways highly expressed, in addition to genes of aerobic and anaerobic (nitrate and sulphate) respiratory chains. High expression of chemotaxis and flagella genes reflected a lifestyle in a dynamic habitat rich in physico-chemical gradients. The major metabolic pathways could be assigned to distinct taxonomic groups, thus enabling hypotheses about the function of the different prokaryotic and eukaryotic taxa. This study advances our understanding of the functioning of microbial communities in diffuse hydrothermal venting sediments. © 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.
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NASA Astrophysics Data System (ADS)
Tfaily, M. M.; Walker, L. R.; Kyle, J. E.; Chu, R. K.; Dohnalkova, A.; Tolic, N.; Orton, D.; Robinson, E. R.; Paša-Tolić, L.; Hess, N. J.
2015-12-01
The focus on soil C dynamics is currently relevant as researchers and policymakers strive to understand the feedbacks between ecosystem stress and climate change. Successful development of molecular profiles that link soil microbiology with soil carbon (C) dynamics to ascertain soil vulnerability and resilience to climate change would have great impact on assessments of soil ecosystems in response to climate change. Additionally, a better understanding of the soil C dynamics would improve climate modeling, and fate and transport of carbon across terrestrial, subsurface and atmospheric interfaces. Unravelling the wide range of possible interactions between and within the microbial communities, with minerals and organic compounds in the terrestrial ecosystem requires a multimodal, molecular approach. Here we report on the use of a combination of several molecular 'omics' approaches: metabolomics, metallomics, lipidomics, and proteomics coupled with a suite of high resolution imaging, and X-ray diffraction crystallographic techniques, as a novel methodology to understand SOM composition, and its interaction with microbial communities in different ecosystems, including C associated with mineral surfaces. The findings of these studies provide insights into the SOM persistence and microbial stabilization of carbon in ecosystems and reveal the powerful coupling of a multi-scale of techniques. Examples of this approach will be presented from field studies of simulated climate change, and laboratory column-grown Pinus resinosa mesocosms.
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Vijayakumar, Vinod; Liebisch, Gerhard; Buer, Benjamin; Xue, Li; Gerlach, Nina; Blau, Samira; Schmitz, Jessica; Bucher, Marcel
2016-02-01
Interaction of plant roots with arbuscular mycorrhizal fungi (AMF) is a complex trait resulting in cooperative interactions among the two symbionts including bidirectional exchange of resources. To study arbuscular mycorrhizal symbiosis (AMS) trait variation in the model plant Lotus japonicus, we performed an integrated multi-omics analysis with a focus on plant and fungal phospholipid (PL) metabolism and biological significance of lysophosphatidylcholine (LPC). Our results support the role of LPC as a bioactive compound eliciting cellular and molecular response mechanisms in Lotus. Evidence is provided for large interspecific chemical diversity of LPC species among mycorrhizae with related AMF species. Lipid, gene expression and elemental profiling emphasize the Lotus-Glomus intraradices interaction as distinct from other arbuscular mycorrhizal (AM) interactions. In G. intraradices, genes involved in fatty acid (FA) elongation and biosynthesis of unsaturated FAs were enhanced, while in Lotus, FA synthesis genes were up-regulated during AMS. Furthermore, FAS protein localization to mitochondria suggests FA biosynthesis and elongation may also occur in AMF. Our results suggest the existence of interspecific partitioning of PL resources for generation of LPC and novel candidate bioactive PLs in the Lotus-G. intraradices symbiosis. Moreover, the data advocate research with phylogenetically diverse Glomeromycota species for a broader understanding of the molecular underpinnings of AMS. © 2015 John Wiley & Sons Ltd.
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Weijer, Ruud; Clavier, Séverine; Zaal, Esther A; Pijls, Maud M E; van Kooten, Robert T; Vermaas, Klaas; Leen, René; Jongejan, Aldo; Moerland, Perry D; van Kampen, Antoine H C; van Kuilenburg, André B P; Berkers, Celia R; Lemeer, Simone; Heger, Michal
2017-03-01
Photodynamic therapy (PDT) is an established palliative treatment for perihilar cholangiocarcinoma that is clinically promising. However, tumors tend to regrow after PDT, which may result from the PDT-induced activation of survival pathways in sublethally afflicted tumor cells. In this study, tumor-comprising cells (i.e., vascular endothelial cells, macrophages, perihilar cholangiocarcinoma cells, and EGFR-overexpressing epidermoid cancer cells) were treated with the photosensitizer zinc phthalocyanine that was encapsulated in cationic liposomes (ZPCLs). The post-PDT survival pathways and metabolism were studied following sublethal (LC 50 ) and supralethal (LC 90 ) PDT. Sublethal PDT induced survival signaling in perihilar cholangiocarcinoma (SK-ChA-1) cells via mainly HIF-1-, NF-кB-, AP-1-, and heat shock factor (HSF)-mediated pathways. In contrast, supralethal PDT damage was associated with a dampened survival response. PDT-subjected SK-ChA-1 cells downregulated proteins associated with EGFR signaling, particularly at LC 90 . PDT also affected various components of glycolysis and the tricarboxylic acid cycle as well as metabolites involved in redox signaling. In conclusion, sublethal PDT activates multiple pathways in tumor-associated cell types that transcriptionally regulate cell survival, proliferation, energy metabolism, detoxification, inflammation/angiogenesis, and metastasis. Accordingly, tumor cells sublethally afflicted by PDT are a major therapeutic culprit. Our multi-omic analysis further unveiled multiple druggable targets for pharmacological co-intervention.
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Predicting Biological Information Flow in a Model Oxygen Minimum Zone
NASA Astrophysics Data System (ADS)
Louca, S.; Hawley, A. K.; Katsev, S.; Beltran, M. T.; Bhatia, M. P.; Michiels, C.; Capelle, D.; Lavik, G.; Doebeli, M.; Crowe, S.; Hallam, S. J.
2016-02-01
Microbial activity drives marine biochemical fluxes and nutrient cycling at global scales. Geochemical measurements as well as molecular techniques such as metagenomics, metatranscriptomics and metaproteomics provide great insight into microbial activity. However, an integration of molecular and geochemical data into mechanistic biogeochemical models is still lacking. Recent work suggests that microbial metabolic pathways are, at the ecosystem level, strongly shaped by stoichiometric and energetic constraints. Hence, models rooted in fluxes of matter and energy may yield a holistic understanding of biogeochemistry. Furthermore, such pathway-centric models would allow a direct consolidation with meta'omic data. Here we present a pathway-centric biogeochemical model for the seasonal oxygen minimum zone in Saanich Inlet, a fjord off the coast of Vancouver Island. The model considers key dissimilatory nitrogen and sulfur fluxes, as well as the population dynamics of the genes that mediate them. By assuming a direct translation of biocatalyzed energy fluxes to biosynthesis rates, we make predictions about the distribution and activity of the corresponding genes. A comparison of the model to molecular measurements indicates that the model explains observed DNA, RNA, protein and cell depth profiles. This suggests that microbial activity in marine ecosystems such as oxygen minimum zones is well described by DNA abundance, which, in conjunction with geochemical constraints, determines pathway expression and process rates. Our work further demonstrates how meta'omic data can be mechanistically linked to environmental redox conditions and biogeochemical processes.
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Stability of personality traits in schizophrenia and schizoaffective disorder: a pilot project.
Kentros, M; Smith, T E; Hull, J; McKee, M; Terkelsen, K; Capalbo, C
1997-09-01
This study was performed in an effort to begin characterization of personality traits in schizophrenia. Specific concerns included personality profiles relative to normal adults, personality profile stability over time, and trait-state issues. The authors administered the NEO Personality Inventory as well as symptom ratings at two time points to 21 patients. Patients were all stabilized outpatients attending an adult continuing day treatment program and diagnosed with either schizophrenia or schizoaffective disorder. Personality profiles were determined for all patients. Compared with a normal adult sample, this sample's scores on three out of five of the personality domains assessed were not distinguishable from normal adults. Test-retest correlations were highly significant over an average 28.2-week time interval. In general, the presence of positive symptoms did not appear related to NEO-PI stability, while negative symptoms did show a relationship to the stability of personality profiles. These data suggest that personality profiles can be looked at in schizophrenia, that these profiles do appear stable over time, and that negative symptoms have a strong influence on profile stability and appear to be "trait-like."
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The personality context of relational aggression: A Five-Factor Model profile analysis.
Reardon, Kathleen W; Tackett, Jennifer L; Lynam, Don
2018-05-01
Relational aggression (RAgg) is a form of behavior intended to damage the victim's social status or interpersonal relationships through the use of purposeful interpersonal manipulation or social exclusion (Archer & Coyne, 2005). RAgg is impairing, stable, and largely defined by dysfunctional patterns of interpersonal interactions-all of which invokes comparisons to personality and, more specifically, personality pathology. Leveraging research using the Five Factor Model (FFM) in personality disorder (PD) work, the present study aims to understand the personality context of RAgg by applying this FFM profile approach in 2 ways: (a) by compiling a personality profile of RAgg based on a thorough review of the relevant literature and (b) by compiling a personality profile of RAgg based on expert ratings (N = 19). We then compared these profiles to each other and to existing personality profiles of Cluster B PDs to examine how RAgg fits into the personality space represented by Cluster B PDs. These analyses indicate that both FFM profiles of RAgg show substantial overlap with the FFM profile of narcissistic PD. The present study has important implications for bridging disjointed domains of research on personality pathology and RAgg and underscores the relevance of RAgg for early emergence of PD characteristics. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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An overview on forensic analysis devoted to analytical chemists.
Castillo-Peinado, L S; Luque de Castro, M D
2017-05-15
The present article has as main aim to show analytical chemists interested in forensic analysis the world they will face if decision in favor of being a forensic analytical chemist is adopted. With this purpose, the most outstanding aspects of forensic analysis in dealing with sampling (involving both bodily and no bodily samples), sample preparation, and analytical equipment used in detection, identification and quantitation of key sample components are critically discussed. The role of the great omics in forensic analysis, and the growing role of the youngest of the great omics -metabolomics- are also discussed. The foreseeable role of integrative omics is also outlined. Copyright © 2017 Elsevier B.V. All rights reserved.
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[Clinical value evaluation of Chinese herbal formula in context of multi-omics network].
Li, Bing; Han, Fei; Wang, Zhong; Wang, Yong-Yan
2017-03-01
Clinical value evaluation is the key issue to solve the problems such as high repetition rate, fuzzy clinical positioning, broad indications and unclear clinical values in Chinese herbal formula(Chinese patent medicine). By analyzing the challenges and opportunities of Chinese herbal formula in clinical value evaluation, this paper introduced a strategy of multi-omic network analysis. Through comparative analysis of three stroke treatment formulas, we suggested their different characteristic advantages for variant symptoms or phenotypes of stroke, which may provide reference for rational clinical choice. Such multi-omic network analysis strategy may open a unique angle of view for clinical evaluation and comparison of Chinese herbal formula. Copyright© by the Chinese Pharmaceutical Association.
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The OMICS of Sports & Space: How Genomics is Transforming Both Fields
NASA Technical Reports Server (NTRS)
Reeves, Katherine
2016-01-01
Join top 10 New York Times Bestseller “The Sports Gene” author David Epstein and NASA Twins Study investigator Christopher E. Mason, Ph.D., in the debate as old as physical competition—nature versus nurture. From personal experience, Epstein tackles the great debate and traces how far science has come in solving this timeless riddle, and how genetics has entered into the field of sports. He’s an investigative science reporter for ProPublica and longtime contributor to Sports Illustrated. Epstein will share insights into performance-enhancing drugs, the lucky genetics that separate a professional athlete from a less talented athlete, and his research into the death of a friend with Hypertrophic Cardiomyopathy (HCM).From an epigenomic viewpoint, Mason examines the benefits and risks for astronauts who face extreme spaceflight conditions and what it means for the future of human space travel. He is an associate professor in the Department of Physiology and Biophysics, The Feil Family Brain and Mind Research Institute (BMRI) & The Institute for Computational Biomedicine at Weill Cornell Medicine. He is also part of the Tri-Institutional Program on Computational Biology and a Medicine Fellow of Genomics, Ethics, and Law in the Information Society Project at Yale Law School.The study of omics shows tremendous potential in prevention, diagnosis and treatment of injuries and diseases but genetic discrimination and molecular privacy concerns are raised in both sports and space.
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Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades
2013-01-01
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.
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Standardization and structural annotation of public toxicity databases: Improving SAR capabilities and linkage to 'omics data
Ann M. Richard', ClarLynda Williams', Jamie Burch2
'Nat Health & Environ Res Lab, US EPA, RTP, NC 27711; 2EPA/NC Central Univ Student COOP Trainee<... -
Perspectives of Physiology as a Discipline from Senior-Level Millennial-Generation Students
ERIC Educational Resources Information Center
Steury, Michael D.; Poteracki, James M.; Kelly, Kevin L.; Wehrwein, Erica A.
2015-01-01
In the last several decades, there has been a shift in the mindset of research structure from classical "systems or integrative biology" to more molecular focused "-omics" study. A recent topic of debate in physiological societies has been whether or not the "-omic" revolution has delivered in its promises in both…
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Chadeau-Hyam, Marc; Campanella, Gianluca; Jombart, Thibaut; Bottolo, Leonardo; Portengen, Lutzen; Vineis, Paolo; Liquet, Benoit; Vermeulen, Roel C H
2013-08-01
Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets. Copyright © 2013 Wiley Periodicals, Inc.
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A correlated meta-analysis strategy for data mining "OMIC" scans.
Province, Michael A; Borecki, Ingrid B
2013-01-01
Meta-analysis is becoming an increasingly popular and powerful tool to integrate findings across studies and OMIC dimensions. But there is the danger that hidden dependencies between putatively "independent" studies can cause inflation of type I error, due to reinforcement of the evidence from false-positive findings. We present here a simple method for conducting meta-analyses that automatically estimates the degree of any such non-independence between OMIC scans and corrects the inference for it, retaining the proper type I error structure. The method does not require the original data from the source studies, but operates only on summary analysis results from these in OMIC scans. The method is applicable in a wide variety of situations including combining GWAS and or sequencing scan results across studies with dependencies due to overlapping subjects, as well as to scans of correlated traits, in a meta-analysis scan for pleiotropic genetic effects. The method correctly detects which scans are actually independent in which case it yields the traditional meta-analysis, so it may safely be used in all cases, when there is even a suspicion of correlation amongst scans.
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From data to knowledge: The future of multi-omics data analysis for the rhizosphere
Allen White III, Richard; Borkum, Mark I.; Rivas-Ubach, Albert; ...
2017-05-04
The rhizosphere is the interface between the root system of a plant and its surrounding soil. The microbiome of the rhizosphere, which is the totality of all microbes present there, represents a complex microbial ecosystem that nourishes the terrestrial biosphere. In order to untangle the complexity of the rhizosphere, and of the rhizospheric microbiome in particular, an integrated multi-omics approach can be applied to reveal the composition of the rhizospheric microbiome (through 16S ribosomal amplicons and metagenomics), the functional properties of the microbiome (through metatranscriptomics and metaproteomics), and the signaling network within the rhizosphere (through metametabolomics). The successful application ofmore » integrated multi-omics to rhizospheric science depends on the availability of rhizosphere-specific data and on the appropriate software used to analyze omics data from the rhizosphere. Here, we analyze the availability of software suites that are normally applied to surrogate disciplines (e.g., soil and plants) but which can be used for rhizospheric science. We also identify potential issues, challenges, and opportunities for rhizosphere science.« less
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Suárez-Ulloa, Victoria; Fernández-Tajes, Juan; Manfrin, Chiara; Gerdol, Marco; Venier, Paola; Eirín-López, José M.
2013-01-01
The extraordinary progress experienced by sequencing technologies and bioinformatics has made the development of omic studies virtually ubiquitous in all fields of life sciences nowadays. However, scientific attention has been quite unevenly distributed throughout the different branches of the tree of life, leaving molluscs, one of the most diverse animal groups, relatively unexplored and without representation within the narrow collection of well established model organisms. Within this Phylum, bivalve molluscs play a fundamental role in the functioning of the marine ecosystem, constitute very valuable commercial resources in aquaculture, and have been widely used as sentinel organisms in the biomonitoring of marine pollution. Yet, it has only been very recently that this complex group of organisms became a preferential subject for omic studies, posing new challenges for their integrative characterization. The present contribution aims to give a detailed insight into the state of the art of the omic studies and functional information analysis of bivalve molluscs, providing a timely perspective on the available data resources and on the current and prospective applications for the biomonitoring of harmful marine compounds. PMID:24189277
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RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis.
Glaab, Enrico; Schneider, Reinhard
2015-07-01
High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics. Freely available at http://www.repexplore.tk enrico.glaab@uni.lu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.
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Deciphering functional diversification within the lichen microbiota by meta-omics.
Cernava, Tomislav; Erlacher, Armin; Aschenbrenner, Ines Aline; Krug, Lisa; Lassek, Christian; Riedel, Katharina; Grube, Martin; Berg, Gabriele
2017-07-19
Recent evidence of specific bacterial communities extended the traditional concept of fungal-algal lichen symbioses by a further organismal kingdom. Although functional roles were already assigned to dominant members of the highly diversified microbiota, a substantial fraction of the ubiquitous colonizers remained unexplored. We employed a multi-omics approach to further characterize functional guilds in an unconventional model system. The general community structure of the lichen-associated microbiota was shown to be highly similar irrespective of the employed omics approach. Five highly abundant bacterial orders-Sphingomonadales, Rhodospirillales, Myxococcales, Chthoniobacterales, and Sphingobacteriales-harbor functions that are of substantial importance for the holobiome. Identified functions range from the provision of vitamins and cofactors to the degradation of phenolic compounds like phenylpropanoid, xylenols, and cresols. Functions that facilitate the persistence of Lobaria pulmonaria under unfavorable conditions were present in previously overlooked fractions of the microbiota. So far, unrecognized groups like Chthoniobacterales (Verrucomicrobia) emerged as functional protectors in the lichen microbiome. By combining multi-omics and imaging techniques, we highlight previously overlooked participants in the complex microenvironment of the lichens.
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Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology.
Fondi, Marco; Liò, Pietro
2015-02-01
Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists. Copyright © 2015. Published by Elsevier GmbH.
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Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes
NASA Astrophysics Data System (ADS)
Hultman, Jenni; Waldrop, Mark P.; Mackelprang, Rachel; David, Maude M.; McFarland, Jack; Blazewicz, Steven J.; Harden, Jennifer; Turetsky, Merritt R.; McGuire, A. David; Shah, Manesh B.; Verberkmoes, Nathan C.; Lee, Lang Ho; Mavrommatis, Kostas; Jansson, Janet K.
2015-05-01
Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular `omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.
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Microarray platform for omics analysis
NASA Astrophysics Data System (ADS)
Mecklenburg, Michael; Xie, Bin
2001-09-01
Microarray technology has revolutionized genetic analysis. However, limitations in genome analysis has lead to renewed interest in establishing 'omic' strategies. As we enter the post-genomic era, new microarray technologies are needed to address these new classes of 'omic' targets, such as proteins, as well as lipids and carbohydrates. We have developed a microarray platform that combines self- assembling monolayers with the biotin-streptavidin system to provide a robust, versatile immobilization scheme. A hydrophobic film is patterned on the surface creating an array of tension wells that eliminates evaporation effects thereby reducing the shear stress to which biomolecules are exposed to during immobilization. The streptavidin linker layer makes it possible to adapt and/or develop microarray based assays using virtually any class of biomolecules including: carbohydrates, peptides, antibodies, receptors, as well as them ore traditional DNA based arrays. Our microarray technology is designed to furnish seamless compatibility across the various 'omic' platforms by providing a common blueprint for fabricating and analyzing arrays. The prototype microarray uses a microscope slide footprint patterned with 2 by 96 flat wells. Data on the microarray platform will be presented.
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High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.
Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo
2014-01-01
To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.
