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Borsatto KC, Coronado MA, Arni RK, Chaboli Alevi KC. Omics Tools Applied to the Study of Chagas Disease Vectors: Cytogenomics and Genomics. Am J Trop Med Hyg 2021; 104:1973-1977. [PMID: 33872207 DOI: 10.4269/ajtmh.20-1047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/14/2020] [Indexed: 11/07/2022] Open
Abstract
Chagas disease is an illness caused by the protozoan Trypanosoma cruzi that is distributed in 21 countries of Latin America. The main way of transmission of T. cruzi is through the feces of triatomines (Hemiptera and Triatominae) infected with the parasite. With technological advances came new technologies called omics. In the pre-genomic era, the omics science was based on cytogenomic studies of triatomines. With the Rhodnius prolixus genome sequencing project, new omics tools were applied to understand the organism at a systemic level and not just from a genomic point of view. Thus, the present review aims to put together the cytogenomic and genomic information available in the literature for Chagas disease vectors. Here, we review all studies related to cytogenomics and genomics of Chagas disease vectors, contributing to the direction of further research with these insect vectors, because it was evident that most studies focus on cytogenomic knowledge of the species. Given the importance of genomic studies, which contributed to the knowledge of taxonomy, systematics, as well as the vector's biology, the need to apply these techniques in other genera and species of Triatominae subfamily is emphasized.
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Affiliation(s)
- Kelly Cristine Borsatto
- 1Departamento de Física, Instituto de Biociências Letras e Ciências Exatas, Centro Multiusuário de Inovação Biomolecular, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), São José do Rio Preto, Brazil
| | - Monika Aparecida Coronado
- 1Departamento de Física, Instituto de Biociências Letras e Ciências Exatas, Centro Multiusuário de Inovação Biomolecular, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), São José do Rio Preto, Brazil
| | - Raghuvir Krishnaswamy Arni
- 1Departamento de Física, Instituto de Biociências Letras e Ciências Exatas, Centro Multiusuário de Inovação Biomolecular, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), São José do Rio Preto, Brazil
| | - Kaio Cesar Chaboli Alevi
- 2Departamento de Ciências Biológicas, Faculdade de Ciências Farmacêuticas, Laboratório de Parasitologia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), Araraquara, Brazil
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2
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Comte B, Baumbach J, Benis A, Basílio J, Debeljak N, Flobak Å, Franken C, Harel N, He F, Kuiper M, Méndez Pérez JA, Pujos-Guillot E, Režen T, Rozman D, Schmid JA, Scerri J, Tieri P, Van Steen K, Vasudevan S, Watterson S, Schmidt HH. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. NETWORK AND SYSTEMS MEDICINE 2020; 3:67-90. [PMID: 32954378 PMCID: PMC7500076 DOI: 10.1089/nsm.2020.0004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/14/2022] Open
Abstract
Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.
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Affiliation(s)
- Blandine Comte
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan (WZW), Technical University of Munich (TUM), Freising-Weihenstephan, Germany
| | | | - José Basílio
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Nataša Debeljak
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
| | - Christian Franken
- Digital Health Systems, Einsingen, Germany
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | | | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Martin Kuiper
- Department of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Juan Albino Méndez Pérez
- Department of Computer Science and Systems Engineering, Universidad de La Laguna, Tenerife, Spain
| | - Estelle Pujos-Guillot
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Johannes A. Schmid
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Jeanesse Scerri
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Sona Vasudevan
- Georgetown University Medical Centre, Washington, District of Columbia, USA
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, MeHNS, Maastricht University, The Netherlands
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3
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O’Donnell ST, Ross RP, Stanton C. The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach. Front Microbiol 2020; 10:3084. [PMID: 32047482 PMCID: PMC6997344 DOI: 10.3389/fmicb.2019.03084] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 12/20/2019] [Indexed: 12/12/2022] Open
Abstract
Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.
