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Zhang Y, Shi J, Tan C, Liu Y, Xu YJ. Oilomics: An important branch of foodomics dealing with oil science and technology. Food Res Int 2023; 173:113301. [PMID: 37803609 DOI: 10.1016/j.foodres.2023.113301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 10/08/2023]
Abstract
Oil is one of three nutritious elements. The application of omics techniques in the field of oil science and technology is attracted increasing attention. Oilomics, which emerged as an important branch of foodomics, has been widely used in various aspects of oil science and technology. However, there are currently no articles systematically reviewing the application of oilomics. This paper aims to provide a critical overview of the advantages and value of oilomics technology compared to traditional techniques in various aspects of oil science and technology, including oil nutrition, oil processing, oil quality, safety, and traceability. Moreover, this article intends to review major issues in oilomics and give a comprehensive, critical overview of the current state of the art, future challenges and trends in oilomics, with a view to promoting the optimal application and development of oilomics technology in oil science and technology.
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Affiliation(s)
- Yu Zhang
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China
| | - Jiachen Shi
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China
| | - Chinping Tan
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, UPM, 43400 Serdang, Selangor, Malaysia
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
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2
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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3
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Transcriptome repository of North-Western Himalayan endangered medicinal herbs: a paramount approach illuminating molecular perspective of phytoactive molecules and secondary metabolism. Mol Genet Genomics 2021; 296:1177-1202. [PMID: 34557965 DOI: 10.1007/s00438-021-01821-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/12/2021] [Indexed: 01/23/2023]
Abstract
Medicinal plants of the North-Western Himalayan region are known for their unprecedented biodiversity and valuable secondary metabolites that are unique to this dynamic geo-climatic region. From ancient times these medicinal herbs have been used traditionally for their therapeutic potentials. But from the last 2 decades increasing pharmaceutical demand, illegal and unorganized trade of these medicinal plants have accelerated the rate of over-exploitation in a non-scientific manner. In addition, climate change and anthropogenic activities also affected their natural habitat and driving most of these endemic plant species to critically endangered that foresee peril of mass extinction from this eco-region. Hence there is an urgent need for developing alternative sustainable approaches and policies to utilize this natural bioresource ensuring simultaneous conservation. Hither, arise the advent of sequencing-based transcriptomic studies significantly contributes to better understand the background of important metabolic pathways and related genes/enzymes of high-value medicinal herbs, in the absence of genomic information. The use of comparative transcriptomics in conjunction with biochemical techniques in North-Western Himalayan medicinal plants has resulted in significant advances in the identification of the molecular players involved in the production of secondary metabolic pathways over the last decade. This information could be used to further engineer metabolic pathways and breeding programs, ultimately leading to the development of in vitro systems dedicated to the production of pharmaceutically important secondary metabolites at the industrial level. Collectively, successful adoption of these approaches can certainly ensure the sustainable utilization of Himalayan bioresource by reducing the pressure on the wild population of these critically endangered medicinal herbs. This review provides novel insight as a transcriptome-based bioresource repository for the understanding of important secondary metabolic pathways genes/enzymes and metabolism of endangered high-value North-Western Himalayan medicinal herbs, so that researchers across the globe can effectively utilize this information for devising effective strategies for the production of pharmaceutically important compounds and their scale-up for sustainable usage and take a step forward in omics-based conservation genetics.
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Systems Wide Analysis of CCM Signaling Complex Alterations in CCM-Deficient Models Using Omics Approaches. Methods Mol Biol 2021; 2152:325-344. [PMID: 32524563 DOI: 10.1007/978-1-0716-0640-7_24] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Omics research has garnered popularity recently to integrate in-depth analysis of alterations at the molecular level to elucidate observable phenotypes resulting from knockdown/knockout models. Genomics, performed through RNA-seq, allows the user to evaluate alterations at the transcription level, oftentimes more sensitive than other types of analysis, especially when attempting to understand lack of observation of an expected phenotype. Proteomics facilitates an understanding of mechanisms being altered at the translational level allowing for an understanding of multiple layers of regulation occurring, elucidating discrepancies between what is seen at the RNA level compared to what is translated to a functional protein. Here we describe the methods currently being used to evaluate CCM-deficient strains in human brain microvascular endothelial cells (HBMVEC), zebrafish embryos as well as in vivo mouse model to evaluate impacts on various signaling cascades resulting from deficiencies in KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). The integration of data from genomics and proteomics analysis allows for the composition of interactomes, elucidating systems wide impacts resulting from disruption of the CCM signaling complex (CSC).