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Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes.
Hultman, Jenni; Waldrop, Mark P; Mackelprang, Rachel; David, Maude M; McFarland, Jack; Blazewicz, Steven J; Harden, Jennifer; Turetsky, Merritt R; McGuire, A David; Shah, Manesh B; VerBerkmoes, Nathan C; Lee, Lang Ho; Mavrommatis, Kostas; Jansson, Janet K
2015-05-14
Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular 'omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.
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Personality Profiles of Effective Leadership Performance in Assessment Centers.
Parr, Alissa D; Lanza, Stephanie T; Bernthal, Paul
2016-01-01
Most research examining the relationship between effective leadership and personality has focused on individual personality traits. However, profiles of personality traits more fully describe individuals, and these profiles may be important as they relate to leadership. This study used latent class analysis to examine how personality traits combine and interact to form subpopulations of leaders, and how these subpopulations relate to performance criteria. Using a sample of 2,461 executive-level leaders, six personality profiles were identified: Unpredictable Leaders with Low Diligence (7.3%); Conscientious, Backend Leaders (3.6%); Unpredictable Leaders (8.6%); Creative Communicators (20.8%); Power Players (32.4%); and Protocol Followers (27.1%). One profile performed well on all criteria in an assessment center; remaining profiles exhibited strengths and weaknesses across criteria. Implications and future directions for research are highlighted.
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Veldhoen, Nik; Ikonomou, Michael G; Helbing, Caren C
2012-02-01
Many species that contribute to the commercial and ecological richness of our marine ecosystems are harbingers of environmental change. The ability of organisms to rapidly detect and respond to changes in the surrounding environment represents the foundation for application of molecular profiling technologies towards marine sentinel species in an attempt to identify signature profiles that may reside within the transcriptome, proteome, or metabolome and that are indicative of a particular environmental exposure event. The current review highlights recent examples of the biological information obtained for marine sentinel teleosts, mammals, and invertebrates. While in its infancy, such basal information can provide a systems biology framework in the detection and evaluation of environmental chemical contaminant effects on marine fauna. Repeated evaluation across different seasons and local marine environs will lead to discrimination between signature profiles representing normal variation within the complex milieu of environmental factors that trigger biological response in a given sentinel species and permit a greater understanding of normal versus anthropogenic-associated modulation of biological pathways, which prove detrimental to marine fauna. It is anticipated that incorporation of contaminant-specific molecular signatures into current risk assessment paradigms will lead to enhanced wildlife management strategies that minimize the impacts of our industrialized society on marine ecosystems. Copyright © 2011 Elsevier Inc. All rights reserved.
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Advancing the use of noncoding RNA in regulatory toxicology: Report of an ECETOC workshop.
Aigner, Achim; Buesen, Roland; Gant, Tim; Gooderham, Nigel; Greim, Helmut; Hackermüller, Jörg; Hubesch, Bruno; Laffont, Madeleine; Marczylo, Emma; Meister, Gunter; Petrick, Jay S; Rasoulpour, Reza J; Sauer, Ursula G; Schmidt, Kerstin; Seitz, Hervé; Slack, Frank; Sukata, Tokuo; van der Vies, Saskia M; Verhaert, Jan; Witwer, Kenneth W; Poole, Alan
2016-12-01
The European Centre for the Ecotoxicology and Toxicology of Chemicals (ECETOC) organised a workshop to discuss the state-of-the-art research on noncoding RNAs (ncRNAs) as biomarkers in regulatory toxicology and as analytical and therapeutic agents. There was agreement that ncRNA expression profiling data requires careful evaluation to determine the utility of specific ncRNAs as biomarkers. To advance the use of ncRNA in regulatory toxicology, the following research priorities were identified: (1) Conduct comprehensive literature reviews to identify possibly suitable ncRNAs and areas of toxicology where ncRNA expression profiling could address prevailing scientific deficiencies. (2) Develop consensus on how to conduct ncRNA expression profiling in a toxicological context. (3) Conduct experimental projects, including, e.g., rat (90-day) oral toxicity studies, to evaluate the toxicological relevance of the expression profiles of selected ncRNAs. Thereby, physiological ncRNA expression profiles should be established, including the biological variability of healthy individuals. To substantiate the relevance of key ncRNAs for cell homeostasis or pathogenesis, molecular events should be dose-dependently linked with substance-induced apical effects. Applying a holistic approach, knowledge on ncRNAs, 'omics and epigenetics technologies should be integrated into adverse outcome pathways to improve the understanding of the functional roles of ncRNAs within a regulatory context. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.
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Morris, Alan H
2018-02-01
Our education system seems to fail to enable clinicians to broadly understand core physiological principles. The emphasis on reductionist science, including "omics" branches of research, has likely contributed to this decrease in understanding. Consequently, clinicians cannot be expected to consistently make clinical decisions linked to best physiological evidence. This is a large-scale problem with multiple determinants, within an even larger clinical decision problem: the failure of clinicians to consistently link their decisions to best evidence. Clinicians, like all human decision-makers, suffer from significant cognitive limitations. Detailed context-sensitive computer protocols can generate personalized medicine instructions that are well matched to individual patient needs over time and can partially resolve this problem.
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A System Biology Perspective on Environment-Host-Microbe Interactions.
Chen, Lianmin; Garmaeva, Sanzhima; Zherankova, Alexandra; Fu, Jingyuan; Wijmenga, Cisca
2018-04-16
A vast, complex and dynamic consortium of microorganisms known as the gut microbiome colonizes the human gut. Over the past few decades we have developed an increased awareness of its important role in human health. In this review we discuss the role of the gut microbiome in complex diseases and the possible causal scenarios behind its interactions with the host genome and environmental factors. We then propose a new analysis framework that combines a systems biology approach, cross-kingdom integration of multiple levels of omics data, and innovative in vitro models to yield an integrated picture of human host-microbe interactions. This new framework will lay the foundation for the development of the next phase in personalized medicine.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Rawle, Rachel A.; Hamerly, Timothy; Tripet, Brian P.
Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted ‘omics’ analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized usingmore » interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis–N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. In conclusion, this multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis–N. equitans association. This study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies.« less
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Studying Cellular Signal Transduction with OMIC Technologies.
Landry, Benjamin D; Clarke, David C; Lee, Michael J
2015-10-23
In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Reinhold, William C
2015-12-10
There is currently a split within the cancer research community between traditional molecular biological hypothesis-driven and the more recent "omic" forms or research. While the molecular biological approach employs the tried and true single alteration-single response formulations of experimentation, the omic employs broad-based assay or sample collection approaches that generate large volumes of data. How to integrate the benefits of these two approaches in an efficient and productive fashion remains an outstanding issue. Ideally, one would merge the understandability, exactness, simplicity, and testability of the molecular biological approach, with the larger amounts of data, simultaneous consideration of multiple alterations, consideration of genes both of known interest along with the novel, cross-sample comparisons among cell lines and patient samples, and consideration of directed questions while simultaneously gaining exposure to the novel provided by the omic approach. While at the current time integration of the two disciplines remains problematic, attempts to do so are ongoing, and will be necessary for the understanding of the large cell line screens including the Developmental Therapeutics Program's NCI-60, the Broad Institute's Cancer Cell Line Encyclopedia, and the Wellcome Trust Sanger Institute's Cancer Genome Project, as well as the the Cancer Genome Atlas clinical samples project. Going forward there is significant benefit to be had from the integration of the molecular biological and the omic forms or research, with the desired goal being improved translational understanding and application.
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Second Era of OMICS in Caries Research: Moving Past the Phase of Disillusionment.
Nascimento, M M; Zaura, E; Mira, A; Takahashi, N; Ten Cate, J M
2017-07-01
Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries. For example, the determination of complete bacterial microbiomes of oral samples rather than selected species, together with oral metatranscriptome and metabolome analyses, supports the viewpoint of dysbiosis of the supragingival biofilms. In addition, metabolome studies have been instrumental in disclosing the contributions of major pathways for central carbon and amino acid metabolisms to biofilm pH homeostasis. New, often noncultured, oral streptococci have been identified, and their phenotypic characterization has revealed candidates for probiotic therapy. Although findings from OMICS research have been greatly informative, problems related to study design, data quality, integration, and reproducibility still need to be addressed. Also, the emergence and continuous updates of these computationally demanding technologies require expertise in advanced bioinformatics for reliable interpretation of data. Despite the obstacles cited above, OMICS research is expected to encourage the discovery of novel caries biomarkers and the development of next-generation diagnostics and therapies for caries control. These observations apply equally to the study of other oral diseases.
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Sievert, Martin; Zwir, Igor; Cloninger, Kevin M.; Lester, Nigel; Rozsa, Sandor
2016-01-01
Background Multiple factors influence the decision to enter a career in medicine and choose a specialty. Previous studies have looked at personality differences in medicine but often were unable to describe the heterogeneity that exists within each specialty. Our study used a person-centered approach to characterize the complex relations between the personality profiles of resident physicians and their choice of specialty. Methods 169 resident physicians at a large Midwestern US training hospital completed the Temperament and Character Inventory (TCI) and the Satisfaction with Life Scale (SWLS). Clusters of personality profiles were identified without regard to medical specialty, and then the personality clusters were tested for association with their choice of specialty by co-clustering analysis. Life satisfaction was tested for association with personality traits and medical specialty by linear regression and analysis of variance. Results We identified five clusters of people with distinct personality profiles, and found that these were associated with particular medical specialties Physicians with an “investigative” personality profile often chose pathology or internal medicine, those with a “commanding” personality often chose general surgery, “rescuers” often chose emergency medicine, the “dependable” often chose pediatrics, and the “compassionate” often chose psychiatry. Life satisfaction scores were not enhanced by personality-specialty congruence, but were related strongly to self-directedness regardless of specialty. Conclusions The personality profiles of physicians were strongly associated with their medical specialty choices. Nevertheless, the relationships were complex: physicians with each personality profile went into a variety of medical specialties, and physicians in each medical specialty had variable personality profiles. The plasticity and resilience of physicians were more important for their life satisfaction than was matching personality to the prototype of a particular specialty. PMID:27651982
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Unsupervised multiple kernel learning for heterogeneous data integration.
Mariette, Jérôme; Villa-Vialaneix, Nathalie
2018-03-15
Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.
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Reconstructing targetable pathways in lung cancer by integrating diverse omics data
Balbin, O. Alejandro; Prensner, John R.; Sahu, Anirban; Yocum, Anastasia; Shankar, Sunita; Malik, Rohit; Fermin, Damian; Dhanasekaran, Saravana M.; Chandler, Benjamin; Thomas, Dafydd; Beer, David G.; Cao, Xuhong; Nesvizhskii, Alexey I.; Chinnaiyan, Arul M.
2014-01-01
Global ‘multi-omics’ profiling of cancer cells harbours the potential for characterizing the signaling networks associated with specific oncogenes. Here we profile the transcriptome, proteome and phosphoproteome in a panel of non-small cell lung cancer (NSCLC) cell lines in order to reconstruct targetable networks associated with KRAS dependency. We develop a two-step bioinformatics strategy addressing the challenge of integrating these disparate data sets. We first define an ‘abundance-score’ combining transcript, protein and phospho-protein abundances to nominate differentially abundant proteins and then use the Prize Collecting Steiner Tree algorithm to identify functional sub-networks. We identify three modules centered on KRAS and MET, LCK and PAK1 and b-Catenin. We validate activation of these proteins in KRAS-dependent (KRAS-Dep) cells and perform functional studies defining LCK as a critical gene for cell proliferation in KRAS-Dep but not KRAS-independent NSCLCs. These results suggest that LCK is a potential druggable target protein in KRAS-Dep lung cancers. PMID:24135919
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Carreno-Quintero, Natalia; Acharjee, Animesh; Maliepaard, Chris; Bachem, Christian W.B.; Mumm, Roland; Bouwmeester, Harro; Visser, Richard G.F.; Keurentjes, Joost J.B.
2012-01-01
Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solanum tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits. PMID:22223596
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El Karkouri, Khalid; Kowalczewska, Malgorzata; Armstrong, Nicholas; Azza, Said; Fournier, Pierre-Edouard; Raoult, Didier
2017-01-01
Arthropod-borne Rickettsia species are obligate intracellular bacteria which are pathogenic for humans. Within this genus, Rickettsia slovaca and Rickettsia conorii cause frequent and potentially severe infections, whereas Rickettsia raoultii and Rickettsia massiliae cause rare and milder infections. All four species belong to spotted fever group (SFG) rickettsiae. However, R. slovaca and R. raoultii cause scalp eschar and neck lymphadenopathy (SENLAT) and are mainly associated with Dermacentor ticks, whereas the other two species cause Mediterranean spotted fever (MSF) and are mainly transmitted by Rhipicephalus ticks. To identify the potential genes and protein profiles and to understand the evolutionary processes that could, comprehensively, relate to the differences in virulence and pathogenicity observed between these four species, we compared their genomes and proteomes. The virulent and milder agents displayed divergent phylogenomic evolution in two major clades, whereas either SENLAT or MSF disease suggests a discrete convergent evolution of one virulent and one milder agent, despite their distant genetic relatedness. Moreover, the two virulent species underwent strong reductive genomic evolution and protein structural variations, as well as a probable loss of plasmid(s), compared to the two milder species. However, an abundance of mobilome genes was observed only in the less pathogenic species. After infecting Xenopus laevis cells, the virulent agents displayed less up-regulated than down-regulated proteins, as well as less number of identified core proteins. Furthermore, their similar and distinct protein profiles did not contain some genes (e.g., omp A/B and rick A) known to be related to rickettsial adhesion, motility and/or virulence, but may include other putative virulence-, antivirulence-, and/or disease-related proteins. The identified evolutionary forces herein may have a strong impact on intracellular expressions and strategies in these rickettsiae, and that may contribute to the emergence of distinct virulence and diseases in humans. Thus, the current multi-omics data provide new insights into the evolution and fitness of SFG virulence and pathogenicity, and intracellular pathogenic bacteria.
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Multi-omics Evidence for Inheritance of Energy Pathways in Red Blood Cells.
Weisenhorn, Erin M M; van T Erve, Thomas J; Riley, Nicholas M; Hess, John R; Raife, Thomas J; Coon, Joshua J
2016-12-01
Each year over 90 million units of blood are transfused worldwide. Our dependence on this blood supply mandates optimized blood management and storage. During storage, red blood cells undergo degenerative processes resulting in altered metabolic characteristics which may make blood less viable for transfusion. However, not all stored blood spoils at the same rate, a difference that has been attributed to variable rates of energy usage and metabolism in red blood cells. Specific metabolite abundances are heritable traits; however, the link between heritability of energy metabolism and red blood cell storage profiles is unclear. Herein we performed a comprehensive metabolomics and proteomics study of red blood cells from 18 mono- and di-zygotic twin pairs to measure heritability and identify correlations with ATP and other molecular indices of energy metabolism. Without using affinity-based hemoglobin depletion, our work afforded the deepest multi-omic characterization of red blood cell membranes to date (1280 membrane proteins and 330 metabolites), with 119 membrane protein and 148 metabolite concentrations found to be over 30% heritable. We demonstrate a high degree of heritability in the concentration of energy metabolism metabolites, especially glycolytic metabolites. In addition to being heritable, proteins and metabolites involved in glycolysis and redox metabolism are highly correlated, suggesting that crucial energy metabolism pathways are inherited en bloc at distinct levels. We conclude that individuals can inherit a phenotype composed of higher or lower concentrations of these proteins together. This can result in vastly different red blood cells storage profiles which may need to be considered to develop precise and individualized storage options. Beyond guiding proper blood storage, this intimate link in heritability between energy and redox metabolism pathways may someday prove useful in determining the predisposition of an individual toward metabolic diseases. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.
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Polivka, Jiri; Polivka, Jiri; Karlikova, Marie; Topolcan, Ondrej
2014-01-01
The main goal of personalized medicine is the individualized approach to the patient's treatment. It could be achieved only by the integration of the complexity of novel findings in diverse "omics" disciplines, new methods of medical imaging, as well as implementation of reliable biomarkers into the medical care. The implementation of personalized medicine into clinical practice is dependent on the adaptation of pre-graduate and post-graduate medical education to these principles. The situation in the education of personalized medicine in the Czech Republic is analyzed together with novel educational tools that are currently established in our country. The EPMA representatives in the Czech Republic in cooperation with the working group of professionals at the Faculty of Medicine in Pilsen, Charles University in Prague have implemented the survey of personalized medicine awareness among students of Faculty of Medicine in Pilsen-the "Personalized Medicine Questionnaire". The results showed lacking knowledge of personalized medicine principles and students' will of education in this domain. Therefore, several educational activities addressed particularly to medical students and young physicians were realized at our facility with very positive evaluation. These educational activities (conferences, workshops, seminars, e-learning and special courses in personalized medicine (PM)) will be a part of pre-graduate and post-graduate medical education, will be extended to other medical faculties in our country. The "Summer School of Personalized Medicine in Plzen 2015" will be organized at the Faculty of Medicine and Faculty Hospital in Pilsen as the first event on this topic in the Czech Republic.
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Personality Profiles of Effective Leadership Performance in Assessment Centers
Parr, Alissa D.; Lanza, Stephanie T.; Bernthal, Paul
2016-01-01
Most research examining the relationship between effective leadership and personality has focused on individual personality traits. However, profiles of personality traits more fully describe individuals, and these profiles may be important as they relate to leadership. This study used latent class analysis to examine how personality traits combine and interact to form subpopulations of leaders, and how these subpopulations relate to performance criteria. Using a sample of 2,461 executive-level leaders, six personality profiles were identified: Unpredictable Leaders with Low Diligence (7.3%); Conscientious, Backend Leaders (3.6%); Unpredictable Leaders (8.6%); Creative Communicators (20.8%); Power Players (32.4%); and Protocol Followers (27.1%). One profile performed well on all criteria in an assessment center; remaining profiles exhibited strengths and weaknesses across criteria. Implications and future directions for research are highlighted. PMID:27746587
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Chapter 3: Omics and the Future of Sustainable Biomaterials
Juliet D. Tang; Susan V. Diehl
2014-01-01
With global focus on the conversion of biomass into products, fuels, and energy, there is a strong need for information that will lead to new sustainable products, applications, and biotechnological advances. The omics approach to biology is a discovery-driven method that may deliver solutions to these overarching problems. It gives scientists the ability to obtain a...
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"Predatory" Online Journals Lure Scholars Who Are Eager to Publish
ERIC Educational Resources Information Center
Stratford, Michael
2012-01-01
OMICS Publishing Group is an open-access publisher operating under an author-pays model. Unlike traditional journal subscriptions in which readers or institutions pay to read content, OMICS relies on its contributors for financial support. Although the author-pays model is not a new phenomenon in the realm of open access, its recent popularity has…
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Wullschleger, Stan
2018-02-13
Stan Wullschleger of Oak Ridge National Laboratory on "Omics in the Arctic: Genome-enabled Contributions to Carbon Cycle Research in High-Latitude Ecosystems" on March 22, 2012 at the 7th Annual Genomics of Energy & Environment Meeting in Walnut Creek, California.
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Twelkmeyer, Brigitte; Tardif, Nicolas; Rooyackers, Olav
2017-05-01
The purpose of this review is to recapture recent advances in cachexia-related diseases, mainly cancer cachexia, and treatment using genomic, transcriptomics, proteomic, and metabolomics-related techniques. From recent studies in the cancer cachexia field it is clear that the tumor has a direct effect on distant organs via its secretome. The affected pathways on the other hand were largely known from earlier studies with changes in energy-related pathways (mainly lipid metabolism) and the protein degradation pathways. Treatment-oriented studies use mostly rodent models and in-vivo cultures and it is too early for human studies. Omics tools are powerful if used in the right way. Omics research has identified the tumor as an important player in cancer cachexia and some interesting novel treatments have been found in experimental models.
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Functional Foods and Lifestyle Approaches for Diabetes Prevention and Management.