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Affiliation(s)
- Shane Thomas O’Donnell
- Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
- Department of Microbiology, University College Cork – National University of Ireland, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - R. Paul Ross
- Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
- Department of Microbiology, University College Cork – National University of Ireland, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Catherine Stanton
- Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
- APC Microbiome Ireland, Cork, Ireland
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4
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Harvey LD, Chan SY. Evolving systems biology approaches to understanding non-coding RNAs in pulmonary hypertension. J Physiol 2018; 597:1199-1208. [PMID: 30113078 DOI: 10.1113/jp275855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/04/2018] [Indexed: 01/17/2023] Open
Abstract
Our appreciation of the roles of non-coding RNAs, in particular microRNAs, in the manifestation of pulmonary hypertension (PH) has advanced considerably over the past decade. Comprised of small nucleotide sequences, microRNAs have demonstrated critical and broad regulatory roles in the pathogenesis of PH via the direct binding to messenger RNA transcripts for degradation or inhibition of translation, thereby exerting a profound influence on cellular activity. Yet, as inherently pleiotropic molecules, microRNAs have been difficult to study using traditional, reductionist approaches alone. With the advent of high-throughput -omics technologies and more advanced computational modelling, the study of microRNAs and their multi-faceted and complex functions in human disease serves as a fertile platform for the application of systems biology methodologies in combination with traditional experimental techniques. Here, we offer our viewpoint of past successes of systems biology in elucidating the otherwise hidden actions of microRNAs in PH, as well as areas for future development to integrate these strategies into the discovery of RNA pathobiology in this disease. We contend that such successful applications of systems biology in elucidating the functional architecture of microRNA regulation will further reveal the molecular mechanisms of disease, while simultaneously revealing potential diagnostic and therapeutic strategies in disease amelioration.
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Affiliation(s)
- Lloyd D Harvey
- Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Stephen Y Chan
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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5
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Diep JK, Russo TA, Rao GG. Mechanism-Based Disease Progression Model Describing Host-Pathogen Interactions During the Pathogenesis of Acinetobacter baumannii Pneumonia. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:507-516. [PMID: 29761668 PMCID: PMC6118322 DOI: 10.1002/psp4.12312] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/09/2018] [Indexed: 01/01/2023]
Abstract
The emergence of highly resistant bacteria is a serious threat to global public health. The host immune response is vital for clearing bacteria from the infected host; however, the current drug development paradigm does not take host‐pathogen interactions into consideration. Here, we used a systems‐based approach to develop a quantitative, mechanism‐based disease progression model to describe bacterial dynamics, host immune response, and lung injury in an immunocompetent rat pneumonia model. Previously, Long‐Evans rats were infected with Acinetobacter baumannii (A. baumannii) strain 307‐0294 at five different inocula and total lung bacteria, interleukin‐1beta (IL‐1β), tumor necrosis factor‐α (TNF‐α), cytokine‐induced neutrophil chemoattractant 1 (CINC‐1), neutrophil counts, and albumin were quantified. Model development was conducted in ADAPT5 version 5.0.54 using a pooled approach with maximum likelihood estimation; all data were co‐modeled. The final model characterized host‐pathogen interactions during the natural time course of bacterial pneumonia. Parameters were estimated with good precision. Our expandable model will integrate drug effects to aid in the design of optimized antibiotic regimens.
