51
|
Wang Q, Peng WX, Wang L, Ye L. Toward multiomics-based next-generation diagnostics for precision medicine. Per Med 2019; 16:157-170. [PMID: 30816060 DOI: 10.2217/pme-2018-0085] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Our healthcare system is experiencing a paradigm shift to precision medicine, aiming at an early prediction of individual disease risks and targeted interventions. Whole-genome sequencing is currently gaining momentum, as it has the potential to capture all classes of genetic variation, thus providing a more complete picture of the individual's genetic makeup, which could be utilized in genetic testing; however, this will also lead to difficulties in interpreting the test results, necessitating careful integration of genomic data with other layers of information, both molecular multiomics measurements of epigenome, transcriptome, proteome, metabolome and even microbiome, as well as comprehensive information on diet, lifestyle and environment. Overall, the translation of patient-specific data into actionable diagnostic tools will be a challenging task, requiring expertise from multiple disciplines, secure data sharing in large reference databases and a strong computational infrastructure.
Collapse
Affiliation(s)
- Qi Wang
- Department of Emergency Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou 310007, Zhejiang Province, China
| | - Wei-Xian Peng
- Department of Emergency Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou 310007, Zhejiang Province, China
| | - Lu Wang
- Department of Emergency Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou 310007, Zhejiang Province, China
| | - Li Ye
- Department of Nursing, Tongde Hospital of Zhejiang Province, Hangzhou 310012, Zhejiang Province, China
| |
Collapse
|
52
|
Siahpirani AF, Chasman D, Roy S. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks. Methods Mol Biol 2019; 1883:161-194. [PMID: 30547400 DOI: 10.1007/978-1-4939-8882-2_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.
Collapse
Affiliation(s)
- Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.,Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
| |
Collapse
|
53
|
Erola P, Bonnet E, Michoel T. Learning Differential Module Networks Across Multiple Experimental Conditions. Methods Mol Biol 2019; 1883:303-321. [PMID: 30547406 DOI: 10.1007/978-1-4939-8882-2_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.
Collapse
Affiliation(s)
- Pau Erola
- Division of Genetics and Genomics, Roslin Institute, University of Edinburgh, Midlothian, Scotland, UK
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, Direction de la Recherche Fondamentale, CEA, Evry, France
| | - Tom Michoel
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Midlothian, Scotland, UK.
- Current Address: Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| |
Collapse
|
54
|
An Improved Method for Prediction of Cancer Prognosis by Network Learning. Genes (Basel) 2018; 9:genes9100478. [PMID: 30279327 PMCID: PMC6210393 DOI: 10.3390/genes9100478] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 09/21/2018] [Accepted: 09/27/2018] [Indexed: 01/01/2023] Open
Abstract
Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data.
Collapse
|
55
|
Perakakis N, Yazdani A, Karniadakis GE, Mantzoros C. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism 2018; 87:A1-A9. [PMID: 30098323 PMCID: PMC6325641 DOI: 10.1016/j.metabol.2018.08.002] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 08/07/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Nikolaos Perakakis
- Department of Endocrinology, VA Boston Healthcare System, Jamaica Plain, Boston, MA 02130, USA; Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Alireza Yazdani
- Division of Applied Mathematics, Brown University, Providence, RI 02906, USA
| | | | - Christos Mantzoros
- Department of Endocrinology, VA Boston Healthcare System, Jamaica Plain, Boston, MA 02130, USA; Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
| |
Collapse
|
56
|
Moirangthem A, Wang X, Yan IK, Patel T. Network analyses-based identification of circular ribonucleic acid-related pathways in intrahepatic cholangiocarcinoma. Tumour Biol 2018; 40:1010428318795761. [PMID: 30168369 DOI: 10.1177/1010428318795761] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Circular ribonucleic acids are non-coding ribonucleic acids that can be identified from genome sequencing studies. Although they can be readily detected, their regulation and functional role in human diseases such as cancer are unknown. Using a systematic approach, we analyzed ribonucleic acid-sequencing data from a well-characterized cohort of intrahepatic cholangiocarcinoma to identify genetic pathways related to circular ribonucleic acids. Although the expression of most circular ribonucleic acids was similar in both the cancer and non-cancer tissues, expression of circ2174 was significantly increased in cancer tissues. Network analysis of co-related genes identified several pathways associated with circ2174, and common regulatory mediators between genes in these pathways and circ2174. Among these, alterations in several genes involved in interleukin-16 signaling responses such Lck, interleukin-16, and macrophage inflammatory protein-1-beta were the most prominent. Octamer transcription factor (Oct)-2 was identified as a signal transducer that was common to both circ2174 and interleukin-16. Circ2174 has sequence complementarity to miR149 which can target Oct-2. These data suggest a mechanism whereby circ2174 can act as a sponge to regulate the expression of miR149, and thereby modulate Oct-2 and interleukin-16 signaling pathways in cholangiocarcinoma.