Alkhatib, Ahmad; Tsang, Catherine; Tiss, Ali; Bahorun, Theeshan; Arefanian, Hossein; Barake, Roula; Khadir, Abdelkrim; Tuomilehto, Jaakko
2017-12-01
Functional foods contain biologically active ingredients associated with physiological health benefits for preventing and managing chronic diseases, such as type 2 diabetes mellitus (T2DM). A regular consumption of functional foods may be associated with enhanced anti-oxidant, anti-inflammatory, insulin sensitivity, and anti-cholesterol functions, which are considered integral to prevent and manage T2DM. Components of the Mediterranean diet (MD)-such as fruits, vegetables, oily fish, olive oil, and tree nuts-serve as a model for functional foods based on their natural contents of nutraceuticals, including polyphenols, terpenoids, flavonoids, alkaloids, sterols, pigments, and unsaturated fatty acids. Polyphenols within MD and polyphenol-rich herbs-such as coffee, green tea, black tea, and yerba maté-have shown clinically-meaningful benefits on metabolic and microvascular activities, cholesterol and fasting glucose lowering, and anti-inflammation and anti-oxidation in high-risk and T2DM patients. However, combining exercise with functional food consumption can trigger and augment several metabolic and cardiovascular protective benefits, but it is under-investigated in people with T2DM and bariatric surgery patients. Detecting functional food benefits can now rely on an "omics" biological profiling of individuals' molecular, genetics, transcriptomics, proteomics, and metabolomics, but is under-investigated in multi-component interventions. A personalized approach for preventing and managing T2DM should consider biological and behavioral models, and embed nutrition education as part of lifestyle diabetes prevention studies. Functional foods may provide additional benefits in such an approach.
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Ferguson, Lynnette R; De Caterina, Raffaele; Görman, Ulf; Allayee, Hooman; Kohlmeier, Martin; Prasad, Chandan; Choi, Myung Sook; Curi, Rui; de Luis, Daniel Antonio; Gil, Ángel; Kang, Jing X; Martin, Ron L; Milagro, Fermin I; Nicoletti, Carolina Ferreira; Nonino, Carla Barbosa; Ordovas, Jose Maria; Parslow, Virginia R; Portillo, María P; Santos, José Luis; Serhan, Charles N; Simopoulos, Artemis P; Velázquez-Arellano, Antonio; Zulet, Maria Angeles; Martinez, J Alfredo
2016-01-01
Diversity in the genetic profile between individuals and specific ethnic groups affects nutrient requirements, metabolism and response to nutritional and dietary interventions. Indeed, individuals respond differently to lifestyle interventions (diet, physical activity, smoking, etc.). The sequencing of the human genome and subsequent increased knowledge regarding human genetic variation is contributing to the emergence of personalized nutrition. These advances in genetic science are raising numerous questions regarding the mode that precision nutrition can contribute solutions to emerging problems in public health, by reducing the risk and prevalence of nutrition-related diseases. Current views on personalized nutrition encompass omics technologies (nutrigenomics, transcriptomics, epigenomics, foodomics, metabolomics, metagenomics, etc.), functional food development and challenges related to legal and ethical aspects, application in clinical practice, and population scope, in terms of guidelines and epidemiological factors. In this context, precision nutrition can be considered as occurring at three levels: (1) conventional nutrition based on general guidelines for population groups by age, gender and social determinants; (2) individualized nutrition that adds phenotypic information about the person's current nutritional status (e.g. anthropometry, biochemical and metabolic analysis, physical activity, among others), and (3) genotype-directed nutrition based on rare or common gene variation. Research and appropriate translation into medical practice and dietary recommendations must be based on a solid foundation of knowledge derived from studies on nutrigenetics and nutrigenomics. A scientific society, such as the International Society of Nutrigenetics/Nutrigenomics (ISNN), internationally devoted to the study of nutrigenetics/nutrigenomics, can indeed serve the commendable roles of (1) promoting science and favoring scientific communication and (2) permanently working as a 'clearing house' to prevent disqualifying logical jumps, correct or stop unwarranted claims, and prevent the creation of unwarranted expectations in patients and in the general public. In this statement, we are focusing on the scientific aspects of disciplines covering nutrigenetics and nutrigenomics issues. Genetic screening and the ethical, legal, social and economic aspects will be dealt with in subsequent statements of the Society. © 2016 S. Karger AG, Basel.
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Rajamani, Deepa; Bhasin, Manoj K
2016-05-03
Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.
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Integrated multi-omic analysis of host-microbiota interactions in acute oak decline.
Broberg, Martin; Doonan, James; Mundt, Filip; Denman, Sandra; McDonald, James E
2018-01-30
Britain's native oak species are currently under threat from acute oak decline (AOD), a decline-disease where stem bleeds overlying necrotic lesions in the inner bark and larval galleries of the bark-boring beetle, Agrilus biguttatus, represent the primary symptoms. It is known that complex interactions between the plant host and its microbiome, i.e. the holobiont, significantly influence the health status of the plant. In AOD, necrotic lesions are caused by a microbiome shift to a pathobiome consisting predominantly of Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and potentially other bacteria. However, the specific mechanistic processes of the microbiota causing tissue necrosis, and the host response, have not been established and represent a barrier to understanding and managing this decline. We profiled the metagenome, metatranscriptome and metaproteome of inner bark tissue from AOD symptomatic and non-symptomatic trees to characterise microbiota-host interactions. Active bacterial virulence factors such as plant cell wall-degrading enzymes, reactive oxygen species defence and flagella in AOD lesions, along with host defence responses including reactive oxygen species, cell wall modification and defence regulators were identified. B. goodwinii dominated the lesion microbiome, with significant expression of virulence factors such as the phytopathogen effector avrE. A smaller proportion of microbiome activity was attributed to G. quercinecans and R. victoriana. In addition, we describe for the first time the potential role of two previously uncharacterised Gram-positive bacteria predicted from metagenomic binning and identified as active in the AOD lesion metatranscriptome and metaproteome, implicating them in lesion formation. This multi-omic study provides novel functional insights into microbiota-host interactions in AOD, a complex arboreal decline disease where polymicrobial-host interactions result in lesion formation on tree stems. We present the first descriptions of holobiont function in oak health and disease, specifically, the relative lesion activity of B. goodwinii, G. quercinecans, Rahnella victoriana and other bacteria. Thus, the research presented here provides evidence of some of the mechanisms used by members of the lesion microbiome and a template for future multi-omic research into holobiont characterisation, plant polymicrobial diseases and pathogen defence in trees.
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The Bio-Community Perl toolkit for microbial ecology.
Angly, Florent E; Fields, Christopher J; Tyson, Gene W
2014-07-01
The development of bioinformatic solutions for microbial ecology in Perl is limited by the lack of modules to represent and manipulate microbial community profiles from amplicon and meta-omics studies. Here we introduce Bio-Community, an open-source, collaborative toolkit that extends BioPerl. Bio-Community interfaces with commonly used programs using various file formats, including BIOM, and provides operations such as rarefaction and taxonomic summaries. Bio-Community will help bioinformaticians to quickly piece together custom analysis pipelines and develop novel software. Availability an implementation: Bio-Community is cross-platform Perl code available from http://search.cpan.org/dist/Bio-Community under the Perl license. A readme file describes software installation and how to contribute. © The Author 2014. Published by Oxford University Press.
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Perini, Matteo; Paolini, Mauro; Camin, Federica; Appendino, Giovanni; Vitulo, Francesca; De Combarieu, Eric; Sardone, Nicola; Martinelli, Ernesto Marco; Pace, Roberto
2018-04-22
Saw palmetto (Serenoa repens, SP) is the most expensive oil source of the pharmaceutical and healthfood market, and its high cost and recurrent shortages have spurred the development of designer blends of fatty acids to mimic its phytochemical profile and fraudulently comply with the current authentication assays. To detect this adulteration, the combined use of isotopic fingerprint and omic analysis has been investigated, using Principal Component Analysis (PCA) to handle the complex databases generated by these techniques and to identify the possible source of the adulterants. Surprisingly, the presence of fatty acids of animal origin turned out to be widespread in commercial samples of saw palmetto oil. Copyright © 2018 Elsevier B.V. All rights reserved.
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EXPOsOMICS: final policy workshop and stakeholder consultation.
Turner, Michelle C; Vineis, Paolo; Seleiro, Eduardo; Dijmarescu, Michaela; Balshaw, David; Bertollini, Roberto; Chadeau-Hyam, Marc; Gant, Timothy; Gulliver, John; Jeong, Ayoung; Kyrtopoulos, Soterios; Martuzzi, Marco; Miller, Gary W; Nawrot, Timothy; Nieuwenhuijsen, Mark; Phillips, David H; Probst-Hensch, Nicole; Samet, Jonathan; Vermeulen, Roel; Vlaanderen, Jelle; Vrijheid, Martine; Wild, Christopher; Kogevinas, Manolis
2018-02-15
The final meeting of the EXPOsOMICS project "Final Policy Workshop and Stakeholder Consultation" took place 28-29 March 2017 to present the main results of the project and discuss their implications both for future research and for regulatory and policy activities. This paper summarizes presentations and discussions at the meeting related with the main results and advances in exposome research achieved through the EXPOsOMICS project; on other parallel research initiatives on the study of the exposome in Europe and in the United States and their complementarity to EXPOsOMICS; lessons learned from these early studies on the exposome and how they may shape the future of research on environmental exposure assessment; and finally the broader implications of exposome research for risk assessment and policy development on environmental exposures. The main results of EXPOsOMICS in relation to studies of the external exposome and internal exposome in relation to both air pollution and water contaminants were presented as well as new technologies for environmental health research (adductomics) and advances in statistical methods. Although exposome research strengthens the scientific basis for policy development, there is a need in terms of showing added value for public health to: improve communication of research results to non-scientific audiences; target research to the broader landscape of societal challenges; and draw applicable conclusions. Priorities for future work include the development and standardization of methodologies and technologies for assessing the external and internal exposome, improved data sharing and integration, and the demonstration of the added value of exposome science over conventional approaches in answering priority policy questions.
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Using Omics to Understand and Treat Pulmonary Vascular Disease.
Hemnes, Anna R
2018-01-01
Pulmonary arterial hypertension (PAH) is a devastating disease for which there is no cure. Presently this condition is differentiated from other diseases of the pulmonary vasculature by a practitioner's history, physical examination, and clinical studies with clinical markers of disease severity primarily guiding therapeutic choices. New technologies such as next generation DNA sequencing, high throughput RNA sequencing, metabolomics and proteomics have greatly enhanced the amount of data that can be studied efficiently in patients with PAH and other rare diseases. There is emerging data on the use of these "Omics" for pulmonary vascular disease classification and diagnosis and also new work that suggests molecular markers, including Omics, may be used to more efficiently match patients to their own most effective therapies. This review focuses on the state of knowledge on molecular classification and treatment of PAH. Strengths and weaknesses of current Omic technologies are discussed and how these new technologies can be used in the future to improve diagnosis of pulmonary vascular disease, more effectively treat patients with existing and future drugs, and generate new understanding of disease pathogenesis and mechanisms underlying treatment success or failure. Bioinformatic methods to analyze the large volumes of data are developing rapidly, but still present major challenges to interpretation of potential Omic findings in pulmonary vascular disease, with low numbers of patients studied and a potentially high false discovery rate. With more experience, precise and established drug response definitions, this field with move forward and will likely be a major component of the clinical care of PH patients in the future.
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A New Omics Data Resource of Pleurocybella porrigens for Gene Discovery
Dohra, Hideo; Someya, Takumi; Takano, Tomoyuki; Harada, Kiyonori; Omae, Saori; Hirai, Hirofumi; Yano, Kentaro; Kawagishi, Hirokazu
2013-01-01
Background Pleurocybella porrigens is a mushroom-forming fungus, which has been consumed as a traditional food in Japan. In 2004, 55 people were poisoned by eating the mushroom and 17 people among them died of acute encephalopathy. Since then, the Japanese government has been alerting Japanese people to take precautions against eating the P . porrigens mushroom. Unfortunately, despite efforts, the molecular mechanism of the encephalopathy remains elusive. The genome and transcriptome sequence data of P . porrigens and the related species, however, are not stored in the public database. To gain the omics data in P . porrigens , we sequenced genome and transcriptome of its fruiting bodies and mycelia by next generation sequencing. Methodology/Principal Findings Short read sequences of genomic DNAs and mRNAs in P . porrigens were generated by Illumina Genome Analyzer. Genome short reads were de novo assembled into scaffolds using Velvet. Comparisons of genome signatures among Agaricales showed that P . porrigens has a unique genome signature. Transcriptome sequences were assembled into contigs (unigenes). Biological functions of unigenes were predicted by Gene Ontology and KEGG pathway analyses. The majority of unigenes would be novel genes without significant counterparts in the public omics databases. Conclusions Functional analyses of unigenes present the existence of numerous novel genes in the basidiomycetes division. The results mean that the omics information such as genome, transcriptome and metabolome in basidiomycetes is short in the current databases. The large-scale omics information on P . porrigens , provided from this research, will give a new data resource for gene discovery in basidiomycetes. PMID:23936076
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NASA Astrophysics Data System (ADS)
Koelmel, Jeremy P.; Kroeger, Nicholas M.; Gill, Emily L.; Ulmer, Candice Z.; Bowden, John A.; Patterson, Rainey E.; Yost, Richard A.; Garrett, Timothy J.
2017-05-01
Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease.
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Malla, Muneer A; Dubey, Anamika; Yadav, Shweta; Kumar, Ashwani; Hashem, Abeer; Abd Allah, Elsayed Fathi
2018-01-01
Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.
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Wolters, Jarno E J; van Breda, Simone G J; Grossmann, Jonas; Fortes, Claudia; Caiment, Florian; Kleinjans, Jos C S
2018-06-01
We performed a multiple 'omics study by integrating data on epigenomic, transcriptomic, and proteomic perturbations associated with mitochondrial dysfunction in primary human hepatocytes caused by the liver toxicant valproic acid (VPA), to deeper understand downstream events following epigenetic alterations in the mitochondrial genome. Furthermore, we investigated persistence of cross-omics changes after terminating drug treatment. Upon transient methylation changes of mitochondrial genes during VPA-treatment, increasing complexities of gene-interaction networks across time were demonstrated, which normalized during washout. Furthermore, co-expression between genes and their corresponding proteins increased across time. Additionally, in relation to persistently decreased ATP production, we observed decreased expression of mitochondrial complex I and III-V genes. Persistent transcripts and proteins were related to citric acid cycle and β-oxidation. In particular, we identified a potential novel mitochondrial-nuclear signaling axis, MT-CO2-FN1-MYC-CPT1. In summary, this cross-omics study revealed dynamic responses of the mitochondrial epigenome to an impulse toxicant challenge resulting in persistent mitochondrial dysfunctioning. Moreover, this approach allowed for discriminating between the toxic effect of VPA and adaptation. Copyright © 2018 Elsevier B.V. All rights reserved.
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Omics of the marine medaka (Oryzias melastigma) and its relevance to marine environmental research.
Kim, Bo-Mi; Kim, Jaebum; Choi, Ik-Young; Raisuddin, Sheikh; Au, Doris W T; Leung, Kenneth M Y; Wu, Rudolf S S; Rhee, Jae-Sung; Lee, Jae-Seong
2016-02-01
In recent years, the marine medaka (Oryzias melastigma), also known as the Indian medaka or brackish medaka, has been recognized as a model fish species for ecotoxicology and environmental research in the Asian region. O. melastigma has several promising features for research, which include a short generation period (3-4 months), daily spawning, small size (3-4 cm), transparent embryos, sexual dimorphism, and ease of mass culture in the laboratory. There have been extensive transcriptome and genome studies on the marine medaka in the past decade. Such omics data can be useful in understanding the signal transduction pathways of small teleosts in response to environmental stressors. An omics-integrated approach in the study of the marine medaka is important for strengthening its role as a small fish model for marine environmental studies. In this review, we present current omics information about the marine medaka and discuss its potential applications in the study of various molecular pathways that can be targets of marine environmental stressors, such as chemical pollutants. We believe that this review will encourage the use of this small fish as a model species in marine environmental research. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali
2018-06-01
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.
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Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level
Pathak, Ravi Ramesh; Davé, Vrushank
2014-01-01
Assimilation and integration of “omics” technologies, including genomics, epigenomics, proteomics, and metabolomics has readily altered the landscape of medical research in the last decade. The vast and complex nature of omics data can only be interpreted by linking molecular information at the organismic level, forming the foundation of systems biology. Research in pulmonary biology/medicine has necessitated integration of omics, network, systems and computational biology data to differentially diagnose, interpret, and prognosticate pulmonary diseases, facilitating improvement in therapy and treatment modalities. This review describes how to leverage this emerging technology in understanding pulmonary diseases at the systems level –called a “systomic” approach. Considering the operational wholeness of cellular and organ systems, diseased genome, proteome, and the metabolome needs to be conceptualized at the systems level to understand disease pathogenesis and progression. Currently available omics technology and resources require a certain degree of training and proficiency in addition to dedicated hardware and applications, making them relatively less user friendly for the pulmonary biologist and clinicians. Herein, we discuss the various strategies, computational tools and approaches required to study pulmonary diseases at the systems level for biomedical scientists and clinical researchers. PMID:24802001
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The Twins Study: NASA's First Foray into 21st Century Omics Research
NASA Technical Reports Server (NTRS)
Kundrot, C. E.; Shelhamer, M.; Scott, G. B. I.
2015-01-01
The full array of 21st century omics-based research methods should be intelligently employed to reduce the health and performance risks that astronauts will be exposed to during exploration missions beyond low Earth Orbit. In March of 2015, US Astronaut Scott Kelly will launch to the International Space Station for a one year mission while his twin brother, Mark Kelly, a retired US Astronaut, remains on the ground. This situation presents an extremely rare flight opportunity to perform an integrated omics-based demonstration pilot study involving identical twin astronauts. A group of 10 principal investigators has been competitively selected, funded, and teamed together to form the Twins Study. A very broad range of biological function are being examined including the genome, epigenome, transcriptome, proteome, metabolome, gut microbiome, immunological response to vaccinations, indicators of atherosclerosis, physiological fluid shifts, and cognition. The plans for the Twins Study and an overview of initial results will be described as well as the technological and ethical issues raised for such spaceflight studies. An anticipated outcome of the Twins Study is that it will place NASA on a trajectory of using omics-based information to develop precision countermeasures for individual astronauts.
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Malla, Muneer A.; Dubey, Anamika; Yadav, Shweta; Kumar, Ashwani; Hashem, Abeer; Abd_Allah, Elsayed Fathi
2018-01-01
Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches. PMID:29915565
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Gender profiling: a gendered race perspective on person-position fit.
Hall, Erika V; Galinsky, Adam D; Phillips, Katherine W
2015-06-01
The current research integrates perspectives on gendered race and person-position fit to introduce the concept of a gender profile. We propose that both the "gender" of a person's biological sex and the "gender" of a person's race (Asians are perceived as feminine and Blacks as masculine) help comprise an individual's gender profile-the overall femininity or masculinity associated with their demographic characteristics. We also propose that occupational positions have gender profiles. Finally, we argue that the overall gender profile of one's demographics, rather than just one's biological sex, determines one's fit and hirability for feminine or masculine occupational roles. The current five studies establish the gender profiles of different races and sexes, and then demonstrate that individuals with feminine-typed and masculine-typed gender profiles are selected for feminine and masculine positions, respectively. These studies provide new insights on who gets ahead in different environments. © 2015 by the Society for Personality and Social Psychology, Inc.
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RASopathies are associated with a distinct personality profile.