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Hodgson SH, Muller J, Lockstone HE, Hill AVS, Marsh K, Draper SJ, Knight JC. Use of gene expression studies to investigate the human immunological response to malaria infection. Malar J 2019; 18:418. [PMID: 31835999 PMCID: PMC6911278 DOI: 10.1186/s12936-019-3035-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 11/26/2019] [Indexed: 01/02/2023] Open
Abstract
Background Transcriptional profiling of the human immune response to malaria has been used to identify diagnostic markers, understand the pathogenicity of severe disease and dissect the mechanisms of naturally acquired immunity (NAI). However, interpreting this body of work is difficult given considerable variation in study design, definition of disease, patient selection and methodology employed. This work details a comprehensive review of gene expression profiling (GEP) of the human immune response to malaria to determine how this technology has been applied to date, instances where this has advanced understanding of NAI and the extent of variability in methodology between studies to allow informed comparison of data and interpretation of results. Methods Datasets from the gene expression omnibus (GEO) including the search terms; ‘plasmodium’ or ‘malaria’ or ‘sporozoite’ or ‘merozoite’ or ‘gametocyte’ and ‘Homo sapiens’ were identified and publications analysed. Datasets of gene expression changes in relation to malaria vaccines were excluded. Results Twenty-three GEO datasets and 25 related publications were included in the final review. All datasets related to Plasmodium falciparum infection, except two that related to Plasmodium vivax infection. The majority of datasets included samples from individuals infected with malaria ‘naturally’ in the field (n = 13, 57%), however some related to controlled human malaria infection (CHMI) studies (n = 6, 26%), or cells stimulated with Plasmodium in vitro (n = 6, 26%). The majority of studies examined gene expression changes relating to the blood stage of the parasite. Significant heterogeneity between datasets was identified in terms of study design, sample type, platform used and method of analysis. Seven datasets specifically investigated transcriptional changes associated with NAI to malaria, with evidence supporting suppression of the innate pro-inflammatory response as an important mechanism for this in the majority of these studies. However, further interpretation of this body of work was limited by heterogeneity between studies and small sample sizes. Conclusions GEP in malaria is a potentially powerful tool, but to date studies have been hypothesis generating with small sample sizes and widely varying methodology. As CHMI studies are increasingly performed in endemic settings, there will be growing opportunity to use GEP to understand detailed time-course changes in host response and understand in greater detail the mechanisms of NAI.
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Affiliation(s)
- Susanne H Hodgson
- The Jenner Institute, University of Oxford, Old Road Campus Road Building, Off Roosevelt Drive, Oxford, OX3 7DQ, UK. .,Department of Infectious Diseases & Microbiology, Oxford University Hospitals Trust, Oxford, UK.
| | - Julius Muller
- The Jenner Institute, University of Oxford, Old Road Campus Road Building, Off Roosevelt Drive, Oxford, OX3 7DQ, UK
| | - Helen E Lockstone
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Adrian V S Hill
- The Jenner Institute, University of Oxford, Old Road Campus Road Building, Off Roosevelt Drive, Oxford, OX3 7DQ, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Kevin Marsh
- Department of Tropical Medicine, University of Oxford, Oxford, UK
| | - Simon J Draper
- The Jenner Institute, University of Oxford, Old Road Campus Road Building, Off Roosevelt Drive, Oxford, OX3 7DQ, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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Genomics of Particulate Matter Exposure Associated Cardiopulmonary Disease: A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16224335. [PMID: 31703266 PMCID: PMC6887978 DOI: 10.3390/ijerph16224335] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/28/2019] [Accepted: 10/30/2019] [Indexed: 12/25/2022]
Abstract
Particulate matter (PM) exposure is associated with the development of cardiopulmonary disease. Our group has studied the adverse health effects of World Trade Center particulate matter (WTC-PM) exposure on firefighters. To fully understand the complex interplay between exposure, organism, and resultant disease phenotype, it is vital to analyze the underlying role of genomics in mediating this relationship. A PubMed search was performed focused on environmental exposure, genomics, and cardiopulmonary disease. We included original research published within 10 years, on epigenetic modifications and specific genetic or allelic variants. The initial search resulted in 95 studies. We excluded manuscripts that focused on work-related chemicals, heavy metals and tobacco smoke as primary sources of exposure, as well as reviews, prenatal research, and secondary research studies. Seven full-text articles met pre-determined inclusion criteria, and were reviewed. The effects of air pollution were evaluated in terms of methylation (n = 3), oxidative stress (n = 2), and genetic variants (n = 2). There is evidence to suggest that genomics plays a meditating role in the formation of adverse cardiopulmonary symptoms and diseases that surface after exposure events. Genomic modifications and variations affect the association between environmental exposure and cardiopulmonary disease, but additional research is needed to further define this relationship.