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Affiliation(s)
- John K Diep
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.,University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Thomas A Russo
- University at Buffalo, State University of New York, Buffalo, New York, USA.,Veterans Administration Western New York Healthcare System, Buffalo, New York, USA
| | - Gauri G Rao
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.,University at Buffalo, State University of New York, Buffalo, New York, USA
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6
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Assessment and modelling of antibacterial combination regimens. Clin Microbiol Infect 2018; 24:689-696. [DOI: 10.1016/j.cmi.2017.12.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 11/30/2017] [Accepted: 12/07/2017] [Indexed: 12/11/2022]
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Abstract
Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences. Contemporary biology has largely become computational biology, whether it involves applying physical principles to simulate the motion of each atom in a piece of DNA, or using machine learning algorithms to integrate and mine “omics” data across whole cells (or even entire ecosystems). The ability to design algorithms and program computers, even at a novice level, may be the most indispensable skill that a modern researcher can cultivate. As with human languages, computational fluency is developed actively, not passively. This self-contained text, structured as a hybrid primer/tutorial, introduces any biologist—from college freshman to established senior scientist—to basic computing principles (control-flow, recursion, regular expressions, etc.) and the practicalities of programming and software design. We use the Python language because it now pervades virtually every domain of the biosciences, from sequence-based bioinformatics and molecular evolution to phylogenomics, systems biology, structural biology, and beyond. To introduce both coding (in general) and Python (in particular), we guide the reader via concrete examples and exercises. We also supply, as Supplemental Chapters, a few thousand lines of heavily-annotated, freely distributed source code for personal study.
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Affiliation(s)
- Berk Ekmekci
- Department of Chemistry, University of Virginia, Charlottesville, Virginia, United States of America
| | - Charles E. McAnany
- Department of Chemistry, University of Virginia, Charlottesville, Virginia, United States of America
| | - Cameron Mura
- Department of Chemistry, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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8
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FabR regulates Salmonella biofilm formation via its direct target FabB. BMC Genomics 2016; 17:253. [PMID: 27004424 PMCID: PMC4804515 DOI: 10.1186/s12864-016-2387-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 01/08/2016] [Indexed: 12/02/2022] Open
Abstract
Background Biofilm formation is an important survival strategy of Salmonella in all environments. By mutant screening, we showed a knock-out mutant of fabR, encoding a repressor of unsaturated fatty acid biosynthesis (UFA), to have impaired biofilm formation. In order to unravel how this regulator impinges on Salmonella biofilm formation, we aimed at elucidating the S. Typhimurium FabR regulon. Hereto, we applied a combinatorial high-throughput approach, combining ChIP-chip with transcriptomics. Results All the previously identified E. coli FabR transcriptional target genes (fabA, fabB and yqfA) were shown to be direct S. Typhimurium FabR targets as well. As we found a fabB overexpressing strain to partly mimic the biofilm defect of the fabR mutant, the effect of FabR on biofilms can be attributed at least partly to FabB, which plays a key role in UFA biosynthesis. Additionally, ChIP-chip identified a number of novel direct FabR targets (the intergenic regions between hpaR/hpaG and ddg/ydfZ) and yet putative direct targets (i.a. genes involved in tRNA metabolism, ribosome synthesis and translation). Next to UFA biosynthesis, a number of these direct targets and other indirect targets identified by transcriptomics (e.g. ribosomal genes, ompA, ompC, ompX, osmB, osmC, sseI), could possibly contribute to the effect of FabR on biofilm formation. Conclusion Overall, our results point at the importance of FabR and UFA biosynthesis in Salmonella biofilm formation and their role as potential targets for biofilm inhibitory strategies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2387-x) contains supplementary material, which is available to authorized users.
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9
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Li C, Li J, Wang G, Li X. Heterologous biosynthesis of artemisinic acid in Saccharomyces cerevisiae. J Appl Microbiol 2016; 120:1466-78. [PMID: 26743771 DOI: 10.1111/jam.13044] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 12/11/2015] [Accepted: 01/02/2016] [Indexed: 02/06/2023]
Abstract
Artemisinic acid is a precursor of antimalarial compound artemisinin. The titre of biosynthesis of artemisinic acid using Saccharomyces cerevisiae platform has been achieved up to 25 g l(-1) ; however, the performance of platform cells is still industrial unsatisfied. Many strategies have been proposed to improve the titre of artemisinic acid. The traditional strategies mainly focused on partial target sites, simple up-regulation key genes or repression competing pathways in the total synthesis route. However, this may result in unbalance of carbon fluxes and dysfunction of metabolism. In this review, the recent advances on the promising methods in silico and in vivo for biosynthesis of artemisinic acid have been discussed. The bioinformatics and omics techniques have brought a great prospect for improving production of artemisinin and other pharmacal compounds in heterologous platform.