Bizaoui, Varoona; Gage, Jessica; Brar, Rita; Rauen, Katherine A; Weiss, Lauren A
2018-06-01
Personality is a complex, yet partially heritable, trait. Although some Mendelian diseases like Williams-Beuren syndrome are associated with a particular personality profile, studies have failed to assign the personality features to a single gene or pathway. As a family of monogenic disorders caused by mutations in the Ras/MAPK pathway known to influence social behavior, RASopathies are likely to provide insight into the genetic basis of personality. Eighty subjects diagnosed with cardiofaciocutaneous syndrome, Costello syndrome, neurofibromatosis type 1, and Noonan syndrome were assessed using a parent-report BFQ-C (Big Five Questionnaire for Children) evaluating agreeableness, extraversion, conscientiousness, intellect/openness, and neuroticism, along with 55 unaffected sibling controls. A short questionnaire was added to assess sense of humor. RASopathy subjects and sibling controls were compared for individual components of personality, multidimensional personality profiles, and individual questions using Student tests, analysis of variance, and principal component analysis. RASopathy subjects were given lower scores on average compared to sibling controls in agreeableness, extraversion, conscientiousness, openness, and sense of humor, and similar scores in neuroticism. When comparing the multidimensional personality profile between groups, RASopathies showed a distinct profile from unaffected siblings, but no difference in this global profile was found within RASopathies, revealing a common profile for the Ras/MAPK-related disorders. In addition, several syndrome-specific strengths or weaknesses were observed in individual domains. We describe for the first time an association between a single pathway and a specific personality profile, providing a better understanding of the genetics underlying personality, and new tools for tailoring educational and behavioral approaches for individuals with RASopathies. © 2018 Wiley Periodicals, Inc.
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A Syst-OMICS Approach to Ensuring Food Safety and Reducing the Economic Burden of Salmonellosis.
Emond-Rheault, Jean-Guillaume; Jeukens, Julie; Freschi, Luca; Kukavica-Ibrulj, Irena; Boyle, Brian; Dupont, Marie-Josée; Colavecchio, Anna; Barrere, Virginie; Cadieux, Brigitte; Arya, Gitanjali; Bekal, Sadjia; Berry, Chrystal; Burnett, Elton; Cavestri, Camille; Chapin, Travis K; Crouse, Alanna; Daigle, France; Danyluk, Michelle D; Delaquis, Pascal; Dewar, Ken; Doualla-Bell, Florence; Fliss, Ismail; Fong, Karen; Fournier, Eric; Franz, Eelco; Garduno, Rafael; Gill, Alexander; Gruenheid, Samantha; Harris, Linda; Huang, Carol B; Huang, Hongsheng; Johnson, Roger; Joly, Yann; Kerhoas, Maud; Kong, Nguyet; Lapointe, Gisèle; Larivière, Line; Loignon, Stéphanie; Malo, Danielle; Moineau, Sylvain; Mottawea, Walid; Mukhopadhyay, Kakali; Nadon, Céline; Nash, John; Ngueng Feze, Ida; Ogunremi, Dele; Perets, Ann; Pilar, Ana V; Reimer, Aleisha R; Robertson, James; Rohde, John; Sanderson, Kenneth E; Song, Lingqiao; Stephan, Roger; Tamber, Sandeep; Thomassin, Paul; Tremblay, Denise; Usongo, Valentine; Vincent, Caroline; Wang, Siyun; Weadge, Joel T; Wiedmann, Martin; Wijnands, Lucas; Wilson, Emily D; Wittum, Thomas; Yoshida, Catherine; Youfsi, Khadija; Zhu, Lei; Weimer, Bart C; Goodridge, Lawrence; Levesque, Roger C
2017-01-01
The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.
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Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering☆
Rabitz, Herschel; Welsh, William J.; Kohn, Joachim; de Boer, Jan
2016-01-01
The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. PMID:26876875
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RCAS: an RNA centric annotation system for transcriptome-wide regions of interest.
Uyar, Bora; Yusuf, Dilmurat; Wurmus, Ricardo; Rajewsky, Nikolaus; Ohler, Uwe; Akalin, Altuna
2017-06-02
In the field of RNA, the technologies for studying the transcriptome have created a tremendous potential for deciphering the puzzles of the RNA biology. Along with the excitement, the unprecedented volume of RNA related omics data is creating great challenges in bioinformatics analyses. Here, we present the RNA Centric Annotation System (RCAS), an R package, which is designed to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies. The design of RCAS is modular, which enables flexible usage and convenient integration with other bioinformatics workflows. RCAS is an R/Bioconductor package but we also created graphical user interfaces including a Galaxy wrapper and a stand-alone web service. The application of RCAS on published datasets shows that RCAS is not only able to reproduce published findings but also helps generate novel knowledge and hypotheses. The meta-gene profiles, gene-centric annotation, motif analysis and gene-set analysis provided by RCAS provide contextual knowledge which is necessary for understanding the functional aspects of different biological events that involve RNAs. In addition, the array of different interfaces and deployment options adds the convenience of use for different levels of users. RCAS is available at http://bioconductor.org/packages/release/bioc/html/RCAS.html and http://rcas.mdc-berlin.de. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Metaproteomics as a Complementary Approach to Gut Microbiota in Health and Disease
NASA Astrophysics Data System (ADS)
Petriz, Bernardo A.; Franco, Octávio L.
2017-01-01
Classic studies on phylotype profiling are limited to the identification of microbial constituents, where information is lacking about the molecular interaction of these bacterial communities with the host genome and the possible outcomes in host biology. A range of OMICs approaches have provided great progress linking the microbiota to health and disease. However, the investigation of this context through proteomic mass spectrometry-based tools is still being improved. Therefore, metaproteomics or community proteogenomics has emerged as a complementary approach to metagenomic data, as a field in proteomics aiming to perform large-scale characterization of proteins from environmental microbiota such as the human gut. The advances in molecular separation methods coupled with mass spectrometry (e.g. LC-MS/MS) and proteome bioinformatics have been fundamental in these novel large-scale metaproteomic studies, which have further been performed in a wide range of samples including soil, plant and human environments. Metaproteomic studies will make major progress if a comprehensive database covering the genes and expresses proteins from all gut microbial species is developed. To this end, we here present some of the main limitations of metaproteomic studies in complex microbiota environments such as the gut, also addressing the up-to-date pipelines in sample preparation prior to fractionation/separation and mass spectrometry analysis. In addition, a novel approach to the limitations of metagenomic databases is also discussed. Finally, prospects are addressed regarding the application of metaproteomic analysis using a unified host-microbiome gene database and other meta-OMICs platforms.
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Profiles of drug addicts in relation to personality variables and disorders.
Carou, María; Romero, Estrella; Luengo, Mª Ángeles
2016-10-07
In recent decades, research has identified a set of impulsive/disinhibited personality variables closely associated with drug addiction. As well as this, disorders linked with these variables, such as ADHD and personality disorders, are being closely studied in the field of drug addiction. Although much knowledge has been accumulated about the relation of these variables and disorders taken separately, less is known about how these constructs allow identify-specific profiles within the drug dependent population to be identified. This work, on the basis of data collected on a sample of drug addicts in treatment, analyzes how impulsiveness, sensation seeking, self-control, ADHD and personality disorders contribute to identifying specific profiles of addicts. Cluster analysis allowed two profiles to be outlined according to these personality and psychopathology characteristics. Self-control, impulsiveness, impulsive and antisocial personality disorders, as well as scores in ADHD, emerge as the variables that contribute more to profile differentiation. One of these profiles (56.1% of participants) with a high disinhibition pattern, is associated with severe indicators of consumption and criminal career patterns. These results allow us to emphasize the role of personality and impulsiveness-related disorders in the identification of distinctive profiles within the addict population, and suggest the need to generate treatment strategies adapted to personal/psychopathology configurations of drug addicts.
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Barajas Iglesias, Belén; Jáuregui Lobera, Ignacio; Laporta Herrero, Isabel; Santed Germán, Miguel Ángel
2017-10-24
Previous studies provide relevant information about the relationship between personality and eating disorders (ED). The involvement of personality factors in the etiology and maintenance of ED indicates the need of emphasizing the study of the adolescent's personality when diagnosed of ED. The aims of this study were to analyze the adolescent's personality profiles that differ significantly in anorexia nervosa (AN) and bulimia nervosa (BN), and to explore the most common profiles and their associations with those subtypes of eating disorders (ED). A total of 104 patients with AN and BN were studied by means of the Millon Adolescent Clinical Inventory (MACI). The personality profiles that differ significantly in both AN and BN were submissive, egotistic, unruly, forceful, conforming, oppositional, self-demeaning and borderline. The most frequent profiles in AN were conforming (33.33%), egotistic (22.72%) and dramatizing (18.18%) while in the case of BN those profiles were unruly (18.42%), submissive (18.42%) and borderline (15.78%). We did not find any associations between the diagnostic subgroup (AN, BN) and the fact of having personality profiles that could become dysfunctional. Bearing in mind these results, it may be concluded that there are relevant differences between personality profiles associated with AN and BN during adolescence, so tailoring therapeutic interventions for this specific population would be important.
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Next-generation sequencing for cancer drug development: the present and visions for the future.
Kahn, Scott M
2014-03-01
NGS for Cancer Drug Development 24-26 September 2013, Boston, MA, USA Many of us who, prior to the -omics revolution, staunchly pursued the molecular basis of disease causation recognized the fundamental importance that biomarkers held for the eventuality of personalized medicine, more accurate diagnostics and targeted drug discovery. With slab sequencing apparatuses now mere memories relegated to the dusty reaches of old laboratory cabinets, we are fervently unraveling the complexities of many diseases through newer, more powerful innovative technologies. Quicker than ever before, we are achieving a deeper understanding of causative genetic lesions, perturbed pathways and the functions of altered networks. This has opened the door to a golden age of data generation that shows promise as a vital key to the successful treatment of human diseases. Hanson Wade recently organized a series of three related symposia that highlighted advances in industry and academic research in the areas of drug development, companion diagnostics and disease profiling: 'NGS for Cancer Drug Development' conference in Boston on 24-26 September 2013, the 'NGS Data Analysis' conference in San Francisco on 15-17 October 2013 and the 'World CDx' conference in Boston on 12-15 November 2013. These meetings provided forums for leaders to present advancements in personalized medicine, strategies for drug development, and innovations in diagnostics and other important areas. Their highly interactive nature also provided opportunities for industry and academic attendees to identify common hurdles that stand in the way of progress, (e.g., biomarker validation, analytical software limitations, data sharing, sample handling, regulatory and reimbursement restrictions), and to propose strategies for independent and community approaches toward overcoming such hurdles. In the interest of space, this review will be limited to the first in this series, the NGS for Cancer Drug Development conference, and its six major areas of focus.
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Guhlin, Joseph; Silverstein, Kevin A T; Zhou, Peng; Tiffin, Peter; Young, Nevin D
2017-08-10
Rapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data. The Omics Database Generator (ODG) allows users to create customized databases that utilize published genomics data integrated with experimental data which can be queried using a flexible graph database. When provided with omics and experimental data, ODG will create a comparative, multi-dimensional graph database. ODG can import definitions and annotations from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others. This annotation data can be especially useful for studying new or understudied species for which transcripts have only been predicted, and rapidly give additional layers of annotation to predicted genes. In better studied species, ODG can perform syntenic annotation translations or rapidly identify characteristics of a set of genes or nucleotide locations, such as hits from an association study. ODG provides a web-based user-interface for configuring the data import and for querying the database. Queries can also be run from the command-line and the database can be queried directly through programming language hooks available for most languages. ODG supports most common genomic formats as well as generic, easy to use tab-separated value format for user-provided annotations. ODG is a user-friendly database generation and query tool that adapts to the supplied data to produce a comparative genomic database or multi-layered annotation database. ODG provides rapid comparative genomic annotation and is therefore particularly useful for non-model or understudied species. For species for which more data are available, ODG can be used to conduct complex multi-omics, pattern-matching queries.
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Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana
2017-01-01
Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).
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Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han
2015-01-01
Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459
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Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han
2015-01-01
Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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Inferring gene ontologies from pairwise similarity data
Kramer, Michael; Dutkowski, Janusz; Yu, Michael; Bafna, Vineet; Ideker, Trey
2014-01-01
Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene–gene pairwise similarities from -omics data;infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; andrespect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference. Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method’s ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast. Results: For task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20–25% precision, recall). Conclusion: This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data. Contact: tideker@ucsd.edu PMID:24932003
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OMICS-strategies and methods in the fight against doping.
Reichel, Christian
2011-12-10
During the past decade OMICS-methods not only continued to have their impact on research strategies in life sciences and in particular molecular biology, but also started to be used for anti-doping control purposes. Research activities were mainly reasoned by the fact that several substances and methods, which were prohibited by the World Anti-Doping Agency (WADA), were or still are difficult to detect by direct methods. Transcriptomics, proteomics, and metabolomics in theory offer ideal platforms for the discovery of biomarkers for the indirect detection of the abuse of these substances and methods. Traditionally, the main focus of transcriptomics and proteomics projects has been on the prolonged detection of the misuse of human growth hormone (hGH), recombinant erythropoietin (rhEpo), and autologous blood transfusion. An additional benefit of the indirect or marker approach would also be that similarly acting substances might then be detected by a single method, without being forced to develop new direct detection methods for new but comparable prohibited substances (as has been the case, e.g. for the various forms of Epo analogs and biosimilars). While several non-OMICS-derived parameters for the indirect detection of doping are currently in use, for example the blood parameters of the hematological module of the athlete's biological passport, the outcome of most non-targeted OMICS-projects led to no direct application in routine doping control so far. The main reason is the inherent complexity of human transcriptomes, proteomes, and metabolomes and their inter-individual variability. The article reviews previous and recent research projects and their results and discusses future strategies for a more efficient application of OMICS-methods in doping control. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
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Compagnone, Christian; Schatman, Michael E; Rauck, Richard L; Van Zundert, Jan; Kraus, Monika; Primorac, Dragan; Williams, Frances; Allegri, Massimo; Saccani Jordi, Gloria; Fanelli, Guido
2017-01-01
In recent decades, there has been a revision of the role of institutional review boards with the intention of protecting human subjects from harm and exploitation in research. Informed consent aims to protect the subject by explaining all of the benefits and risks associated with a specific research project. To date, there has not been a review published analyzing issues of informed consent in research in the field of genetic/Omics in subjects with chronic pain, and the current review aims to fill that gap in the ethical aspects of such investigation. Despite the extensive discussion on ethical challenges unique to the field of genetic/Omics, this is the first attempt at addressing ethical challenges regarding Informed Consent Forms for pain research as the primary focus. We see this contribution as an important one, for while ethical issues are too often ignored in pain research in general, the numerous arising ethical issues that are unique to pain genetic/Omics suggest that researchers in the field need to pay even greater attention to the rights of subjects/patients. This article presents the work of the Ethic Committee of the Pain-Omics Group (www.painomics.eu), a consortium of 11 centers that is running the Pain-Omics project funded by the European Community in the 7th Framework Program theme (HEALTH.2013.2.2.1-5-Understanding and controlling pain). The Ethic Committee is composed of 1 member of each group of the consortium as well as key opinion leaders in the field of ethics and pain more generally. © 2016 The Authors. Pain Practice published by Wiley Periodicals, Inc. on behalf of World Institute of Pain.
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Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.
Pineda, Silvia; Van Steen, Kristel; Malats, Núria
2017-09-01
Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.
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Zhang, Shu-Dong; Gant, Timothy W
2009-07-31
Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures. This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies. The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap.
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The emerging CHO systems biology era: harnessing the 'omics revolution for biotechnology.
Kildegaard, Helene Faustrup; Baycin-Hizal, Deniz; Lewis, Nathan E; Betenbaugh, Michael J
2013-12-01
Chinese hamster ovary (CHO) cells are the primary factories for biopharmaceuticals because of their capacity to correctly fold and post-translationally modify recombinant proteins compatible with humans. New opportunities are arising to enhance these cell factories, especially since the CHO-K1 cell line was recently sequenced. Now, the CHO systems biology era is underway. Critical 'omics data sets, including proteomics, transcriptomics, metabolomics, fluxomics, and glycomics, are emerging, allowing the elucidation of the molecular basis of CHO cell physiology. The incorporation of these data sets into mathematical models that describe CHO phenotypes will provide crucial biotechnology insights. As 'omics technologies and computational systems biology mature, genome-scale approaches will lead to major innovations in cell line development and metabolic engineering, thereby improving protein production and bioprocessing. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Tools for the functional interpretation of metabolomic experiments.
Chagoyen, Monica; Pazos, Florencio
2013-11-01
The so-called 'omics' approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the 'omics' family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other 'omics', such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future.
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Omics-Based Identification of Biomarkers for Nasopharyngeal Carcinoma
2015-01-01
Nasopharyngeal carcinoma (NPC) is a head and neck cancer that is highly found in distinct geographic areas, such as Southeast Asia. The management of NPC remains burdensome as the prognosis is poor due to the late presentation of the disease and the complex nature of NPC pathogenesis. Therefore, it is necessary to find effective molecular markers for early detection and therapeutic measure of NPC. In this paper, the discovery of molecular biomarker for NPC through the emerging omics technologies including genomics, miRNA-omics, transcriptomics, proteomics, and metabolomics will be extensively reviewed. These markers have been shown to play roles in various cellular pathways in NPC progression. The knowledge on their function will help us understand in more detail the complexity in tumor biology, leading to the better strategies for early detection, outcome prediction, detection of disease recurrence, and therapeutic approach. PMID:25999660
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How are personality trait and profile agreement related?
Allik, Jüri; Borkenau, Peter; Hřebíčková, Martina; Kuppens, Peter; Realo, Anu
2015-01-01
It is argued that if we compute self-other agreement on some personality traits then we possess no or very little information about the individuals who are the targets of this judgment. This idea is largely based on two separate ways of computing self-other agreement: trait agreement (rT) and profile agreement (rP), which are typically associated with two different trait-centered and person-centered approaches in personality research. Personality traits of 4115 targets from Czech, Belgian, Estonian, and German samples were rated by themselves and knowledgeable informants. We demonstrate that trait agreement can be partialled into individual contributions so that it is possible to show how much each individual pair of judges contributes to agreement on a particular trait. Similarly, it is possible to decompose agreement between two personality profiles into the individual contributions of traits from which these profiles are assembled. If normativeness is separated from distinctiveness of personality scores and individual profiles are ipsatized, then mean profile agreement rP becomes identical to mean trait agreement rT. The views that trait-by-trait analysis does not provide information regarding accuracy level of a particular pair of judges and profile analysis does not permit assessment of the relative contributions of traits to overall accuracy are not supported. PMID:26106356
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The Tohoku Medical Megabank Project: Design and Mission.