Collapse
Affiliation(s)
| | - Xue Wang
- 2 Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Irene K Yan
- 1 Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Tushar Patel
- 1 Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA.,3 Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| |
Collapse
|
57
|
He A, Ning Y, Wen Y, Cai Y, Xu K, Cai Y, Han J, Liu L, Du Y, Liang X, Li P, Fan Q, Hao J, Wang X, Guo X, Ma T, Zhang F. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018; 7:343-350. [PMID: 29922454 PMCID: PMC5987683 DOI: 10.1302/2046-3758.75.bjr-2017-0284.r1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Aim Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.
Collapse
Affiliation(s)
- A He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Y Ning
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Y Cai
- Department of Orthopaedics, The First Affiliated Hospital, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - K Xu
- Department of Joint Surgery, Xi'an Hong-Hui Hospital, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Y Cai
- Department of Joint Surgery, Xi'an Hong-Hui Hospital, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - J Han
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - L Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Y Du
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - X Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - P Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Q Fan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - J Hao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - X Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - X Guo
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - T Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| |
Collapse
|
58
|
Lu Y, Zhou X, Nardini C. Dissection of the module network implementation "LemonTree": enhancements towards applications in metagenomics and translation in autoimmune maladies. MOLECULAR BIOSYSTEMS 2018; 13:2083-2091. [PMID: 28809429 DOI: 10.1039/c7mb00248c] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Under the current deluge of omics, module networks distinctively emerge as methods capable of not only identifying inherently coherent groups (modules), thus reducing dimensionality, but also hypothesizing cause-effect relationships between modules and their regulators. Module networks were first designed in the transcriptomic era and further exploited in the multi-omic context to assess (for example) miRNA regulation of gene expression. Despite a number of available implementations, expansion of module networks to other omics is constrained by a limited characterization of the solutions' (modules plus regulators) accuracy and stability - an immediate need for the better characterization of molecular biology complexity in silico. We hence carefully assessed for LemonTree - a popular and open source module network implementation - the dependency of the software performances (sensitivity, specificity, false discovery rate, solutions' stability) on the input parameters and on the data quality (sample size, expression noise) based on synthetic and real data. In the process, we uncovered and fixed an issue in the code for the regulator assignment procedure. We concluded this evaluation with a table of recommended parameter settings. Finally, we applied these recommended settings to gut-intestinal metagenomic data from rheumatoid arthritis patients, to characterize the evolution of the gut-intestinal microbiome under different pharmaceutical regimens (methotrexate and prednisone) and we inferred innovative clinical recommendations with therapeutic potential, based on the computed module network.
Collapse
Affiliation(s)
- Youtao Lu
- CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | | | | |
Collapse
|
59
|
Dihazi H, Asif AR, Beißbarth T, Bohrer R, Feussner K, Feussner I, Jahn O, Lenz C, Majcherczyk A, Schmidt B, Schmitt K, Urlaub H, Valerius O. Integrative omics - from data to biology. Expert Rev Proteomics 2018; 15:463-466. [DOI: 10.1080/14789450.2018.1476143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Hassan Dihazi
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Nephrology and Rheumatology, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Abdul R. Asif
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Tim Beißbarth
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Department of Medical Statistics, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Rainer Bohrer
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Gesellschaft für Wissenschaftlische Datenverarbeitung mbH, Göttingen, Germany
| | - Kirstin Feussner
- Göttingen Metabolomics and Lipidomics Platform (GMLP), Göttingen, Germany
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany
| | - Ivo Feussner
- Göttingen Metabolomics and Lipidomics Platform (GMLP), Göttingen, Germany
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany
| | - Olaf Jahn
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Christof Lenz
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
- Bioanalytical Mass Spectrometry, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Andrzej Majcherczyk
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Büsgen-Institute, Section Molecular Wood Biotechnology and Technical Mycology, University of Göttingen, Göttingen, Germany
| | - Bernhard Schmidt
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Department of Cellular Biochemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Kerstin Schmitt
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
| | - Henning Urlaub
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
- Bioanalytical Mass Spectrometry, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Oliver Valerius
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
| |
Collapse
|
60
|
Sabino M, Carmelo VAO, Mazzoni G, Cappelli K, Capomaccio S, Ajmone-Marsan P, Verini-Supplizi A, Trabalza-Marinucci M, Kadarmideen HN. Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils. BMC Genomics 2018; 19:236. [PMID: 29618337 PMCID: PMC5885410 DOI: 10.1186/s12864-018-4632-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 03/27/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Essential oil (EO) dietary supplementation is a new strategy to improve animal health. EO compounds have antiparasitic, antimicrobial, antiviral, antimycotic, antioxidant and anti-inflammatory proprieties. Nutrigenomics investigations represent innovative approaches in understanding the relation between diet effect and gene expression related to the animal performance. Few nutrigenomics studies have used a high-throughput RNA-Sequencing (RNA-Seq) approach, despite great potential of RNA-Seq data in gene expression quantification and in co-expression network analyses. Our aim is to use the potential of RNA-Sequencing data in order to evaluate the effect of an EO supplementary diet on gene expression in both lamb liver and muscle. RESULTS Using a treatment and sex interaction model, 13 and 4 differentially expressed genes were identified in liver and muscle respectively. Sex-specific differentially expressed (DE) genes were identified in both sexes. Using network based analysis, different clusters of co-expressed genes that were highly correlated to the diet were detected in males vs. females, in agreement with DE analysis. A total of five regulatory genes in liver tissue associated to EO diet were identified: DNAJB9, MANF, UFM1, CTNNLA1 and NFX1. Our study reveals a sex-dependent effect of EO diet in both tissues, and an influence on the expression of genes mainly involved in immune, inflammatory and stress pathway. CONCLUSION Our analysis suggests a sex-dependent effect of the EO dietary supplementation on the expression profile of both liver and muscle tissues. We hypothesize that the presence of EOs could have beneficial effects on wellness of male lamb and further analyses are needed to understand the biological mechanisms behind the different effect of EO metabolites based on sex. Using lamb as a model for nutrigenomics studies, it could be interesting to investigate the effects of EO diets in other species and in humans.