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Affiliation(s)
- C Li
- Key Laboratory of Environmental and Applied Microbiology, Chinese Academy of Sciences, Chengdu, China.,Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China.,University of Chinese Academy of Sciences, Beijing, China
| | - J Li
- Key Laboratory of Environmental and Applied Microbiology, Chinese Academy of Sciences, Chengdu, China.,Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - G Wang
- Key Laboratory of Environmental and Applied Microbiology, Chinese Academy of Sciences, Chengdu, China.,Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - X Li
- Key Laboratory of Environmental and Applied Microbiology, Chinese Academy of Sciences, Chengdu, China.,Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
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10
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Angione C, Lió P. Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep 2015; 5:15147. [PMID: 26482106 PMCID: PMC4611489 DOI: 10.1038/srep15147] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 09/14/2015] [Indexed: 01/22/2023] Open
Abstract
Bacterial phenotypic traits and lifestyles in response to diverse environmental conditions depend on changes in the internal molecular environment. However, predicting bacterial adaptability is still difficult outside of laboratory controlled conditions. Many molecular levels can contribute to the adaptation to a changing environment: pathway structure, codon usage, metabolism. To measure adaptability to changing environmental conditions and over time, we develop a multi-omic model of Escherichia coli that accounts for metabolism, gene expression and codon usage at both transcription and translation levels. After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic markers. In the condition space, we propose Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and metabolic) evolution, thus enabling comparative analysis of metabolic conditions. We therefore compare, evaluate and cluster different experimental conditions, models and bacterial strains according to their metabolic response in a multidimensional objective space, rather than in the original space of microarray data. We finally validate our methods on a phenomics dataset of growth conditions. Our framework, named METRADE, is freely available as a MATLAB toolbox.
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Affiliation(s)
| | - Pietro Lió
- Computer Laboratory - University of Cambridge, UK
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11
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Integrating -Omics: Systems Biology as Explored Through C. elegans Research. J Mol Biol 2015; 427:3441-51. [PMID: 25839106 DOI: 10.1016/j.jmb.2015.03.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 03/24/2015] [Accepted: 03/25/2015] [Indexed: 10/23/2022]
Abstract
-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.
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12
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Ricke SC. Anaerobic Microbiology Laboratory Training and Writing Comprehension for Food Safety Education. Food Saf (Tokyo) 2015. [DOI: 10.1016/b978-0-12-800245-2.00019-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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13
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Sheth BP, Thaker VS. Plant systems biology: insights, advances and challenges. PLANTA 2014; 240:33-54. [PMID: 24671625 DOI: 10.1007/s00425-014-2059-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 03/06/2014] [Indexed: 05/20/2023]
Abstract
Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.
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Affiliation(s)
- Bhavisha P Sheth
- Department of Biosciences, Centre for Advanced Studies in Plant Biotechnology and Genetic Engineering, Saurashtra University, Rajkot, 360005, Gujarat, India,
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14
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Najafi A, Bidkhori G, Bozorgmehr JH, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Curr Genomics 2014; 15:130-59. [PMID: 24822031 PMCID: PMC4009841 DOI: 10.2174/1389202915666140319002221] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Revised: 02/16/2014] [Accepted: 03/17/2014] [Indexed: 12/18/2022] Open
Abstract
In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.