Kuriyama, Shinichi; Yaegashi, Nobuo; Nagami, Fuji; Arai, Tomohiko; Kawaguchi, Yoshio; Osumi, Noriko; Sakaida, Masaki; Suzuki, Yoichi; Nakayama, Keiko; Hashizume, Hiroaki; Tamiya, Gen; Kawame, Hiroshi; Suzuki, Kichiya; Hozawa, Atsushi; Nakaya, Naoki; Kikuya, Masahiro; Metoki, Hirohito; Tsuji, Ichiro; Fuse, Nobuo; Kiyomoto, Hideyasu; Sugawara, Junichi; Tsuboi, Akito; Egawa, Shinichi; Ito, Kiyoshi; Chida, Koichi; Ishii, Tadashi; Tomita, Hiroaki; Taki, Yasuyuki; Minegishi, Naoko; Ishii, Naoto; Yasuda, Jun; Igarashi, Kazuhiko; Shimizu, Ritsuko; Nagasaki, Masao; Koshiba, Seizo; Kinoshita, Kengo; Ogishima, Soichi; Takai-Igarashi, Takako; Tominaga, Teiji; Tanabe, Osamu; Ohuchi, Noriaki; Shimosegawa, Toru; Kure, Shigeo; Tanaka, Hiroshi; Ito, Sadayoshi; Hitomi, Jiro; Tanno, Kozo; Nakamura, Motoyuki; Ogasawara, Kuniaki; Kobayashi, Seiichiro; Sakata, Kiyomi; Satoh, Mamoru; Shimizu, Atsushi; Sasaki, Makoto; Endo, Ryujin; Sobue, Kenji; Tohoku Medical Megabank Project Study Group, The; Yamamoto, Masayuki
2016-09-05
The Great East Japan Earthquake (GEJE) and resulting tsunami of March 11, 2011 gave rise to devastating damage on the Pacific coast of the Tohoku region. The Tohoku Medical Megabank Project (TMM), which is being conducted by Tohoku University Tohoku Medical Megabank Organization (ToMMo) and Iwate Medical University Iwate Tohoku Medical Megabank Organization (IMM), has been launched to realize creative reconstruction and to solve medical problems in the aftermath of this disaster. We started two prospective cohort studies in Miyagi and Iwate Prefectures: a population-based adult cohort study, the TMM Community-Based Cohort Study (TMM CommCohort Study), which will recruit 80 000 participants, and a birth and three-generation cohort study, the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study), which will recruit 70 000 participants, including fetuses and their parents, siblings, grandparents, and extended family members. The TMM CommCohort Study will recruit participants from 2013 to 2016 and follow them for at least 5 years. The TMM BirThree Cohort Study will recruit participants from 2013 to 2017 and follow them for at least 4 years. For children, the ToMMo Child Health Study, which adopted a cross-sectional design, was also started in November 2012 in Miyagi Prefecture. An integrated biobank will be constructed based on the two prospective cohort studies, and ToMMo and IMM will investigate the chronic medical impacts of the GEJE. The integrated biobank of TMM consists of health and clinical information, biospecimens, and genome and omics data. The biobank aims to establish a firm basis for personalized healthcare and medicine, mainly for diseases aggravated by the GEJE in the two prefectures. Biospecimens and related information in the biobank will be distributed to the research community. TMM itself will also undertake genomic and omics research. The aims of the genomic studies are: 1) to construct an integrated biobank; 2) to return genomic research results to the participants of the cohort studies, which will lead to the implementation of personalized healthcare and medicine in the affected areas in the near future; and 3) to contribute the development of personalized healthcare and medicine worldwide. Through the activities of TMM, we will clarify how to approach prolonged healthcare problems in areas damaged by large-scale disasters and how useful genomic information is for disease prevention.
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Computational Omics Pre-Awardees | Office of Cancer Clinical Proteomics Research
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) is pleased to announce the pre-awardees of the Computational Omics solicitation. Working with NVIDIA Foundation's Compute the Cure initiative and Leidos Biomedical Research Inc., the NCI, through this solicitation, seeks to leverage computational efforts to provide tools for the mining and interpretation of large-scale publicly available ‘omics’ datasets.
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Data Standards for Omics Data: The Basis of Data Sharing and Reuse
Chervitz, Stephen A.; Deutsch, Eric W.; Field, Dawn; Parkinson, Helen; Quackenbush, John; Rocca-Serra, Phillipe; Sansone, Susanna-Assunta; Stoeckert, Christian J.; Taylor, Chris F.; Taylor, Ronald; Ball, Catherine A.
2014-01-01
To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data. PMID:21370078
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Gilbert, Jack A; Dick, Gregory J; Jenkins, Bethany; Heidelberg, John; Allen, Eric; Mackey, Katherine R M; DeLong, Edward F
2014-06-15
The National Science Foundation's EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on 'omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, "big-data capable" analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean 'omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the 'omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.
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Narayanasamy, Shaman; Muller, Emilie E L; Sheik, Abdul R; Wilmes, Paul
2015-05-01
Biological wastewater treatment plants harbour diverse and complex microbial communities which prominently serve as models for microbial ecology and mixed culture biotechnological processes. Integrated omic analyses (combined metagenomics, metatranscriptomics, metaproteomics and metabolomics) are currently gaining momentum towards providing enhanced understanding of community structure, function and dynamics in situ as well as offering the potential to discover novel biological functionalities within the framework of Eco-Systems Biology. The integration of information from genome to metabolome allows the establishment of associations between genetic potential and final phenotype, a feature not realizable by only considering single 'omes'. Therefore, in our opinion, integrated omics will become the future standard for large-scale characterization of microbial consortia including those underpinning biological wastewater treatment processes. Systematically obtained time and space-resolved omic datasets will allow deconvolution of structure-function relationships by identifying key members and functions. Such knowledge will form the foundation for discovering novel genes on a much larger scale compared with previous efforts. In general, these insights will allow us to optimize microbial biotechnological processes either through better control of mixed culture processes or by use of more efficient enzymes in bioengineering applications. © 2015 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.
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Gilbert, Jack A; Dick, Gregory J.; Jenkins, Bethany; Heidelberg, John; Allen, Eric; Mackey, Katherine R. M.
2014-01-01
The National Science Foundation’s EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on ‘omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean ‘omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ‘omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography. PMID:25197495
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Systems Biology Approaches for Host–Fungal Interactions: An Expanding Multi-Omics Frontier
Culibrk, Luka; Croft, Carys A.
2016-01-01
Abstract Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic “two systems” biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host–fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale. PMID:26885725
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2010-01-01
Background Genetic diversity among wild accessions and cultivars of common bean (Phaseolus vulgaris L.) has been characterized using plant morphology, seed protein allozymes, random amplified polymorphic DNA, restriction fragment length polymorphisms, DNA sequence analysis, chloroplast DNA, and microsatellite markers. Yet, little is known about whether these traits, which distinguish among genetically distinct types of common bean, can be evaluated using omics technologies. Results Three 'omics' approaches: transcriptomics, proteomics, and metabolomics were used to qualitatively evaluate the diversity of common bean from two Centers of Domestication (COD). All three approaches were able to classify common bean according to their COD using unsupervised analyses; these findings are consistent with the hypothesis that differences exist in gene transcription, protein expression, and synthesis and metabolism of small molecules among common bean cultivars representative of different COD. Metabolomic analyses of multiple cultivars within two common bean gene pools revealed cultivar differences in small molecules that were of sufficient magnitude to allow identification of unique cultivar fingerprints. Conclusions Given the high-throughput and low cost of each of these 'omics' platforms, significant opportunities exist for their use in the rapid identification of traits of agronomic and nutritional importance as well as to characterize genetic diversity. PMID:21126341
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Guirro, Maria; Costa, Andrea; Gual-Grau, Andreu; Mayneris-Perxachs, Jordi; Torrell, Helena; Herrero, Pol; Canela, Núria; Arola, Lluís
2018-02-10
Over the last few years, the application of high-throughput meta-omics methods has provided great progress in improving the knowledge of the gut ecosystem and linking its biodiversity to host health conditions, offering complementary support to classical microbiology. Gut microbiota plays a crucial role in relevant diseases such as obesity or cardiovascular disease (CVD), and its regulation is closely influenced by several factors, such as dietary composition. In fact, polyphenol-rich diets are the most palatable treatment to prevent hypertension associated with CVD, although the polyphenol-microbiota interactions have not been completely elucidated. For this reason, the aim of this study was to evaluate microbiota effect in obese rats supplemented by hesperidin, after being fed with cafeteria or standard diet, using a multi meta-omics approaches combining strategy of metagenomics and metaproteomics analysis. We reported that cafeteria diet induces obesity, resulting in changes in the microbiota composition, which are related to functional alterations at proteome level. In addition, hesperidin supplementation alters microbiota diversity and also proteins involved in important metabolic pathways. Overall, going deeper into strategies to integrate omics sciences is necessary to understand the complex relationships between the host, gut microbiota, and diet. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap
Xing, Eric P.; Curtis, Ross E.; Schoenherr, Georg; Lee, Seunghak; Yin, Junming; Puniyani, Kriti; Wu, Wei; Kinnaird, Peter
2014-01-01
With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from http://sailing.cs.cmu.edu/genamap. PMID:24905018
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Plant-Derived Terpenes: A Feedstock for Specialty Biofuels.
Mewalal, Ritesh; Rai, Durgesh K; Kainer, David; Chen, Feng; Külheim, Carsten; Peter, Gary F; Tuskan, Gerald A
2017-03-01
Research toward renewable and sustainable energy has identified specific terpenes capable of supplementing or replacing current petroleum-derived fuels. Despite being naturally produced and stored by many plants, there are few examples of commercial recovery of terpenes from plants because of low yields. Plant terpene biosynthesis is regulated at multiple levels, leading to wide variability in terpene content and chemistry. Advances in the plant molecular toolkit, including annotated genomes, high-throughput omics profiling, and genome editing, have begun to elucidate plant terpene metabolism, and such information is useful for bioengineering metabolic pathways for specific terpenes. We review here the status of terpenes as a specialty biofuel and discuss the potential of plants as a viable agronomic solution for future terpene-derived biofuels. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Personalized professional content recommendation
Xu, Songhua
2015-10-27
A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.
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Marmet, Simon; Studer, Joseph; Rougemont-Bücking, Ansgar; Gmel, Gerhard
2018-05-04
Recent theories suggest that behavioural addictions and substance use disorders may be the result of the same underlying vulnerability. The present study investigates profiles of family background, personality and mental health factors and their associations with seven behavioural addictions (to the internet, gaming, smartphones, internet sex, gambling, exercise and work) and three substance use disorder scales (for alcohol, cannabis and tobacco). The sample consisted of 5287 young Swiss men (mean age = 25.42) from the Cohort Study on Substance Use Risk Factors (C-SURF). A latent profile analysis was performed on family background, personality and mental health factors. The derived profiles were compared with regards to means and prevalence rates of the behavioural addiction and substance use disorder scales. Seven latent profiles were identified, ranging from profiles with a positive family background, favourable personality patterns and low values on mental health scales to profiles with a negative family background, unfavourable personality pattern and high values on mental health scales. Addiction scale means, corresponding prevalence rates and the number of concurrent addictions were highest in profiles with high values on mental health scales and a personality pattern dominated by neuroticism. Overall, behavioural addictions and substance use disorders showed similar patterns across latent profiles. Patterns of family background, personality and mental health factors were associated with different levels of vulnerability to addictions. Behavioural addictions and substance use disorders may thus be the result of the same underlying vulnerabilities. Copyright © 2018 Elsevier Masson SAS. All rights reserved.
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Perception of facial profiles: influence of female sex hormones and personality traits.
Jovic, T; Pavlic, A; Varga, S; Kovacevic Pavicic, D; Slaj, M; Spalj, S
2016-11-01
The observational study investigated whether women's perception of the facial profile is related to changes in sex hormones during the menstrual cycle and under the influence of personality traits. Participants were heterosexual Caucasian normally menstruating women not using oral contraceptives (N = 30, aged 20-44 years). The profile attractiveness was assessed by grading of thirteen men's and women's Caucasian profile distortions by a visual analogue scale (0 = least to 100 = most attractive) in the non-ovulating phase and ovulating phase of the menstrual cycle. Male profiles were graded twice-in social and emotional contexts. Personality traits were assessed by Big Five Inventory. The most attractive male profiles in both phases and contexts were a straight profile or mild lip retrusion. According to cluster analysis, non-ovulating females distinguish skeletal from dentoalveolar alterations; however, maxillary retrognathism was considered to be closer to an attractive profile, which were resulting from dentoalveolar manipulations only. Ovulating females, when considering emotional relationship, exhibit lowest preference for males with convex profiles and extreme concave profile, while they consider males with slightly prominent chins due to maxillary retrognathism, mandibular prognathism or pronounced lip retrusion closer to the most attractive males. No clear patterns of influence of personality traits were detected. Moderate lip protrusion was the most attractive female profile in ovulating and straight profile in non-ovulating phase. The favorable profiles, on average, are the same regardless of the female hormonal status and personality traits. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Xu, Peng; Wang, Junhua; Sun, Bo; Xiao, Zhongdang
2018-06-15
Investigating the potential biological function of differential changed genes through integrating multiple omics data including miRNA and mRNA expression profiles, is always hot topic. However, how to evaluate the repression effect on target genes integrating miRNA and mRNA expression profiles are not fully solved. In this study, we provide an analyzing method by integrating both miRNAs and mRNAs expression data simultaneously. Difference analysis was adopted based on the repression score, then significantly repressed mRNAs were screened out by DEGseq. Pathway analysis for the significantly repressed mRNAs shows that multiple pathways such as MAPK signaling pathway, TGF-beta signaling pathway and so on, may correlated to the colorectal cancer(CRC). Focusing on the MAPK signaling pathway, a miRNA-mRNA network that centering the cell fate genes was constructed. Finally, the miRNA-mRNAs that potentially important in the CRC carcinogenesis were screened out and scored by impact index. Copyright © 2018 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Jahn, Martin T.; Markert, Sebastian M.; Ryu, Taewoo; Ravasi, Timothy; Stigloher, Christian; Hentschel, Ute; Moitinho-Silva, Lucas
2016-10-01
Assigning functions to uncultivated environmental microorganisms continues to be a challenging endeavour. Here, we present a new microscopy protocol for fluorescence in situ hybridisation-correlative light and electron microscopy (FISH-CLEM) that enabled, to our knowledge for the first time, the identification of single cells within their complex microenvironment at electron microscopy resolution. Members of the candidate phylum Poribacteria, common and uncultivated symbionts of marine sponges, were used towards this goal. Cellular 3D reconstructions revealed bipolar, spherical granules of low electron density, which likely represent carbon reserves. Poribacterial activity profiles were retrieved from prokaryotic enriched sponge metatranscriptomes using simulation-based optimised mapping. We observed high transcriptional activity for proteins related to bacterial microcompartments (BMC) and we resolved their subcellular localisation by combining FISH-CLEM with immunohistochemistry (IHC) on ultra-thin sponge tissue sections. In terms of functional relevance, we propose that the BMC-A region may be involved in 1,2-propanediol degradation. The FISH-IHC-CLEM approach was proven an effective toolkit to combine -omics approaches with functional studies and it should be widely applicable in environmental microbiology.
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Linear Regression Links Transcriptomic Data and Cellular Raman Spectra.
Kobayashi-Kirschvink, Koseki J; Nakaoka, Hidenori; Oda, Arisa; Kamei, Ken-Ichiro F; Nosho, Kazuki; Fukushima, Hiroko; Kanesaki, Yu; Yajima, Shunsuke; Masaki, Haruhiko; Ohta, Kunihiro; Wakamoto, Yuichi
2018-06-08
Raman microscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra and transcriptomic profiles of Schizosaccharomyces pombe and Escherichia coli can be computationally connected and thus interpreted. We find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA sequencing can be reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Highly expressed non-coding RNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs in S. pombe. This demonstration of correspondence between cellular Raman spectra and transcriptomes is a promising step toward establishing spectroscopic live-cell omics studies. Copyright © 2018 Elsevier Inc. All rights reserved.
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Omics Approaches To Probe Microbiota and Drug Metabolism Interactions.
Nichols, Robert G; Hume, Nicole E; Smith, Philip B; Peters, Jeffrey M; Patterson, Andrew D
2016-12-19
The drug metabolism field has long recognized the beneficial and sometimes deleterious influence of microbiota in the absorption, distribution, metabolism, and excretion of drugs. Early pioneering work with the sulfanilamide precursor prontosil pointed toward the necessity not only to better understand the metabolic capabilities of the microbiota but also, importantly, to identify the specific microbiota involved in the generation and metabolism of drugs. However, technological limitations important for cataloging the microbiota community as well as for understanding and/or predicting their metabolic capabilities hindered progress. Current advances including mass spectrometry-based metabolite profiling as well as culture-independent sequence-based identification and functional analysis of microbiota have begun to shed light on microbial metabolism. In this review, case studies will be presented to highlight key aspects (e.g., microbiota identification, metabolic function and prediction, metabolite identification, and profiling) that have helped to clarify how the microbiota might impact or be impacted by drug metabolism. Lastly, a perspective of the future of this field is presented that takes into account what important knowledge is lacking and how to tackle these problems.
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Zhu, Qiuqiang; Yu, Shuguang; Chen, Guanshui; Ke, Lanlan; Pan, Daren
2017-01-01
The importance of leaf rolling in rice (Oryza sativa L.) has been widely recognized. Although several studies have investigated rice leaf rolling and identified some related genes, knowledge of the molecular mechanism underlying rice leaf rolling, especially outward leaf rolling, is limited. Therefore, in this study, differential proteomics and gene expression profiling were used to analyze rolled leaf mutant of transgenic rice in order to investigate differentially expressed genes and proteins related to rice leaf rolling. To this end, 28 differentially expressed proteins related to rolling leaf traits were isolated and identified. Digital expression profiling detected 10 genes related to rice leaf rolling. Some of the proteins and genes detected are involved in lipid metabolism, which is related to the development of bulliform cells, such as phosphoinositide phospholipase C, Mgll gene, and At4g26790 gene. The "omics"-level techniques were useful for simultaneously isolating several proteins and genes related to rice leaf rolling. In addition, the results of the analysis of differentially expressed proteins and genes were closely consistent with those from a corresponding functional analysis of cellular mechanisms; our study findings might form the basis for further research on the molecular mechanisms underlying rice leaf rolling.
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Recent "omics" advances in Helicobacter pylori.
Berthenet, Elvire; Sheppard, Sam; Vale, Filipa F
2016-09-01
The development of high-throughput whole genome sequencing (WGS) technologies is changing the face of microbiology, facilitating the comparison of large numbers of genomes from different lineages of a same organism. Our aim was to review the main advances on Helicobacter pylori "omics" and to understand how this is improving our knowledge of the biology, diversity and pathogenesis of H. pylori. Since the first H. pylori isolate was sequenced in 1997, 510 genomes have been deposited in the NCBI archive, providing a basis for improved understanding of the epidemiology and evolution of this important pathogen. This review focuses on works published between April 2015 and March 2016. Helicobacter "omics" is already making an impact and is a growing research field. Ultimately these advances will be translated into a routine clinical laboratory setting in order to improve public health. © 2016 John Wiley & Sons Ltd.
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Metabolomics in Sepsis and Its Impact on Public Health.
Evangelatos, Nikolaos; Bauer, Pia; Reumann, Matthias; Satyamoorthy, Kapaettu; Lehrach, Hans; Brand, Angela
2017-01-01
Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software. © 2018 S. Karger AG, Basel.
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Permafrost Meta-Omics and Climate Change
NASA Astrophysics Data System (ADS)
Mackelprang, Rachel; Saleska, Scott R.; Jacobsen, Carsten Suhr; Jansson, Janet K.; Taş, Neslihan
2016-06-01
Permanently frozen soil, or permafrost, covers a large portion of the Earth's terrestrial surface and represents a unique environment for cold-adapted microorganisms. As permafrost thaws, previously protected organic matter becomes available for microbial degradation. Microbes that decompose soil carbon produce carbon dioxide and other greenhouse gases, contributing substantially to climate change. Next-generation sequencing and other -omics technologies offer opportunities to discover the mechanisms by which microbial communities regulate the loss of carbon and the emission of greenhouse gases from thawing permafrost regions. Analysis of nucleic acids and proteins taken directly from permafrost-associated soils has provided new insights into microbial communities and their functions in Arctic environments that are increasingly impacted by climate change. In this article we review current information from various molecular -omics studies on permafrost microbial ecology and explore the relevance of these insights to our current understanding of the dynamics of permafrost loss due to climate change.
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The Omics Revolution in Agricultural Research.
Van Emon, Jeanette M
2016-01-13
The Agrochemicals Division cosponsored the 13th International Union of Pure and Applied Chemistry International Congress of Pesticide Chemistry held as part of the 248th National Meeting and Exposition of the American Chemical Society in San Francisco, CA, USA, August 10-14, 2014. The topic of the Congress was Crop, Environment, and Public Health Protection; Technologies for a Changing World. Over 1000 delegates participated in the Congress with interactive scientific programming in nine major topic areas including the challenges and opportunities of agricultural biotechnology. Plenary speakers addressed global issues related to the Congress theme prior to the daily technical sessions. The plenary lecture addressing the challenges and opportunities that omic technologies provide agricultural research is presented here. The plenary lecture provided the diverse audience with information on a complex subject to stimulate research ideas and provide a glimpse of the impact of omics on agricultural research.