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7
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Naimo GD, Guarnaccia M, Sprovieri T, Ungaro C, Conforti FL, Andò S, Cavallaro S. A Systems Biology Approach for Personalized Medicine in Refractory Epilepsy. Int J Mol Sci 2019; 20:E3717. [PMID: 31366017 PMCID: PMC6695675 DOI: 10.3390/ijms20153717] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 07/22/2019] [Accepted: 07/28/2019] [Indexed: 02/01/2023] Open
Abstract
Epilepsy refers to a common chronic neurological disorder that affects all age groups. Unfortunately, antiepileptic drugs are ineffective in about one-third of patients. The complex interindividual variability influences the response to drug treatment rendering the therapeutic failure one of the most relevant problems in clinical practice also for increased hospitalizations and healthcare costs. Recent advances in the genetics and neurobiology of epilepsies are laying the groundwork for a new personalized medicine, focused on the reversal or avoidance of the pathophysiological effects of specific gene mutations. This could lead to a significant improvement in the efficacy and safety of treatments for epilepsy, targeting the biological mechanisms responsible for epilepsy in each individual. In this review article, we focus on the mechanism of the epilepsy pharmacoresistance and highlight the use of a systems biology approach for personalized medicine in refractory epilepsy.
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Affiliation(s)
- Giuseppina Daniela Naimo
- Institute for Biomedical Research and Innovation, National Research Council, Contrada Burga, Piano Lago, 87050 Mangone (CS) and Via Paolo Gaifami 18, 95126 Catania, Italy
| | - Maria Guarnaccia
- Institute for Biomedical Research and Innovation, National Research Council, Contrada Burga, Piano Lago, 87050 Mangone (CS) and Via Paolo Gaifami 18, 95126 Catania, Italy
| | - Teresa Sprovieri
- Institute for Biomedical Research and Innovation, National Research Council, Contrada Burga, Piano Lago, 87050 Mangone (CS) and Via Paolo Gaifami 18, 95126 Catania, Italy
| | - Carmine Ungaro
- Institute for Biomedical Research and Innovation, National Research Council, Contrada Burga, Piano Lago, 87050 Mangone (CS) and Via Paolo Gaifami 18, 95126 Catania, Italy
| | - Francesca Luisa Conforti
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, 87036 Cosenza, Italy
| | - Sebastiano Andò
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, 87036 Cosenza, Italy
- Centro Sanitario, University of Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende (CS), Italy
| | - Sebastiano Cavallaro
- Institute for Biomedical Research and Innovation, National Research Council, Contrada Burga, Piano Lago, 87050 Mangone (CS) and Via Paolo Gaifami 18, 95126 Catania, Italy.
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8
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Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, Gruden K, Stitt M, Bolger ME, Usadel B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. MOLECULAR PLANT 2019; 12:879-892. [PMID: 30639314 DOI: 10.1016/j.molp.2019.01.003] [Citation(s) in RCA: 245] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 12/14/2018] [Accepted: 01/01/2019] [Indexed: 05/18/2023]
Abstract
Genome sequences from over 200 plant species have already been published, with this number expected to increase rapidly due to advances in sequencing technologies. Once a new genome has been assembled and the genes identified, the functional annotation of their putative translational products, proteins, using ontologies is of key importance as it places the sequencing data in a biological context. Furthermore, to keep pace with rapid production of genome sequences, this functional annotation process must be fully automated. Here we present a redesigned and significantly enhanced MapMan4 framework, together with a revised version of the associated online Mercator annotation tool. Compared with the original MapMan, the new ontology has been expanded almost threefold and enforces stricter assignment rules. This framework was then incorporated into Mercator4, which has been upgraded to reflect current knowledge across the land plant group, providing protein annotations for all embryophytes with a comparably high quality. The annotation process has been optimized to allow a plant genome to be annotated in a matter of minutes. The output results continue to be compatible with the established MapMan desktop application.
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Affiliation(s)
- Rainer Schwacke
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Gabriel Y Ponce-Soto
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kirsten Krause
- Department of Arctic and Marine Biology, The Arctic University of Norway, Biology Building, 9037 Tromsø, Norway
| | - Anthony M Bolger
- Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
| | - Borjana Arsova
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Asis Hallab
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kristina Gruden
- National Institute of Biology, Department of Biotechnology and Systems Biology, Večna Pot 111, 1000 Ljubljana, Slovenia
| | - Mark Stitt
- Max Planck Institute for Molecular Plant Physiology, Department of Systems Regulation, 14476 Potsdam-Golm, Germany
| | - Marie E Bolger
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany.
| | - Björn Usadel
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany; Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
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Abstract
Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.