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Affiliation(s)
- Ali Najafi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Gholamreza Bidkhori
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Joseph H. Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Ina Koch
- Molecular Bioinformatics, Johann Wolfgang Goethe-University Frankfurt am Main, Germany
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
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15
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Aires J, Butel MJ. Proteomics, human gut microbiota and probiotics. Expert Rev Proteomics 2014; 8:279-88. [DOI: 10.1586/epr.11.5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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16
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Foley SL, Johnson TJ, Ricke SC, Nayak R, Danzeisen J. Salmonella pathogenicity and host adaptation in chicken-associated serovars. Microbiol Mol Biol Rev 2013; 77:582-607. [PMID: 24296573 PMCID: PMC3973385 DOI: 10.1128/mmbr.00015-13] [Citation(s) in RCA: 189] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Enteric pathogens such as Salmonella enterica cause significant morbidity and mortality. S. enterica serovars are a diverse group of pathogens that have evolved to survive in a wide range of environments and across multiple hosts. S. enterica serovars such as S. Typhi, S. Dublin, and S. Gallinarum have a restricted host range, in which they are typically associated with one or a few host species, while S. Enteritidis and S. Typhimurium have broad host ranges. This review examines how S. enterica has evolved through adaptation to different host environments, especially as related to the chicken host, and continues to be an important human pathogen. Several factors impact host range, and these include the acquisition of genes via horizontal gene transfer with plasmids, transposons, and phages, which can potentially expand host range, and the loss of genes or their function, which would reduce the range of hosts that the organism can infect. S. Gallinarum, with a limited host range, has a large number of pseudogenes in its genome compared to broader-host-range serovars. S. enterica serovars such as S. Kentucky and S. Heidelberg also often have plasmids that may help them colonize poultry more efficiently. The ability to colonize different hosts also involves interactions with the host's immune system and commensal organisms that are present. Thus, the factors that impact the ability of Salmonella to colonize a particular host species, such as chickens, are complex and multifactorial, involving the host, the pathogen, and extrinsic pressures. It is the interplay of these factors which leads to the differences in host ranges that we observe today.
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Ricke SC, Khatiwara A, Kwon YM. Application of microarray analysis of foodborne Salmonella in poultry production: A review. Poult Sci 2013; 92:2243-50. [DOI: 10.3382/ps.2012-02740] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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18
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Arakawa K, Tomita M. Merging multiple omics datasets in silico: statistical analyses and data interpretation. Methods Mol Biol 2013; 985:459-70. [PMID: 23417818 DOI: 10.1007/978-1-62703-299-5_23] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multi-omics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.
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Affiliation(s)
- Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, Japan.
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19
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Fierro AC, Vandenbussche F, Engelen K, Van de Peer Y, Marchal K. Meta Analysis of Gene Expression Data within and Across Species. Curr Genomics 2011; 9:525-34. [PMID: 19516959 PMCID: PMC2694560 DOI: 10.2174/138920208786847935] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2008] [Revised: 07/07/2008] [Accepted: 07/18/2008] [Indexed: 01/15/2023] Open
Abstract
Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data.
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Affiliation(s)
- Ana C Fierro
- Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium
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20
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Kint G, Fierro C, Marchal K, Vanderleyden J, De Keersmaecker SCJ. Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions? Future Microbiol 2010; 5:313-28. [DOI: 10.2217/fmb.10.1] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.
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Affiliation(s)
- Gwendoline Kint
- Centre of Microbial & Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
| | - Carolina Fierro
- Centre of Microbial & Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
| | - Kathleen Marchal
- Centre of Microbial & Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
| | - Jos Vanderleyden
- Centre of Microbial & Plant Genetics, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
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Sintchenko V. Informatics for Infectious Disease Research and Control. INFECTIOUS DISEASE INFORMATICS 2010. [PMCID: PMC7120928 DOI: 10.1007/978-1-4419-1327-2_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.