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Omics on bioleaching: current and future impacts.
Martinez, Patricio; Vera, Mario; Bobadilla-Fazzini, Roberto A
2015-10-01
Bioleaching corresponds to the microbial-catalyzed process of conversion of insoluble metals into soluble forms. As an applied biotechnology globally used, it represents an extremely interesting field of research where omics techniques can be applied in terms of knowledge development, but moreover in terms of process design, control, and optimization. In this mini-review, the current state of genomics, proteomics, and metabolomics of bioleaching and the major impacts of these analytical methods at industrial scale are highlighted. In summary, genomics has been essential in the determination of the biodiversity of leaching processes and for development of conceptual and functional metabolic models. Proteomic impacts are mostly related to microbe-mineral interaction analysis, including copper resistance and biofilm formation. Early steps of metabolomics in the field of bioleaching have shown a significant potential for the use of metabolites as industrial biomarkers. Development directions are given in order to enhance the future impacts of the omics in biohydrometallurgy.
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Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches
NASA Astrophysics Data System (ADS)
Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.
2016-02-01
Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.
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Fluxomics - connecting 'omics analysis and phenotypes.
Winter, Gal; Krömer, Jens O
2013-07-01
In our modern 'omics era, metabolic flux analysis (fluxomics) represents the physiological counterpart of its siblings transcriptomics, proteomics and metabolomics. Fluxomics integrates in vivo measurements of metabolic fluxes with stoichiometric network models to allow the determination of absolute flux through large networks of the central carbon metabolism. There are many approaches to implement fluxomics including flux balance analysis (FBA), (13) C fluxomics and (13) C-constrained FBA as well as many experimental settings for flux measurement including dynamic, stationary and semi-stationary. Here we outline the principles of the different approaches and their relative advantages. We demonstrate the unique contribution of flux analysis for phenotype elucidation using a thoroughly studied metabolic reaction as a case study, the microbial aerobic/anaerobic shift, highlighting the importance of flux analysis as a single layer of data as well as interlaced in multi-omics studies. © 2012 John Wiley & Sons Ltd and Society for Applied Microbiology.
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Dimension reduction techniques for the integrative analysis of multi-omics data
Zeleznik, Oana A.; Thallinger, Gerhard G.; Kuster, Bernhard; Gholami, Amin M.
2016-01-01
State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease. PMID:26969681
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Tsurumaki, M; Kotake, M; Iwasaki, M; Saito, M; Tanaka, K; Aw, W; Fukuda, S; Tomita, M
2015-01-01
Inulin, a natural renewable polysaccharide resource produced by various plants in nature, has been reported to possess a significant number of diverse pharmaceutical and food applications. Recently, there has been rapid progress in high-throughput technologies and platforms to assay global mRNA, proteins, metabolites and gut microbiota. In this review, we will describe the current status of utilizing omics technologies of elucidating the impact of inulin and inulin-containing prebiotics at the transcriptome, proteome, metabolome and gut microbiome levels. Although many studies in this review have addressed the impact of inulin comprehensively, these omics technologies only enable us to understand physiological information at each different stage of mRNA, protein, metabolite and gut microbe. We believe that a synergistic approach is vital in order to fully illustrate the intricate beauty behind the relatively modest influence of food factors like inulin on host health. PMID:26619369
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Chang, Cheng; Xu, Kaikun; Guo, Chaoping; Wang, Jinxia; Yan, Qi; Zhang, Jian; He, Fuchu; Zhu, Yunping
2018-05-22
Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data. PANDA-view is freely available at https://sourceforge.net/projects/panda-view/. 1987ccpacer@163.com and zhuyunping@gmail.com. Supplementary data are available at Bioinformatics online.
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Growing trend of CE at the omics level: the frontier of systems biology--an update.
Ban, Eunmi; Park, Soo Hyun; Kang, Min-Jung; Lee, Hyun-Jung; Song, Eun Joo; Yoo, Young Sook
2012-01-01
Omics is the study of proteins, peptides, genes, and metabolites in living organisms. Systems biology aims to understand the system through the study of the relationship between elements such as genes and proteins in biological system. Recently, systems biology emerged as the result of the advanced development of high-throughput analysis technologies such as DNA sequencers, DNA arrays, and mass spectrometry for omics studies. Among a number of analytical tools and technologies, CE and CE coupled to MS are promising and relatively rapidly developing tools with the potential to provide qualitative and quantitative analyses of biological molecules. With an emphasis on CE for systems biology, this review summarizes the method developments and applications of CE for the genomic, transcriptomic, proteomic, and metabolomic studies focusing on the drug discovery and disease diagnosis and therapies since 2009. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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The Omics Revolution in Agricultural Research
2015-01-01
The Agrochemicals Division cosponsored the 13th International Union of Pure and Applied Chemistry International Congress of Pesticide Chemistry held as part of the 248th National Meeting and Exposition of the American Chemical Society in San Francisco, CA, USA, August 10–14, 2014. The topic of the Congress was Crop, Environment, and Public Health Protection; Technologies for a Changing World. Over 1000 delegates participated in the Congress with interactive scientific programming in nine major topic areas including the challenges and opportunities of agricultural biotechnology. Plenary speakers addressed global issues related to the Congress theme prior to the daily technical sessions. The plenary lecture addressing the challenges and opportunities that omic technologies provide agricultural research is presented here. The plenary lecture provided the diverse audience with information on a complex subject to stimulate research ideas and provide a glimpse of the impact of omics on agricultural research. PMID:26468989
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Personality traits in patients with Parkinson's disease: assessment and clinical implications.
Poletti, Michele; Bonuccelli, Ubaldo
2012-06-01
This study reviews empirical evidence on the association between personality traits and Parkinson's disease (PD), with a twofold aim. First, to better identify non-motor symptoms, such as affective symptoms and personality changes, that could help to define the pre-motor phase of PD; second, to better understand the neurobiological bases of personality traits, a goal that is not fully accomplished by a purely anatomical approach. A literature review was performed on studies of personality traits in PD patients, in electronic databases ISI Web of Knowledge, Medline and PsychInfo, conducted in July 2011. We found evidence that the existence of a characteristic premorbid personality profile of PD patients is not actually sustained by robust empirical evidence, mainly due to the methodological bias of the retrospective assessment of personality; PD patients present a personality profile of low novelty seeking and high harm avoidance. We concluded that the definition of a pre-motor phase of PD, based on non-motor symptoms, should search for the presence of concomitant affective disorders and for a positive psychiatric history for affective disorders rather than for a typical personality profile or personality changes. The low novelty seeking profile is probably related to the dopaminergic deficit, while the high harm avoidance profile is probably associated with the presence of affective disorders. Clinical implications of these findings, in regard to personality assessment and pharmacological treatments in PD, are also discussed.
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Shafiee-Kandjani, Ali Reza; Amiri, Shahrokh; Arfaie, Asghar; Ahmadi, Azadeh; Farvareshi, Mahmoud
2014-01-01
Objectives. Inflexible personality traits play an important role in the development of maladaptive behaviors among patients who attempt suicide. This study was conducted to investigate the relationship between personality profiles and suicide attempt via medicine poisoning among the patients hospitalized in a public hospital. Materials and Methods. Fifty-nine patients who attempted suicide for the first time and hospitalized in the poisoning ward were selected as the experimental group. Sixty-three patients hospitalized in the other wards for a variety of reasons were selected as the adjusted control group. Millon Clinical Multiaxial Personality Inventory, 3rd version (MCMI-III) was used to assess the personality profiles. Results. The majority of the suicide attempters were low-level graduates (67.8% versus 47.1%, OR = 2.36). 79.7% of the suicide attempters were suffering from at least one maladaptive personality profile. The most common maladaptive personality profiles among the suicide attempters were depressive personality disorder (40.7%) and histrionic personality disorder (32.2%). Among the syndromes the most common ones were anxiety clinical syndrome (23.7%) and major depression (23.7%). Conclusion. Major depression clinical syndrome, histrionic personality disorder, anxiety clinical syndrome, and depressive personality disorder are among the predicators of first suicide attempts for the patients hospitalized in the public hospital due to the medicine poisoning. PMID:27433491
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Pilot Personality Profile Using the NEO-PI-R
NASA Technical Reports Server (NTRS)
Fitzgibbons, Amy; Davis, Donald; Schutte, Paul C.
2004-01-01
This paper recounts the qualitative research conducted to determine if a general personality measure would provide a personality profile for commercial aviation pilots. The researchers investigated a widely used general personality inventory, the NEO-PI-R, with 93 pilots. The results indicate that a 'pilot personality' does exist. Future research and implications are discussed.
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Pilot Personality Profile Using the NEO-PI-R
NASA Technical Reports Server (NTRS)
Fitzgibbons, Amy; Davis, Don; Schutte, Paul C. (Technical Monitor)
2000-01-01
This paper recounts the qualitative research conducted to determine if a general personality measure would provide a personality profile for commercial aviation pilots. The researchers investigated a widely used general personality inventory, the NEO-PI-R, with 93 pilots. The results indicate that a "pilot personality" does exist. Future research and implications are discussed.
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Precision medicine for managing chronic diseases.
Śliwczynski, Andrzej; Orlewska, Ewa
2016-08-18
Precision medicine (PM) is an important modern paradigm for combining new types of metrics with big medical datasets to create prediction models for prevention, diagnosis, and specific therapy of chronic diseases. The aim of this paper was to differentiate PM from personalized medicine, to show potential benefits of PM for managing chronic diseases, and to define problems with implementation of PM into clinical practice. PM strategies in chronic airway diseases, diabetes, and cardiovascular diseases show that the key to developing PM is the addition of big datasets to the course of individually profiling diseases and patients. Integration of PM into clinical practice requires the reengineering of the health care infrastructure by incorporating necessary tools for clinicians and patients to enable data collection and analysis, interpretation of the results, as well as to facilitate treatment choices based on new understanding of biological pathways. The size of datasets and their large variability pose a considerable technical and statistical challenge. The potential benefits of using PM are as follows: 1) broader possibilities for physicians to use the achievements of genomics, proteomics, metabolomics, and other "omics" disciplines in routine clinical practice; 2) better understanding of the pathogenesis and epidemiology of diseases; 3) a revised approach to prevention, diagnosis, and treatment of chronic diseases; 4) better integration of electronic medical records as well as data from sensors and software applications in an interactive network of knowledge aimed at improving the modelling and testing of therapeutic and preventative strategies, stimulating further research, and spreading information to the general public.
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Coulombe, Simon; Radziszewski, Stephanie; Meunier, Sophie; Provencher, Hélène; Hudon, Catherine; Roberge, Pasquale; Provencher, Martin D.; Houle, Janie
2016-01-01
Context: A shift toward person-centered care has been occurring in services provided to people with mood and anxiety disorders. Recovery is recognized as encompassing personal aspects in addition to clinical ones. Guidelines now recommend supporting people's engagement in self-management as a complementary recovery avenue. Yet the literature lacks evidence on how individualized combinations of self-management strategies used by people relate to their clinical and personal recovery indicators. Objectives: The aims of this study were to identify profiles underlying mental health recovery, describe the characteristics of participants corresponding to each profile, and examine the associations of profiles with criterion variables. Method: 149 people recovering from anxiety, depressive, or bipolar disorders completed questionnaires on self-management, clinical recovery (symptom severity), personal recovery (positive mental health), and criterion variables (personal goal appraisal, social participation, self-care abilities, coping). Results: Latent profile analysis (LPA) revealed three profiles. The Floundering profile included participants who rarely used self-management strategies and had moderately severe symptoms and the lowest positive mental health. The Flourishing profile was characterized by frequent use of self-empowerment strategies, the least severe symptoms, and the highest positive mental health. Participants in the Struggling profile engaged actively in several self-management strategies focused on symptom reduction and healthy lifestyle. They concomitantly reported high symptom severity and moderately high positive mental health. The study revealed that Floundering was associated with higher probabilities of being a man, being single, and having a low income. People in the Flourishing profile had the most favorable scores on criterion variables, supporting the profiles' construct validity. Discussion: The mixed portrait of Struggling participants on recovery indicators suggests the relationship between health engagement and recovery is more intricate than anticipated. Practitioners should strive for a holistic understanding of their clients' self-management strategies and recovery indicators to provide support personalized to their profile. While people presenting risk factors would benefit from person-centered support, societal efforts are needed in the long term to reduce global health inequalities. The integration of constructs from diverse fields (patient-centered care, chronic illness, positive psychology) and the use of person-oriented analysis yielded new insights into people's engagement in their health and well-being. PMID:27199819
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The role of personality in aggressive behaviour among individuals with intellectual disabilities.
Chaïb, L S; Crocker, A G
2014-11-01
Aggressive behaviour is associated with certain personality traits in both the general population and among individuals with mental health problems, but little attention has been paid to the relationship between aggressive behaviour and personality among individuals with intellectual disabilities (ID). The aim of this study was to circumscribe personality profiles associated with aggressive behaviour among individuals with ID. In this cross-sectional study of 296 adults with mild or moderate ID, information on mental health, personality and aggressive behaviour was gathered through structured interviews with the ID participants and their case manager, and a review of client files. The results of the Reiss Profile were submitted to hierarchical cluster analysis method. Subsequently, the distribution of aggressive behaviour, sociodemographic characteristics and clinical characteristics across personality profiles was analysed. The analyses yielded seven distinct personality profiles in relation to patterns of aggressive behaviour: Pacifists, Socials, Confidents, Altruists, Conformists, Emotionals and Asocials. The identification of distinct personality profiles sheds light on the risk factors for aggressive behaviour, and suggests new approaches to improving diagnostic and intervention strategies. © 2013 The Authors. Journal of Intellectual Disability Research © 2013 John Wiley & Sons Ltd, MENCAP & IASSIDD.
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Stirring the motivational soup: within-person latent profiles of motivation in exercise.
Lindwall, Magnus; Ivarsson, Andreas; Weman-Josefsson, Karin; Jonsson, Linus; Ntoumanis, Nikos; Patrick, Heather; Thøgersen-Ntoumani, Cecilie; Markland, David; Teixeira, Pedro
2017-01-14
The purpose of the present study was to use a person-oriented analytical approach to identify latent motivational profiles, based on the different behavioural regulations for exercise, and to examine differences in satisfaction of basic psychological needs (competence, autonomy and relatedness) and exercise behaviour across these motivational profiles. Two samples, consisting of 1084 and 511 adults respectively, completed exercise-related measures of behavioural regulation and psychological need satisfaction as well as exercise behaviour. Latent profile analyses were used to identify motivational profiles. Six profiles, representing different combinations of regulations for exercise, were found to best represent data in both samples. Some profiles were found in both samples (e.g., low motivation profile, self-determined motivation profile and self-determined with high introjected regulation profile), whereas others were unique to each sample. In line with the Self-Determination Theory, individuals belonging to more self-determined profiles demonstrated higher scores on need satisfaction. The results support the notions of motivation being a multidimensional construct and that people have different, sometimes competing, reasons for engaging in exercise. The benefits of using person-oriented analyses to examine within-person interactions of motivation and different regulations are discussed.
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Eley, Diann S; Leung, Janni K; Campbell, Narelle; Cloninger, C Robert
2017-05-01
Resilience, coping with uncertainty and learning from mistakes are vital characteristics for all medical disciplines - particularly rural practice. Levels of coping constructs were examined in medical students with and without a rural background or an interest in rural practice. Cross-sectional surveys identified two personality profiles, and their association with levels of Tolerance of Ambiguity, Resilience, Perfectionism-High Standards and Concern over mistakes as constructs indicative of coping. Medical students (N = 797) were stratified by rural background and degree of rural interest. Mediation analysis tested the effect of personality profile on levels of the coping constructs. More (72%) rural background students had Profile 1 which was associated with higher levels of Tolerance of Ambiguity, High standards, and Resilience, but lower Concern over mistakes. Non-rural background students reporting a strong rural interest also had Profile 1 (64%) and similar levels of coping constructs. Personality profile mediated the association between rural interest and levels of coping constructs regardless of background. Having a rural background or strong rural interest are associated with a personality profile that indicates a better capacity for coping. Personality may play a part in an individual's interest in rural practice. Rural workforce initiatives through education should encourage and nurture students with a genuine interest in rural practice - regardless of background.
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Note on concurrent validation of the personality assessment inventory in law enforcement.
Hays, J R
1997-08-01
This study compared the Personality Assessment Inventory and MMPI-168 profiles of 9 law enforcement applicants with published MMPI profiles to provide concurrent validation for the use of the Personality Assessment Inventory to assess personality pathology of peace officer applicants. The sample showed subclinical elevations of the Positive Impression and Treatment Rejection scales on the Personality Assessment Inventory and subclinical elevations on the MMPI validity scales of Lie and Correction and the clinical scales of Psychopathic Deviate and Hypomania. The applicants' mean MMPI profile provided concurrent validation for the use of the Personality Assessment Inventory in this decision on fitness to serve.
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He, Yongqun
2011-01-01
Brucella is a Gram-negative, facultative intracellular bacterium that causes zoonotic brucellosis in humans and various animals. Out of 10 classified Brucella species, B. melitensis, B. abortus, B. suis, and B. canis are pathogenic to humans. In the past decade, the mechanisms of Brucella pathogenesis and host immunity have been extensively investigated using the cutting edge systems biology and bioinformatics approaches. This article provides a comprehensive review of the applications of Omics (including genomics, transcriptomics, and proteomics) and bioinformatics technologies for the analysis of Brucella pathogenesis, host immune responses, and vaccine targets. Based on more than 30 sequenced Brucella genomes, comparative genomics is able to identify gene variations among Brucella strains that help to explain host specificity and virulence differences among Brucella species. Diverse transcriptomics and proteomics gene expression studies have been conducted to analyze gene expression profiles of wild type Brucella strains and mutants under different laboratory conditions. High throughput Omics analyses of host responses to infections with virulent or attenuated Brucella strains have been focused on responses by mouse and cattle macrophages, bovine trophoblastic cells, mouse and boar splenocytes, and ram buffy coat. Differential serum responses in humans and rams to Brucella infections have been analyzed using high throughput serum antibody screening technology. The Vaxign reverse vaccinology has been used to predict many Brucella vaccine targets. More than 180 Brucella virulence factors and their gene interaction networks have been identified using advanced literature mining methods. The recent development of community-based Vaccine Ontology and Brucellosis Ontology provides an efficient way for Brucella data integration, exchange, and computer-assisted automated reasoning. PMID:22919594
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Hinkson, Izumi V.; Davidsen, Tanja M.; Klemm, Juli D.; Chandramouliswaran, Ishwar; Kerlavage, Anthony R.; Kibbe, Warren A.