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Affiliation(s)
- Hoe-Han Goh
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia.
| | - Chyan Leong Ng
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Kok-Keong Loke
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
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A compendium of physical exercise-related human genes: an 'omic scale analysis. Biol Sport 2017; 35:3-11. [PMID: 30237656 PMCID: PMC6135974 DOI: 10.5114/biolsport.2018.70746] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/11/2016] [Accepted: 06/05/2017] [Indexed: 12/14/2022] Open
Abstract
Regular exercise is an exogenous factor of gene regulation with numerous health benefits. The study aimed to evaluate human genes linked to physical exercise in an ‘omic scale, addressing biological questions to the generated database. Three literature databases were searched with the terms ‘exercise’, ‘fitness’, ‘physical activity’, ‘genetics’ and ‘gene expression’. For additional references, papers were scrutinized and a text-mining tool was used. Papers linking genes to exercise in humans through microarray, RNA-Seq, RT-PCR and genotyping studies were included. Genes were extracted from the collected literature, together with information on exercise protocol, experimental design, gender, age, number of individuals, analytical method, fold change and statistical data. The ‘omic scale dataset was characterized and evaluated with bioinformatics tools searching for gene expression patterns, functional meaning and gene clusters. As a result, a physical exercise-related human gene compendium was created, with data from 58 scientific papers and 5.147 genes functionally correlated with 17 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. While 50.9% of the gene set was up-regulated, 41.9% was down-regulated. 743 up- and 530 down-regulated clusters were found, some connected by regulatory networks. To summarize, up- and down-regulation was encountered, with a wide genomic distribution of the gene set and up- and down-regulated clusters possibly assembled by functional gene evolution. Physical exercise elicits a widespread response in gene expression.
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Mueller AJ, Tew SR, Vasieva O, Clegg PD, Canty-Laird EG. A systems biology approach to defining regulatory mechanisms for cartilage and tendon cell phenotypes. Sci Rep 2016; 6:33956. [PMID: 27670352 PMCID: PMC5037390 DOI: 10.1038/srep33956] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/05/2016] [Indexed: 12/20/2022] Open
Abstract
Phenotypic plasticity of adult somatic cells has provided emerging avenues for the development of regenerative therapeutics. In musculoskeletal biology the mechanistic regulatory networks of genes governing the phenotypic plasticity of cartilage and tendon cells has not been considered systematically. Additionally, a lack of strategies to effectively reproduce in vitro functional models of cartilage and tendon is retarding progress in this field. De- and redifferentiation represent phenotypic transitions that may contribute to loss of function in ageing musculoskeletal tissues. Applying a systems biology network analysis approach to global gene expression profiles derived from common in vitro culture systems (monolayer and three-dimensional cultures) this study demonstrates common regulatory mechanisms governing de- and redifferentiation transitions in cartilage and tendon cells. Furthermore, evidence of convergence of gene expression profiles during monolayer expansion of cartilage and tendon cells, and the expression of key developmental markers, challenges the physiological relevance of this culture system. The study also suggests that oxidative stress and PI3K signalling pathways are key modulators of in vitro phenotypes for cells of musculoskeletal origin.
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Affiliation(s)
- A. J. Mueller
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health & Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom
| | - S. R. Tew
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health & Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom
- The MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)
| | - O. Vasieva
- Institute of Integrative Biology, Biosciences Building, University of Liverpool, Crown St., Liverpool, L69 7ZB, United Kingdom
| | - P. D. Clegg
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health & Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom
- The MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)
| | - E. G. Canty-Laird
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health & Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom
- The MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)
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Rue-Albrecht K, McGettigan PA, Hernández B, Nalpas NC, Magee DA, Parnell AC, Gordon SV, MacHugh DE. GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data. BMC Bioinformatics 2016; 17:126. [PMID: 26968614 PMCID: PMC4788925 DOI: 10.1186/s12859-016-0971-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 02/25/2016] [Indexed: 02/06/2023] Open
Abstract
Background Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. Results We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. Conclusions GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0971-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kévin Rue-Albrecht
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.,Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK
| | - Paul A McGettigan
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.,Novartis Pharmaceuticals, Elm Park Business Campus, Merrion Road, Dublin 4, Ireland
| | - Belinda Hernández
- UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland.,UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Nicolas C Nalpas
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.,Proteome Center Tübingen, Interfaculty Institute for Cell Biology, University of Tübingen, Auf der Morgenstelle 15, 72076, Tübingen, Germany
| | - David A Magee
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Andrew C Parnell
- UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland
| | - Stephen V Gordon
- UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland.,UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - David E MacHugh
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland. .,UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland.