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Tan TW, Lim SJ, Khan AM, Ranganathan S. A proposed minimum skill set for university graduates to meet the informatics needs and challenges of the "-omics" era. BMC Genomics 2009; 10 Suppl 3:S36. [PMID: 19958501 PMCID: PMC2788390 DOI: 10.1186/1471-2164-10-s3-s36] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The development of high throughput experimental technologies have given rise to the "-omics" era where terabyte-scale datasets for systems-level measurements of various cellular and molecular phenomena pose considerable challenges in data processing and extraction of biological meaning. Moreover, it has created an unmet need for the effective integration of these datasets to achieve insights into biological systems. While it has increased the demand for bioinformatics experts who can interface with biologists, it has also raised the requirement for biologists to possess a basic capability in bioinformatics and to communicate seamlessly with these experts. This may be achieved by embedding in their undergraduate and graduate life science education, basic training in bioinformatics geared towards acquiring a minimum skill set in computation and informatics. RESULTS Based on previous attempts to define curricula suitable for addressing the bioinformatics capability gap, an initiative was taken during the Workshops on Education in Bioinformatics and Computational Biology (WEBCB) in 2008 and 2009 to identify a minimum skill set for the training of future bioinformaticians and molecular biologists with informatics capabilities. The minimum skill set proposed is cross-disciplinary in nature, involving a combination of knowledge and proficiency from the fields of biology, computer science, mathematics and statistics, and can be tailored to the needs of the "-omics". CONCLUSION The proposed bioinformatics minimum skill set serves as a guideline for biology curriculum design and development in universities at both the undergraduate and graduate levels.
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Affiliation(s)
- Tin Wee Tan
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore.
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23
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Zhang W, Li F, Nie L. Integrating multiple 'omics' analysis for microbial biology: application and methodologies. MICROBIOLOGY-SGM 2009; 156:287-301. [PMID: 19910409 DOI: 10.1099/mic.0.034793-0] [Citation(s) in RCA: 281] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Recent advances in various 'omics' technologies enable 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. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.
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Affiliation(s)
- Weiwen Zhang
- Center for Ecogenomics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-6501, USA
| | - Feng Li
- Division of Biometrics II, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA
| | - Lei Nie
- Division of Biometrics IV, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA
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24
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Costello JC, Dalkilic MM, Beason SM, Gehlhausen JR, Patwardhan R, Middha S, Eads BD, Andrews JR. Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function. Genome Biol 2009; 10:R97. [PMID: 19758432 PMCID: PMC2768986 DOI: 10.1186/gb-2009-10-9-r97] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2009] [Revised: 08/17/2009] [Accepted: 09/16/2009] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Discovering the functions of all genes is a central goal of contemporary biomedical research. Despite considerable effort, we are still far from achieving this goal in any metazoan organism. Collectively, the growing body of high-throughput functional genomics data provides evidence of gene function, but remains difficult to interpret. RESULTS We constructed the first network of functional relationships for Drosophila melanogaster by integrating most of the available, comprehensive sets of genetic interaction, protein-protein interaction, and microarray expression data. The complete integrated network covers 85% of the currently known genes, which we refined to a high confidence network that includes 20,000 functional relationships among 5,021 genes. An analysis of the network revealed a remarkable concordance with prior knowledge. Using the network, we were able to infer a set of high-confidence Gene Ontology biological process annotations on 483 of the roughly 5,000 previously unannotated genes. We also show that this approach is a means of inferring annotations on a class of genes that cannot be annotated based solely on sequence similarity. Lastly, we demonstrate the utility of the network through reanalyzing gene expression data to both discover clusters of coregulated genes and compile a list of candidate genes related to specific biological processes. CONCLUSIONS Here we present the the first genome-wide functional gene network in D. melanogaster. The network enables the exploration, mining, and reanalysis of experimental data, as well as the interpretation of new data. The inferred annotations provide testable hypotheses of previously uncharacterized genes.