2017-01-01
Advancements in next-generation sequencing and other -omics technologies are accelerating the detailed molecular characterization of individual patient tumors, and driving the evolution of precision medicine. Cancer is no longer considered a single disease, but rather, a diverse array of diseases wherein each patient has a unique collection of germline variants and somatic mutations. Molecular profiling of patient-derived samples has led to a data explosion that could help us understand the contributions of environment and germline to risk, therapeutic response, and outcome. To maximize the value of these data, an interdisciplinary approach is paramount. The National Cancer Institute (NCI) has initiated multiple projects to characterize tumor samples using multi-omic approaches. These projects harness the expertise of clinicians, biologists, computer scientists, and software engineers to investigate cancer biology and therapeutic response in multidisciplinary teams. Petabytes of cancer genomic, transcriptomic, epigenomic, proteomic, and imaging data have been generated by these projects. To address the data analysis challenges associated with these large datasets, the NCI has sponsored the development of the Genomic Data Commons (GDC) and three Cloud Resources. The GDC ensures data and metadata quality, ingests and harmonizes genomic data, and securely redistributes the data. During its pilot phase, the Cloud Resources tested multiple cloud-based approaches for enhancing data access, collaboration, computational scalability, resource democratization, and reproducibility. These NCI-led efforts are continuously being refined to better support open data practices and precision oncology, and to serve as building blocks of the NCI Cancer Research Data Commons. PMID:28983483
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He, Yongqun
2012-01-01
Brucella is a Gram-negative, facultative intracellular bacterium that causes zoonotic brucellosis in humans and various animals. Out of 10 classified Brucella species, B. melitensis, B. abortus, B. suis, and B. canis are pathogenic to humans. In the past decade, the mechanisms of Brucella pathogenesis and host immunity have been extensively investigated using the cutting edge systems biology and bioinformatics approaches. This article provides a comprehensive review of the applications of Omics (including genomics, transcriptomics, and proteomics) and bioinformatics technologies for the analysis of Brucella pathogenesis, host immune responses, and vaccine targets. Based on more than 30 sequenced Brucella genomes, comparative genomics is able to identify gene variations among Brucella strains that help to explain host specificity and virulence differences among Brucella species. Diverse transcriptomics and proteomics gene expression studies have been conducted to analyze gene expression profiles of wild type Brucella strains and mutants under different laboratory conditions. High throughput Omics analyses of host responses to infections with virulent or attenuated Brucella strains have been focused on responses by mouse and cattle macrophages, bovine trophoblastic cells, mouse and boar splenocytes, and ram buffy coat. Differential serum responses in humans and rams to Brucella infections have been analyzed using high throughput serum antibody screening technology. The Vaxign reverse vaccinology has been used to predict many Brucella vaccine targets. More than 180 Brucella virulence factors and their gene interaction networks have been identified using advanced literature mining methods. The recent development of community-based Vaccine Ontology and Brucellosis Ontology provides an efficient way for Brucella data integration, exchange, and computer-assisted automated reasoning.
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Takahashi, Shoko; Saito, Kenji; Jia, Huijuan; Kato, Hisanori
2014-01-01
Many epidemiological studies have indicated that coffee consumption may reduce the risks of developing obesity and diabetes, but the underlying mechanisms of these effects are poorly understood. Our previous study revealed the changes on gene expression profiles in the livers of C57BL/6J mice fed a high-fat diet containing three types of coffee (caffeinated, decaffeinated and green unroasted coffee), using DNA microarrays. The results revealed remarkable alterations in lipid metabolism-related molecules which may be involved in the anti-obesity effects of coffee. We conducted the present study to further elucidate the metabolic alterations underlying the effects of coffee consumption through comprehensive proteomic and metabolomic analyses. Proteomics revealed an up-regulation of isocitrate dehydrogenase (a key enzyme in the TCA cycle) and its related proteins, suggesting increased energy generation. The metabolomics showed an up-regulation of metabolites involved in the urea cycle, with which the transcriptome data were highly consistent, indicating accelerated energy expenditure. The TCA cycle and the urea cycle are likely be accelerated in a concerted manner, since they are directly connected by mutually providing each other's intermediates. The up-regulation of these pathways might result in a metabolic shift causing increased ATP turnover, which is related to the alterations of lipid metabolism. This mechanism may play an important part in the suppressive effects of coffee consumption on obesity, inflammation, and hepatosteatosis. This study newly revealed global metabolic alterations induced by coffee intake, providing significant insights into the association between coffee intake and the prevention of type 2 diabetes, utilizing the benefits of multi-omics analyses. PMID:24618914
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Integrative methods for analyzing big data in precision medicine.
Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša
2016-03-01
We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Systems biology-embedded target validation: improving efficacy in drug discovery.
Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter
2014-01-01
The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.
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El Karkouri, Khalid; Kowalczewska, Malgorzata; Armstrong, Nicholas; Azza, Said; Fournier, Pierre-Edouard; Raoult, Didier
2017-01-01
Arthropod-borne Rickettsia species are obligate intracellular bacteria which are pathogenic for humans. Within this genus, Rickettsia slovaca and Rickettsia conorii cause frequent and potentially severe infections, whereas Rickettsia raoultii and Rickettsia massiliae cause rare and milder infections. All four species belong to spotted fever group (SFG) rickettsiae. However, R. slovaca and R. raoultii cause scalp eschar and neck lymphadenopathy (SENLAT) and are mainly associated with Dermacentor ticks, whereas the other two species cause Mediterranean spotted fever (MSF) and are mainly transmitted by Rhipicephalus ticks. To identify the potential genes and protein profiles and to understand the evolutionary processes that could, comprehensively, relate to the differences in virulence and pathogenicity observed between these four species, we compared their genomes and proteomes. The virulent and milder agents displayed divergent phylogenomic evolution in two major clades, whereas either SENLAT or MSF disease suggests a discrete convergent evolution of one virulent and one milder agent, despite their distant genetic relatedness. Moreover, the two virulent species underwent strong reductive genomic evolution and protein structural variations, as well as a probable loss of plasmid(s), compared to the two milder species. However, an abundance of mobilome genes was observed only in the less pathogenic species. After infecting Xenopus laevis cells, the virulent agents displayed less up-regulated than down-regulated proteins, as well as less number of identified core proteins. Furthermore, their similar and distinct protein profiles did not contain some genes (e.g., ompA/B and rickA) known to be related to rickettsial adhesion, motility and/or virulence, but may include other putative virulence-, antivirulence-, and/or disease-related proteins. The identified evolutionary forces herein may have a strong impact on intracellular expressions and strategies in these rickettsiae, and that may contribute to the emergence of distinct virulence and diseases in humans. Thus, the current multi-omics data provide new insights into the evolution and fitness of SFG virulence and pathogenicity, and intracellular pathogenic bacteria. PMID:28775717
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A holistic approach to study the effects of natural antioxidants on inflammation and liver cancer.
Costantini, Susan; Colonna, Giovanni; Castello, Giuseppe
2014-01-01
The limited effectiveness of chemotherapy and the high recurrence rate of cancers highlight the urgent need to identify new molecular targets and to develop new treatments. Numerous epidemiological studies have recently highlighted the existence of an inverse association between fruit and vegetable consumption, natural antioxidants, and cancer risk; in fact, antioxidant intake through diet or supplements of plant origin is strongly recommended for cancer prevention and cure. In general, antioxidants are substances of vegetable, mineral, or animal origin that neutralize free radicals and protect the body from their negative actions on the plasma membrane, proteins, and DNA. Hence, cancer can be prevented by the stimulation of the immune system to destroy cancer cells or to block their proliferation. Since living organisms may be studied as a whole complex system by the "omics sciences" which tend toward understanding and describing the global information of genes, mRNA, proteins, and metabolites, our aim is to use bioinformatics and systems biology to study cytokinome, which plays an important role in the evolution of inflammatory processes and is also a key component in the evolution of cancer, a disease recognized as depending on chronic inflammation and also with the concomitant presence of type 2 diabetes and obesity. On the whole, we define cytokinome as the totality of these proteins and their interactions in and around biological cells. Understanding the complex interaction network of cytokines in patients affected by cancers should be very useful both to follow the evolution of cancer from its early stages and to define innovative therapeutic strategies by using systems biology approaches. In this paper, we review some results of our group in the light of the "omics" logic, and in particular (1) the need for a global approach to study complex systems such as multifactorial cancer and, in particular, hepatocellular carcinoma, (2) the correlation between natural antioxidants, inflammation, and liver cancer, (3) the challenge and significance of the cytokinome profile, (4) the evaluation of the cytokinome profile of patients with type 2 diabetes and/or chronic hepatitis C infection, and (5) adipokine interactome.
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Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering.
Groen, Nathalie; Guvendiren, Murat; Rabitz, Herschel; Welsh, William J; Kohn, Joachim; de Boer, Jan
2016-04-01
The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. Copyright © 2016. Published by Elsevier Ltd.
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Reconstruction of genome-scale human metabolic models using omics data.
Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup
2015-08-01
The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.
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Shen, Yao; Stanislauskas, Milda; Li, Gen; Zheng, Deyou; Liu, Liang
2017-01-01
To elucidate the complex molecular mechanisms underlying the adverse effects UV radiation (UVR) on skin homeostasis, we performed multi-omics studies to characterize UV-induced genetic and epigenetic changes. Human keratinocytes from a single donor treated with or without UVR were analyzed by RNA-seq, exome-seq, and H3K27ac ChIP-seq at 4 h and 72 h following UVR. Compared to the relatively moderate mutagenic effects of UVR, acute UV exposure induced substantial epigenomic and transcriptomic alterations, illuminating a previously underappreciated role of epigenomic and transcriptomic instability in skin pathogenesis. Integration of the multi-omics data revealed that UVR-induced transcriptional dysregulation of a subset of genes was attributable to either genetic mutations or global redistribution of H3K27ac. H3K27ac redistribution further led to the formation of distinctive super enhancers in UV-irradiated cells. Our analysis also identified several new UV target genes, including CYP24A1, GJA5, SLAMF7 and ETV1, which were frequently dysregulated in human squamous cell carcinomas, highlighting their potential as new molecular targets for prevention or treatment of UVR-induced skin cancers. Taken together, our concurrent multi-omics analyses provide new mechanistic insights into the complex molecular networks underlying UV photobiological effects, which have important implications in understanding its impact on skin homeostasis and pathogenesis. PMID:28211524
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NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies.
Beheshti, Afshin; Miller, Jack; Kidane, Yared; Berrios, Daniel; Gebre, Samrawit G; Costes, Sylvain V
2018-06-01
Accurate assessment of risks of long-term space missions is critical for human space exploration. It is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from galactic cosmic rays (GCR) is a major health risk factor for astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently, there are gaps in our knowledge of the health risks associated with chronic low-dose, low-dose-rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The NASA GeneLab project ( https://genelab.nasa.gov/ ) aims to provide a detailed library of omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information on radiation exposure for ground-based studies, GeneLab is adding detailed, curated dosimetry information for spaceflight experiments. GeneLab is the first comprehensive omics database for space-related research from which an investigator can generate hypotheses to direct future experiments, utilizing both ground and space biological radiation data. The GLDS is continually expanding as omics-related data are generated by the space life sciences community. Here we provide a brief summary of the space radiation-related data available at GeneLab.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Boaro, Amy A.; Kim, Young-Mo; Konopka, Allan
2014-12-01
Integrated ‘omics have been used on pure cultures and co-cultures, yet they have not been applied to complex microbial communities to examine questions of perturbation response. In this study, we used integrated ‘omics to measure the perturbation response of a cellulose-degrading bioreactor community fed with microcrystalline cellulose (Avicel). We predicted that a pH decrease by addition of a pulse of acid would reduce microbial community diversity and temporarily reduce reactor function such as cellulose degradation. However, 16S rDNA pyrosequencing results revealed increased alpha diversity in the microbial community after the perturbation, and a persistence of the dominant community members overmore » the duration of the experiment. Proteomics results showed a decrease in activity of proteins associated with Fibrobacter succinogenes two days after the perturbation followed by increased protein abundances six days after the perturbation. The decrease in cellulolytic activity suggested by the proteomics was confirmed by the accumulation of Avicel in the reactor. Metabolomics showed a pattern similar to that of the proteome, with amino acid production decreasing two days after the perturbation and increasing after six days. This study demonstrated that community ‘omics data provides valuable information about the interactions and function of anaerobic cellulolytic community members after a perturbation.« less
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Framework for the quantitative weight-of-evidence analysis of 'omics data for regulatory purposes.
Bridges, Jim; Sauer, Ursula G; Buesen, Roland; Deferme, Lize; Tollefsen, Knut E; Tralau, Tewes; van Ravenzwaay, Ben; Poole, Alan; Pemberton, Mark
2017-12-01
A framework for the quantitative weight-of-evidence (QWoE) analysis of 'omics data for regulatory purposes is presented. The QWoE framework encompasses seven steps to evaluate 'omics data (also together with non-'omics data): (1) Hypothesis formulation, identification and weighting of lines of evidence (LoEs). LoEs conjoin different (types of) studies that are used to critically test the hypothesis. As an essential component of the QWoE framework, step 1 includes the development of templates for scoring sheets that predefine scoring criteria with scores of 0-4 to enable a quantitative determination of study quality and data relevance; (2) literature searches and categorisation of studies into the pre-defined LoEs; (3) and (4) quantitative assessment of study quality and data relevance using the respective pre-defined scoring sheets for each study; (5) evaluation of LoE-specific strength of evidence based upon the study quality and study relevance scores of the studies conjoined in the respective LoE; (6) integration of the strength of evidence from the individual LoEs to determine the overall strength of evidence; (7) characterisation of uncertainties and conclusion on the QWoE. To put the QWoE framework in practice, case studies are recommended to confirm the relevance of its different steps, or to adapt them as necessary. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
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Mathematical modeling for novel cancer drug discovery and development.
Zhang, Ping; Brusic, Vladimir
2014-10-01
Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.
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Next generation microbiological risk assessment-Potential of omics data for hazard characterisation.
Haddad, Nabila; Johnson, Nick; Kathariou, Sophia; Métris, Aline; Phister, Trevor; Pielaat, Annemarie; Tassou, Chrysoula; Wells-Bennik, Marjon H J; Zwietering, Marcel H
2018-04-12
According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of hazard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
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The current status and problems confronted in delivering precision medicine in Japan and Europe.
Bando, Hideaki
Precision medicine has been defined as "a predictive, preventive, personalized, and participatory health care service delivery model." Today, developments in next-generation sequencing and information technology have made precision medicine possible, with massive amounts of genetic, "omics," clinical, environmental, and lifestyle data now available. Unfortunately, differences in governmental support and health care regulations have resulted in heterogeneous progress among countries. In Japan, for example, precision cancer screening and treatments are increasingly being promoted, with collaboration among research, governmental, and pharmaceutical agencies taking place in the nationwide SCRUM-Japan cancer genome screening project. The missions of SCRUM-Japan are to deliver the most appropriate therapeutic agents to the most suitable patients, and to play key roles in the development of multiplex diagnostic products and new indications for targeted therapy. Starting in February 2015 and ending in March 2017, the aim is to enroll 4750 patients with cancer (2350 patients with lung cancer and 2400 patients with gastrointestinal tract cancer). Compared with other developed countries, investments in scientific innovation for biomedical and omics research are matched or even surpassed in Europe, but regulatory differences in each countries are a major hurdle to rapid implementation. Although market approval for pharmaceuticals is centralized through the European Medicines Agency, access to health care is heterogeneously regulated at national levels, which undermines the consistency, comparability, and quality of precision medicine for cancer patients in Europe. In this review, we focus on the current progress of precision medicine in Japan and Europe, and clarify the differences in progress and the hurdles faced moving forward. Copyright © 2017 Elsevier Inc. All rights reserved.
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[Personality profile among cocaine users].
Sánchez Huesca, R; Guisa Cruz, V M; Cedillo González, A; Pascual Blanco, Y
2002-01-01
Due to the psychiatric comorbidity seen among cocaine addicts, it is of clinical interest to know the personality traits associated to the use of this substance. Personality-profile comparative study of cocaine users and multiple-substance users obtained through the Multistage Personality Inventory. The study analyzed a sample of 30 cocaine users and 26 users of various substances who asked for treatment at a specialized institution. Results show the same profile for both groups, with high 8-4-2 scales. According to the Multistage Personality Inventory, this profile corresponds to an antisocial personality disorder with depressive and schizoid traits. The fact that there is a single profile for different drug users leads us to the hypothesis that there are addictive personality characteristics rather than specific traits related to the use of each substance. These subjects' personality characteristics suggest that the fear to relate to others could make it very difficult to establish a therapeutic link. This, in addition to the acting up tendency seen among users, constitutes a call of alert in terms of their likely abandonment of treatment. Further more, as they take impulses into actions, they build a barrier before words. This could be called acting up, doing instead of saying, which can become an obstacle for the appropriate development of the therapeutic process. The result must consider the size of the sample.
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Why proteomics is not the new genomics and the future of mass spectrometry in cell biology.
Sidoli, Simone; Kulej, Katarzyna; Garcia, Benjamin A
2017-01-02
Mass spectrometry (MS) is an essential part of the cell biologist's proteomics toolkit, allowing analyses at molecular and system-wide scales. However, proteomics still lag behind genomics in popularity and ease of use. We discuss key differences between MS-based -omics and other booming -omics technologies and highlight what we view as the future of MS and its role in our increasingly deep understanding of cell biology. © 2017 Sidoli et al.
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2017-05-02
iss051e034105 (May 2, 2017) --- Commander Peggy Whitson is working on the OsteoOmics bone cell study that utilizes the Microgravity Science Glovebox inside the U.S. Destiny laboratory. OsteoOmics investigates the molecular mechanisms that dictate bone loss in microgravity by examining osteoblasts, which form bone, and osteoclasts, which dissolves bone. This leads to better preventative care or therapeutic treatments for people suffering bone loss as a result of bone diseases like osteopenia and osteoporosis, or for patients on prolonged bed rest.
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2016-12-01
acids (PUFA) on cerebral neurobiology: an integrated omics approach with epigenomic focus Nabarun Chakrabortya,b, Seid Muhiea,b, Raina Kumara,c, Aarti...C57BL/6j mice fed on any of these three diets from their neonatal age to midlife. Integrating the multiomics data, we focused on the genes encoding both...been evaluated in the context of a wide variety of health issues [23]. The escalated risks of pathological and psychological disease have been
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The -omics Era- Toward a Systems-Level Understanding of Streptomyces
Zhou, Zhan; Gu, Jianying; Du, Yi-Ling; Li, Yong-Quan; Wang, Yufeng
2011-01-01
Streptomyces is a group of soil bacteria of medicinal, economic, ecological, and industrial importance. It is renowned for its complex biology in gene regulation, antibiotic production, morphological differentiation, and stress response. In this review, we provide an overview of the recent advances in Streptomyces biology inspired by -omics based high throughput technologies. In this post-genomic era, vast amounts of data have been integrated to provide significant new insights into the fundamental mechanisms of system control and regulation dynamics of Streptomyces. PMID:22379394
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Automatic Tool for Local Assembly Structures
DOE Office of Scientific and Technical Information (OSTI.GOV)
Whole community shotgun sequencing of total DNA (i.e. metagenomics) and total RNA (i.e. metatranscriptomics) has provided a wealth of information in the microbial community structure, predicted functions, metabolic networks, and is even able to reconstruct complete genomes directly. Here we present ATLAS (Automatic Tool for Local Assembly Structures) a comprehensive pipeline for assembly, annotation, genomic binning of metagenomic and metatranscriptomic data with an integrated framework for Multi-Omics. This will provide an open source tool for the Multi-Omic community at large.
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Stenzinger, Albrecht; Klauschen, Frederick; Wittschieber, Daniel; Weichert, Wilko; Denkert, Carsten; Dietel, Manfred; Roller, Claudio
2010-06-01
Research in pathology spans from merely descriptive work to functional studies, "-omics" approaches and, more recently, systems biology. The work presented here aims at placing pathological research into an epistemological context. Aided by Rudolf Virchow, we give an overview on the philosophy of science including the Wiener Kreis, Popper, Kuhn, Fleck and Rheinberger and demonstrate their implications for routine diagnostics and science in pathology. A focus is on the fields of "-omics" and systems pathology.
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Recommending personally interested contents by text mining, filtering, and interfaces
Xu, Songhua
2015-10-27
A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.
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Systems biology of human atherosclerosis.
Shalhoub, Joseph; Sikkel, Markus B; Davies, Kerry J; Vorkas, Panagiotis A; Want, Elizabeth J; Davies, Alun H
2014-01-01
Systems biology describes a holistic and integrative approach to understand physiology and pathology. The "omic" disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the "net" biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism's context. Systems biology relies on the existence of "nodes" and "edges." Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.