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14
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de Figueiredo P, Ficht TA, Rice-Ficht A, Rossetti CA, Adams LG. Pathogenesis and immunobiology of brucellosis: review of Brucella-host interactions. THE AMERICAN JOURNAL OF PATHOLOGY 2015; 185:1505-17. [PMID: 25892682 DOI: 10.1016/j.ajpath.2015.03.003] [Citation(s) in RCA: 272] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 02/10/2015] [Accepted: 03/02/2015] [Indexed: 01/18/2023]
Abstract
This review of Brucella-host interactions and immunobiology discusses recent discoveries as the basis for pathogenesis-informed rationales to prevent or treat brucellosis. Brucella spp., as animal pathogens, cause human brucellosis, a zoonosis that results in worldwide economic losses, human morbidity, and poverty. Although Brucella spp. infect humans as an incidental host, 500,000 new human infections occur annually, and no patient-friendly treatments or approved human vaccines are reported. Brucellae display strong tissue tropism for lymphoreticular and reproductive systems with an intracellular lifestyle that limits exposure to innate and adaptive immune responses, sequesters the organism from the effects of antibiotics, and drives clinical disease manifestations and pathology. Stealthy brucellae exploit strategies to establish infection, including i) evasion of intracellular destruction by restricting fusion of type IV secretion system-dependent Brucella-containing vacuoles with lysosomal compartments, ii) inhibition of apoptosis of infected mononuclear cells, and iii) prevention of dendritic cell maturation, antigen presentation, and activation of naive T cells, pathogenesis lessons that may be informative for other intracellular pathogens. Data sets of next-generation sequences of Brucella and host time-series global expression fused with proteomics and metabolomics data from in vitro and in vivo experiments now inform interactive cellular pathways and gene regulatory networks enabling full-scale systems biology analysis. The newly identified effector proteins of Brucella may represent targets for improved, safer brucellosis vaccines and therapeutics.
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Affiliation(s)
- Paul de Figueiredo
- Department of Veterinary Pathobiology, Texas A&M University and Texas AgriLife Research, College Station, Texas; Norman Borlaug Center, Texas A&M University, College Station, Texas; Department of Microbial Pathogenesis and Immunology, Texas A&M Health Science Center, Bryan, Texas
| | - Thomas A Ficht
- Department of Veterinary Pathobiology, Texas A&M University and Texas AgriLife Research, College Station, Texas
| | - Allison Rice-Ficht
- Department of Molecular and Cellular Medicine, Texas A&M Health Science Center, Bryan, Texas
| | - Carlos A Rossetti
- Institute of Pathobiology, CICVyA-CNIA, National Institute of Animal Agriculture Technology (INTA), Buenos Aires, Argentina
| | - L Garry Adams
- Department of Veterinary Pathobiology, Texas A&M University and Texas AgriLife Research, College Station, Texas.
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Curtin CD, Pretorius IS. Genomic insights into the evolution of industrial yeast species Brettanomyces bruxellensis. FEMS Yeast Res 2014; 14:997-1005. [PMID: 25142832 DOI: 10.1111/1567-1364.12198] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 08/13/2014] [Indexed: 12/14/2022] Open
Abstract
Brettanomyces bruxellensis, like its wine yeast counterpart Saccharomyces cerevisiae, is intrinsically linked with industrial fermentations. In wine, B. bruxellensis is generally considered to contribute negative influences on wine quality, whereas for some styles of beer, it is an essential contributor. More recently, it has shown some potential for bioethanol production. Our relatively poor understanding of B. bruxellensis biology, at least when compared with S. cerevisiae, is partly due to a lack of laboratory tools. As it is a nonmodel organism, efforts to develop methods for sporulation and transformation have been sporadic and largely unsuccessful. Recent genome sequencing efforts are now providing B. bruxellensis researchers unprecedented access to gene catalogues, the possibility of performing transcriptomic studies and new insights into evolutionary drivers. This review summarises these findings, emphasises the rich data sets already available yet largely unexplored and looks over the horizon at what might be learnt soon through comprehensive population genomics of B. bruxellensis and related species.
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16
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Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A, Ballestar E, Bongcam-Rudloff E, Conesa A, Tegnér J. Data integration in the era of omics: current and future challenges. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 2:I1. [PMID: 25032990 PMCID: PMC4101704 DOI: 10.1186/1752-0509-8-s2-i1] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
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.
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