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Affiliation(s)
- James C Costello
- School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA
- Department of Biology, Indiana University, E. Third St, Bloomington, Indiana 47405, USA
| | - Mehmet M Dalkilic
- School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA
- Center for Genomics and Bioinformatics, Indiana University, E. Third St., Bloomington, Indiana 47405, USA
| | - Scott M Beason
- School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA
| | - Jeff R Gehlhausen
- School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA
| | - Rupali Patwardhan
- Center for Genomics and Bioinformatics, Indiana University, E. Third St., Bloomington, Indiana 47405, USA
- Current address: Department of Genome Sciences, University of Washington, NE Pacific St, Seattle, Washington 98195-5065, USA
| | - Sumit Middha
- Center for Genomics and Bioinformatics, Indiana University, E. Third St., Bloomington, Indiana 47405, USA
- Current address: Bioinformatics Core, Mayo Clinic, First St SW, Rochester, Minnesota 55905, USA
| | - Brian D Eads
- Department of Biology, Indiana University, E. Third St, Bloomington, Indiana 47405, USA
| | - Justen R Andrews
- School of Informatics, Indiana University, E. Tenth St, Bloomington, Indiana 47408, USA
- Department of Biology, Indiana University, E. Third St, Bloomington, Indiana 47405, USA
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25
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Frutos R, Viari A, Vachiéry N, Boyer F, Lefrançois T, Martinez D. Emergence and potential of high-throughput and integrative approaches in pathology. Ann N Y Acad Sci 2009; 1149:62-5. [PMID: 19120175 DOI: 10.1196/annals.1428.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In recent years a major revolution has occurred in the analysis and understanding of pathogenesis and host-pathogens/parasite interactions. This revolution has been achieved through the emergence of the high-throughput integrative approaches used in the "omics" fields-such as genomics, transcriptomics, proteomics, interactomics, and metabolomics. The novelty of these approaches has resulted from the development of high-throughput apparatus, assisted by the increasing power and software of computers that allow for high-speed, multifactorial simultaneous analysis of numerous samples. This level of integration allows for in-depth analysis of mechanisms, pace, and patterns of the evolution and adaptation of pathogens. This evolution from linear to multifactorial approaches has opened new ways of creating and characterizing new vaccines, diagnostic candidates, and drug targets.
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Affiliation(s)
- Roger Frutos
- Cirad, TA A-15/G, Campus International de Baillarguet, Montpellier, France
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26
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Zhang C, Crasta O, Cammer S, Will R, Kenyon R, Sullivan D, Yu Q, Sun W, Jha R, Liu D, Xue T, Zhang Y, Moore M, McGarvey P, Huang H, Chen Y, Zhang J, Mazumder R, Wu C, Sobral B. An emerging cyberinfrastructure for biodefense pathogen and pathogen-host data. Nucleic Acids Res 2008; 36:D884-91. [PMID: 17984082 PMCID: PMC2239001 DOI: 10.1093/nar/gkm903] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2007] [Revised: 10/04/2007] [Accepted: 10/05/2007] [Indexed: 01/07/2023] Open
Abstract
The NIAID-funded Biodefense Proteomics Resource Center (RC) provides storage, dissemination, visualization and analysis capabilities for the experimental data deposited by seven Proteomics Research Centers (PRCs). The data and its publication is to support researchers working to discover candidates for the next generation of vaccines, therapeutics and diagnostics against NIAID's Category A, B and C priority pathogens. The data includes transcriptional profiles, protein profiles, protein structural data and host-pathogen protein interactions, in the context of the pathogen life cycle in vivo and in vitro. The database has stored and supported host or pathogen data derived from Bacillus, Brucella, Cryptosporidium, Salmonella, SARS, Toxoplasma, Vibrio and Yersinia, human tissue libraries, and mouse macrophages. These publicly available data cover diverse data types such as mass spectrometry, yeast two-hybrid (Y2H), gene expression profiles, X-ray and NMR determined protein structures and protein expression clones. The growing database covers over 23 000 unique genes/proteins from different experiments and organisms. All of the genes/proteins are annotated and integrated across experiments using UniProt Knowledgebase (UniProtKB) accession numbers. The web-interface for the database enables searching, querying and downloading at the level of experiment, group and individual gene(s)/protein(s) via UniProtKB accession numbers or protein function keywords. The system is accessible at http://www.proteomicsresource.org/.