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Profile of central research and application laboratory of Aǧrı İbrahim Çeçen University
NASA Astrophysics Data System (ADS)
Türkoǧlu, Emir Alper; Kurt, Murat; Tabay, Dilruba
2016-04-01
Aǧrı İbrahim Çeçen University built a central research and application laboratory (CRAL) in the east of Turkey. The CRAL possesses 7 research and analysis laboratories, 12 experts and researchers, 8 standard rooms for guest researchers, a restaurant, a conference hall, a meeting room, a prey room and a computer laboratory. The CRAL aims certain collaborations between researchers, experts, clinicians and educators in the areas of biotechnology, bioimagining, food safety & quality, omic sciences such as genomics, proteomics and metallomics. It also intends to develop sustainable solutions in agriculture and animal husbandry, promote public health quality, collect scientific knowledge and keep it for future generations, contribute scientific awareness of all stratums of society, provide consulting for small initiatives and industries. It has been collaborated several scientific foundations since 2011.
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Identifying candidate driver genes by integrative ovarian cancer genomics data
NASA Astrophysics Data System (ADS)
Lu, Xinguo; Lu, Jibo
2017-08-01
Integrative analysis of molecular mechanics underlying cancer can distinguish interactions that cannot be revealed based on one kind of data for the appropriate diagnosis and treatment of cancer patients. Tumor samples exhibit heterogeneity in omics data, such as somatic mutations, Copy Number Variations CNVs), gene expression profiles and so on. In this paper we combined gene co-expression modules and mutation modulators separately in tumor patients to obtain the candidate driver genes for resistant and sensitive tumor from the heterogeneous data. The final list of modulators identified are well known in biological processes associated with ovarian cancer, such as CCL17, CACTIN, CCL16, CCL22, APOB, KDF1, CCL11, HNF1B, LRG1, MED1 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.
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Doerrer, Nancy; Ladics, Gregory; McClain, Scott; Herouet-Guicheney, Corinne; Poulsen, Lars K; Privalle, Laura; Stagg, Nicola
2010-12-01
The International Life Sciences Institute Health and Environmental Sciences Institute Protein Allergenicity Technical Committee hosted an international workshop November 16-17, 2009, in Paris, France, with over 60 participants from academia, government, and industry to review and discuss the potential utility of "-omics" technologies for assessing the variability in plant gene, protein, and metabolite expression. The goal of the workshop was to illustrate how a plant's constituent makeup and phenotypic processes can be surveyed analytically. Presentations on the "-omics" techniques (i.e., genomics, proteomics, and metabolomics) highlighted the workshop, and summaries of these presentations are published separately in this supplemental issue. This paper summarizes key messages, as well as the consensus points reached, in a roundtable discussion on eight specific questions posed during the final session of the workshop. The workshop established some common, though not unique, challenges for all "-omics" techniques, and include (a) standardization of separation/extraction and analytical techniques; (b) difficulty in associating environmental impacts (e.g., planting, soil texture, location, climate, stress) with potential alterations in plants at genomic, proteomic, and metabolomic levels; (c) many independent analytical measurements, but few replicates/subjects--poorly defined accuracy and precision; and (d) bias--a lack of hypothesis-driven science. Information on natural plant variation is critical in establishing the utility of new technologies due to the variability in specific analytes that may result from genetic differences (crop genotype), different crop management practices (conventional high input, low input, organic), interaction between genotype and environment, and the use of different breeding methods. For example, variations of several classes of proteins were reported among different soybean, rice, or wheat varieties or varieties grown at different locations. Data on the variability of allergenic proteins are important in defining the risk of potential allergenicity. Once established as a standardized assay, survey approaches such as the "-omics" techniques can be considered in a hypothesis-driven analysis of plants, such as determining unintended effects in genetically modified (GM) crops. However, the analysis should include both the GM and control varieties that have the same breeding history and exposure to the same environmental conditions. Importantly, the biological relevance and safety significance of changes in "-omic" data are still unknown. Furthermore, the current compositional assessment for evaluating the substantial equivalence of GM crops is robust, comprehensive, and a good tool for food safety assessments. The overall consensus of the workshop participants was that many "-omics" techniques are extremely useful in the discovery and research phases of biotechnology, and are valuable for hypothesis generation. However, there are many methodological shortcomings identified with "-omics" approaches, a paucity of reference materials, and a lack of focused strategy for their use that currently make them not conducive for the safety assessment of GM crops. Copyright © 2010 Elsevier Inc. All rights reserved.
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76 FR 67454 - Agency Information Collection Request; 30-Day Public Comment Request
Federal Register 2010, 2011, 2012, 2013, 2014
2011-11-01
... revising the collection to include changes adopted by the cross-agency R&R working group. This working... Assistance (Cover); R&R Personal Data; R&R Senior/Key Person Profile; R&R Senior/Key Person Profile (Expanded...
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The Perseus computational platform for comprehensive analysis of (prote)omics data.
Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen
2016-09-01
A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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A compendium of multi-omic sequence information from the Saanich Inlet water column
Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena; ...
2017-10-31
Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less
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A compendium of multi-omic sequence information from the Saanich Inlet water column
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena
Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less
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Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua
2016-02-25
The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.
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Omics Approach to Identify Factors Involved in Brassica Disease Resistance.
Francisco, Marta; Soengas, Pilar; Velasco, Pablo; Bhadauria, Vijai; Cartea, Maria E; Rodríguez, Victor M
2016-01-01
Understanding plant's defense mechanisms and their response to biotic stresses is of fundamental meaning for the development of resistant crop varieties and more productive agriculture. The Brassica genus involves a large variety of economically important species and cultivars used as vegetable source, oilseeds, forage and ornamental. Damage caused by pathogens attack affects negatively various aspects of plant growth, development, and crop productivity. Over the last few decades, advances in plant physiology, genetics, and molecular biology have greatly improved our understanding of plant responses to biotic stress conditions. In this regard, various 'omics' technologies enable qualitative and quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. In this review, we have described advances in 'omic' tools (genomics, transcriptomics, proteomics and metabolomics) in the view of conventional and modern approaches being used to elucidate the molecular mechanisms that underlie Brassica disease resistance.
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Analysis Commons, A Team Approach to Discovery in a Big-Data Environment for Genetic Epidemiology
Brody, Jennifer A.; Morrison, Alanna C.; Bis, Joshua C.; O'Connell, Jeffrey R.; Brown, Michael R.; Huffman, Jennifer E.; Ames, Darren C.; Carroll, Andrew; Conomos, Matthew P.; Gabriel, Stacey; Gibbs, Richard A.; Gogarten, Stephanie M.; Gupta, Namrata; Jaquish, Cashell E.; Johnson, Andrew D.; Lewis, Joshua P.; Liu, Xiaoming; Manning, Alisa K.; Papanicolaou, George J.; Pitsillides, Achilleas N.; Rice, Kenneth M.; Salerno, William; Sitlani, Colleen M.; Smith, Nicholas L.; Heckbert, Susan R.; Laurie, Cathy C.; Mitchell, Braxton D.; Vasan, Ramachandran S.; Rich, Stephen S.; Rotter, Jerome I.; Wilson, James G.; Boerwinkle, Eric; Psaty, Bruce M.; Cupples, L. Adrienne
2017-01-01
Summary paragraph The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multi-center WGS analyses, including data sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation, and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for transforming WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations. PMID:29074945
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Zhang, Yan; Deng, Yongfeng; Zhao, Yanping; Ren, Hongqiang
2014-05-15
Mixed chemical wastewaters (MCWW) from industrial park contain complex mixtures of trace contaminants, which cannot be effectively removed by wastewater treatment plants (WWTP) and have become an unignored threat to ambient environment. However, limited information is available to evaluate the complicated toxic effects of MCWW and its effluent from wastewater treatment plant (WTPE) from the perspective of bio-omics. In this study, mice were exposed to the MCWW and WTPE for 90 days and distinct differences in the hepatic transcriptome and serum metabolome were analyzed by digital gene expression (DGE) and proton nuclear magnetic resonance ((1)H-NMR) spectra, respectively. Our results indicated that disruption of lipid metabolism in liver and hepatotoxicity were induced by both MCWW and WTPE exposure. WTPE is still a health risk to the environment, which is in need of more attention. Furthermore, we demonstrated the potential ability of bio-omics approaches for evaluating toxic effects of MCWW and WTPE. Copyright © 2014 Elsevier B.V. All rights reserved.
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Metagenomic insights into the ecology and physiology of microbes in bioelectrochemical systems.
Kouzuma, Atsushi; Ishii, Shun'ichi; Watanabe, Kazuya
2018-05-01
In bioelectrochemical systems (BESs), electrons are transferred between electrochemically active microbes (EAMs) and conductive materials, such as electrodes, via extracellular electron transfer (EET) pathways, and electrons thus transferred stimulate intracellular catabolic reactions. Catabolic and EET pathways have extensively been studied for several model EAMs, such as Shewanella oneidensis MR-1 and Geobacter sulfurreducens PCA, whereas it is also important to understand the ecophysiology of EAMs in naturally occurring microbiomes, such as those in anode biofilms in microbial fuel cells treating wastewater. Recent studies have exploited metagenomics and metatranscriptomics (meta-omics) approaches to characterize EAMs in BES-associated microbiomes. Here we review recent BES studies that used meta-omics approaches and show that these studies have discovered unexpected features of EAMs and deepened our understanding of functions and behaviors of microbes in BESs. It is desired that more studies will employ meta-omics approaches for advancing our knowledge on microbes in BESs. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Chen, Bailing; Wan, Chun; Mehmood, Muhammad Aamer; Chang, Jo-Shu; Bai, Fengwu; Zhao, Xinqing
2017-11-01
Microalgae have promising potential to produce lipids and a variety of high-value chemicals. Suitable stress conditions such as nitrogen starvation and high salinity could stimulate synthesis and accumulation of lipids and high-value products by microalgae, therefore, various stress-modification strategies were developed to manipulate and optimize cultivation processes to enhance bioproduction efficiency. On the other hand, advancements in omics-based technologies have boosted the research to globally understand microalgal gene regulation under stress conditions, which enable further improvement of production efficiency via genetic engineering. Moreover, integration of multi-omics data, synthetic biology design, and genetic engineering manipulations exhibits a tremendous potential in the betterment of microalgal biorefinery. This review discusses the process manipulation strategies and omics studies on understanding the regulation of metabolite biosynthesis under various stressful conditions, and proposes genetic engineering of microalgae to improve bioproduction via manipulating stress tolerance. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Network Analysis of Rodent Transcriptomes in Spaceflight
NASA Technical Reports Server (NTRS)
Ramachandran, Maya; Fogle, Homer; Costes, Sylvain
2017-01-01
Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.
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Personality and Treatment Effectiveness in Anorexia Nervosa.
ERIC Educational Resources Information Center
Skoog, Dagna K.; And Others
1984-01-01
Compared pre- and posttreatment Minnesota Multiphasic Personality Inventory profiles of female inpatients (N=12) with anorexia nervosa. Results showed change after treatment, and found that pretreatment profiles obtained at a different hospital were remarkably similar, which suggests a common constellation of personality characteristics in…
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NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies
NASA Technical Reports Server (NTRS)
Beheshti, Afshin; Miller, Jack; Kidane, Yared H.; Berrios, Daniel; Gebre, Samrawit G.; Costes, Sylvain V.
2018-01-01
Accurate assessment of risk factors for long-term space missions is critical for human space exploration: therefore it is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) currently includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information for ground-based studies, we are in the process of adding detailed, curated dosimetry information for spaceflight missions. GeneLab is the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. In addition to previously acquired data, the GLDS is continually expanding as Omics related data are generated by the space life sciences community. Here we provide a brief summary of space radiation related data available at GeneLab.
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Ecosystems Biology Approaches To Determine Key Fitness Traits of Soil Microorganisms
NASA Astrophysics Data System (ADS)
Brodie, E.; Zhalnina, K.; Karaoz, U.; Cho, H.; Nuccio, E. E.; Shi, S.; Lipton, M. S.; Zhou, J.; Pett-Ridge, J.; Northen, T.; Firestone, M.
2014-12-01
The application of theoretical approaches such as trait-based modeling represent powerful tools to explain and perhaps predict complex patterns in microbial distribution and function across environmental gradients in space and time. These models are mostly deterministic and where available are built upon a detailed understanding of microbial physiology and response to environmental factors. However as most soil microorganisms have not been cultivated, for the majority our understanding is limited to insights from environmental 'omic information. Information gleaned from 'omic studies of complex systems should be regarded as providing hypotheses, and these hypotheses should be tested under controlled laboratory conditions if they are to be propagated into deterministic models. In a semi-arid Mediterranean grassland system we are attempting to dissect microbial communities into functional guilds with defined physiological traits and are using a range of 'omics approaches to characterize their metabolic potential and niche preference. Initially, two physiologically relevant time points (peak plant activity and prior to wet-up) were sampled and metagenomes sequenced deeply (600-900 Gbp). Following assembly, differential coverage and nucleotide frequency binning were carried out to yield draft genomes. In addition, using a range of cultivation media we have isolated a broad range of bacteria representing abundant bacterial genotypes and with genome sequences of almost 40 isolates are testing genomic predictions regarding growth rate, temperature and substrate utilization in vitro. This presentation will discuss the opportunities and challenges in parameterizing microbial functional guilds from environmental 'omic information for use in trait-based models.
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Research from the NASA Twins Study and Omics in Support of Mars Missions
NASA Technical Reports Server (NTRS)
Kundrot, C.; Shelhamer, M.; Scott, G.
2015-01-01
The NASA Twins Study, NASA's first foray into integrated omic studies in humans, illustrates how an integrated omics approach can be brought to bear on the challenges to human health and performance on a Mars mission. The NASA Twins Study involves US Astronaut Scott Kelly and his identical twin brother, Mark Kelly, a retired US Astronaut. No other opportunity to study a twin pair for a prolonged period with one subject in space and one on the ground is available for the foreseeable future. A team of 10 principal investigators are conducting the Twins Study, examining a very broad range of biological functions including the genome, epigenome, transcriptome, proteome, metabolome, gut microbiome, immunological response to vaccinations, indicators of atherosclerosis, physiological fluid shifts, and cognition. A novel aspect of the study is the integrated study of molecular, physiological, cognitive, and microbiological properties. Major sample and data collection from both subjects for this study began approximately six months before Scott Kelly's one year mission on the ISS, continue while Scott Kelly is in flight and will conclude approximately six months after his return to Earth. Mark Kelly will remain on Earth during this study, in a lifestyle unconstrained by this study, thereby providing a measure of normal variation in the properties being studied. An overview of initial results and the future plans will be described as well as the technological and ethical issues raised for spaceflight studies involving omics.
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Omics Advances in Ecotoxicology.
Zhang, Xiaowei; Xia, Pu; Wang, Pingping; Yang, Jianghu; Baird, Donald J
2018-04-03
Toxic substances in the environment generate adverse effects at all levels of biological organization from the molecular level to community and ecosystem. Given this complexity, it is not surprising that ecotoxicologists have struggled to address the full consequences of toxic substance release at ecosystem level, due to the limits of observational and experimental tools to reveal the changes in deep structure at different levels of organization. -Omics technologies, consisting of genomics and ecogenomics, have the power to reveal, in unprecedented detail, the cellular processes of an individual or biodiversity of a community in response to environmental change with high sample/observation throughput. This represents a historic opportunity to transform the way we study toxic substances in ecosystems, through direct linkage of ecological effects with the systems biology of organisms. Three recent examples of -omics advance in the assessment of toxic substances are explored here: (1) the use of functional genomics in the discovery of novel molecular mechanisms of toxicity of chemicals in the environment; (2) the development of laboratory pipelines of dose-dependent, reduced transcriptomics to support high-throughput chemical testing at the biological pathway level; and (3) the use of eDNA metabarcoding approaches for assessing chemical effects on biological communities in mesocosm experiments and through direct observation in field monitoring. -Omics advances in ecotoxicological studies not only generate new knowledge regarding mechanisms of toxicity and environmental effect, improving the relevance and immediacy of laboratory toxicological assessment, but can provide a wholly new paradigm for ecotoxicology by linking ecological models to mechanism-based, systems biology approaches.
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Schmidt, Michael A; Goodwin, Thomas J; Pelligra, Ralph
The next major steps in human spaceflight include flyby, orbital, and landing missions to the Moon, Mars, and near earth asteroids. The first crewed deep space mission is expected to launch in 2022, which affords less than 7 years to address the complex question of whether and how to apply artificial gravity to counter the effects of prolonged weightlessness. Various phenotypic changes are demonstrated during artificial gravity experiments. However, the molecular dynamics (genotype and molecular phenotypes) that underlie these morphological, physiological, and behavioral phenotypes are far more complex than previously understood. Thus, targeted molecular assessment of subjects under various G conditions can be expected to miss important patterns of molecular variance that inform the more general phenotypes typically being measured. Use of omics methods can help detect changes across broad molecular networks, as various G-loading paradigms are applied. This will be useful in detecting off-target, or unanticipated effects of the different gravity paradigms applied to humans or animals. Insights gained from these approaches may eventually be used to inform countermeasure development or refine the deployment of existing countermeasures. This convergence of the omics and artificial gravity research communities may be critical if we are to develop the proper artificial gravity solutions under the severely compressed timelines currently established. Thus, the omics community may offer a unique ability to accelerate discovery, provide new insights, and benefit deep space missions in ways that have not been previously considered.
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Personality and psychopathological profiles in individuals exposed to mobbing.
Girardi, Paolo; Monaco, Edoardo; Prestigiacomo, Claudio; Talamo, Alessandra; Ruberto, Amedeo; Tatarelli, Roberto
2007-01-01
Increasingly, mental health and medical professionals have been asked to assess claims of psychological harm arising from harassment at the workplace, or "mobbing." This study assessed the personality and psychopathological profiles of 146 individuals exposed to mobbing using validity, clinical, and content scales of the Minnesota Multiphasic Personality Inventory 2. Profiles and factor analyses were obtained. Two major dimensions emerged among those exposed to mobbing: (a) depressed mood, difficulty in making decisions, change-related anguish, and passive-aggressive traits (b) somatic symptoms, and need for attention and affection. This cross-sectional pilot study provides evidence that personality profiles of mobbing victims and psychological damage resulting from mobbing may be evaluated using standardized assessments, though a longitudinal study is needed to delineate cause-and-effect relationships.
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NASA Technical Reports Server (NTRS)
Reinsch, S. S.; Galazka, J..; Berrios, D. C; Chakravarty, K.; Fogle, H.; Lai, S.; Bokyo, V.; Timucin, L. R.; Tran, P.; Skidmore, M.
2016-01-01
NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.
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Oshri, Assaf; Rogosch, Fred A; Cicchetti, Dante
2013-01-01
The purpose of this study is to investigate longitudinal risk processes linking early child maltreatment, childhood personality organizations, and adolescent maladaptation. In a sample of maltreated and nonmaltreated children (N = 400; 62.3% African American, 11.8% Hispanic; 40.8% girls), a tripartite personality typology based on California Child Q-Set items representative of ego resiliency and ego control personality dimensions (Block & Block, 1969/1980 ) was derived at Wave 1 (age range = 10-12). The typology, composed of Resilient, Overcontrolled, and Undercontrolled profiles, was evaluated for associations with previous child maltreatment, and for its utility in predicting adolescent psychopathology (age range = 15-18). Maltreated children were significantly more likely than nonmaltreated children to be classified into the overcontrolled (Odds Ratio = 1.847) and undercontrolled profiles (Odds Ratio = 2.101), compared to the Resilient profile. The undercontrolled profile reported higher cannabis symptoms and externalizing problems in adolescence than the other two profiles. The overcontrolled group showed the highest levels of internalizing and lowest levels of alcohol problems compared to the other profiles. Person-centered mediation analyses showed that the overcontrolled and the undercontrolled profiles significantly and differentially mediated the link between number of child maltreatment subtypes and the development of adolescent psychopathology. Child maltreatment is a potent environmental stressor that potentiates compromised personality development, eventuating in heightened psychopathology in adolescence. These findings have important implications for prevention and intervention of psychopathology and substance abuse among low income and maltreated youth.
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The High-Throughput Analyses Era: Are We Ready for the Data Struggle?
D'Argenio, Valeria
2018-03-02
Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.