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Affiliation(s)
- C. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - O. Crasta
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - S. Cammer
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Will
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Kenyon
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Sullivan
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Q. Yu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - W. Sun
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Jha
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Liu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - T. Xue
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - M. Moore
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - P. McGarvey
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - H. Huang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Chen
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - J. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Mazumder
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - C. Wu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - B. Sobral
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
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Libourel IGL, Shachar-Hill Y. Metabolic flux analysis in plants: from intelligent design to rational engineering. ANNUAL REVIEW OF PLANT BIOLOGY 2008; 59:625-50. [PMID: 18257707 DOI: 10.1146/annurev.arplant.58.032806.103822] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Metabolic flux analysis (MFA) is a rapidly developing field concerned with the quantification and understanding of metabolism at the systems level. The application of MFA has produced detailed maps of flow through metabolic networks of a range of plant systems. These maps represent detailed metabolic phenotypes, contribute significantly to our understanding of metabolism in plants, and have led to the discovery of new metabolic routes. The presentation of thorough statistical evaluation with current flux maps has set a new standard for the quality of quantitative flux studies. In microbial systems, powerful methods have been developed for the reconstruction of metabolic networks from genomic and transcriptomic data, pathway analysis, and predictive modeling. This review brings together the recent developments in quantitative MFA and predictive modeling. The application of predictive tools to high quality flux maps in particular promises to be important in the rational metabolic engineering of plants.
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Affiliation(s)
- Igor G L Libourel
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824, USA.
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28
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Song EJ, Babar SME, Oh E, Hasan MN, Hong HM, Yoo YS. CE at the omics level: Towards systems biology – An update. Electrophoresis 2008; 29:129-42. [DOI: 10.1002/elps.200700467] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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29
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Kim DH, Shreenivasaiah PK, Hong S, Kim T, Song HK. Current research trends in systems biology. Anim Cells Syst (Seoul) 2008. [DOI: 10.1080/19768354.2008.9647172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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30
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Annotation, comparison and databases for hundreds of bacterial genomes. Res Microbiol 2007; 158:724-36. [DOI: 10.1016/j.resmic.2007.09.009] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2007] [Revised: 09/21/2007] [Accepted: 09/26/2007] [Indexed: 11/20/2022]
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Thijs IMV, De Keersmaecker SCJ, Fadda A, Engelen K, Zhao H, McClelland M, Marchal K, Vanderleyden J. Delineation of the Salmonella enterica serovar Typhimurium HilA regulon through genome-wide location and transcript analysis. J Bacteriol 2007; 189:4587-96. [PMID: 17483226 PMCID: PMC1913449 DOI: 10.1128/jb.00178-07] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The Salmonella enterica serovar Typhimurium HilA protein is the key regulator for the invasion of epithelial cells. By a combination of genome-wide location and transcript analysis, the HilA-dependent regulon has been delineated. Under invasion-inducing conditions, HilA binds to most of the known target genes and a number of new target genes. The sopB, sopE, and sopA genes, encoding effector proteins secreted by the type III secretion system on Salmonella pathogenicity island 1 (SPI-1), were identified as being both bound by HilA and differentially regulated in an HilA mutant. This suggests a cooperative role for HilA and InvF in the regulation of SPI-1-secreted effectors. Also, siiA, the first gene of SPI-4, is both bound by HilA and differentially regulated in an HilA mutant, thus linking this pathogenicity island to the invasion key regulator. Finally, the interactions of HilA with the SPI-2 secretion system gene ssaH and the flagellar gene flhD imply a repressor function for HilA under invasion-inducing conditions.
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Affiliation(s)
- Inge M V Thijs
- Centre of Microbial and Plant Genetics, K. U. Leuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium
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