Collapse
Affiliation(s)
- Marcella Sabino
- Dipartimento di Medicina Veterinaria, University of Perugia, Perugia, Italy
| | | | - Gianluca Mazzoni
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Copenhagen, Denmark
| | - Katia Cappelli
- Dipartimento di Medicina Veterinaria, University of Perugia, Perugia, Italy
| | - Stefano Capomaccio
- Dipartimento di Medicina Veterinaria, University of Perugia, Perugia, Italy
| | - Paolo Ajmone-Marsan
- Istituto di Zootecnica, Catholic University of the Sacred Heart, Piacenza, Italy
| | | | | | - Haja N Kadarmideen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Copenhagen, Denmark.
| |
Collapse
|
61
|
Zeng ISL, Lumley T. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights 2018; 12:1177932218759292. [PMID: 29497285 PMCID: PMC5824897 DOI: 10.1177/1177932218759292] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 01/24/2018] [Indexed: 12/14/2022] Open
Abstract
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
Collapse
Affiliation(s)
- Irene Sui Lan Zeng
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
| | - Thomas Lumley
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
62
|
Noell G, Faner R, Agustí A. From systems biology to P4 medicine: applications in respiratory medicine. Eur Respir Rev 2018; 27:27/147/170110. [PMID: 29436404 PMCID: PMC9489012 DOI: 10.1183/16000617.0110-2017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 11/30/2017] [Indexed: 12/22/2022] Open
Abstract
Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field. Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x
Collapse
Affiliation(s)
- Guillaume Noell
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain
| | - Rosa Faner
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain
| | - Alvar Agustí
- Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain .,CIBER Enfermedades Respiratorias (CIBERES), Barcelona, Spain.,Respiratory Institute, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain
| |
Collapse
|
63
|
Lu X, Li X, Liu P, Qian X, Miao Q, Peng S. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes. Molecules 2018; 23:molecules23020183. [PMID: 29364829 PMCID: PMC6099653 DOI: 10.3390/molecules23020183] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/29/2017] [Accepted: 01/08/2018] [Indexed: 11/16/2022] Open
Abstract
With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profiles and copy number variation (CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.
Collapse
Affiliation(s)
- Xinguo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (X.L.); (X.Q.); (Q.M.)
- Correspondence: (X.L.); (S.P.); Tel.: +86-731-88821907(X.L.)
| | - Xing Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (X.L.); (X.Q.); (Q.M.)
| | - Ping Liu
- Hunan Want Want Hospital, Changsha 410006, China;
| | - Xin Qian
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (X.L.); (X.Q.); (Q.M.)
| | - Qiumai Miao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (X.L.); (X.Q.); (Q.M.)
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (X.L.); (X.Q.); (Q.M.)
- School of Computer Science, National University of Defense Technology, Changsha 410073, China
- Correspondence: (X.L.); (S.P.); Tel.: +86-731-88821907(X.L.)
| |
Collapse
|
64
|
|
65
|
Integrated regulatory network reveals novel candidate regulators in the development of negative energy balance in cattle. Animal 2017; 12:1196-1207. [PMID: 29282162 DOI: 10.1017/s1751731117003524] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Negative energy balance (NEB) is an altered metabolic state in modern high-yielding dairy cows. This metabolic state occurs in the early postpartum period when energy demands for milk production and maintenance exceed that of energy intake. Negative energy balance or poor adaptation to this metabolic state has important effects on the liver and can lead to metabolic disorders and reduced fertility. The roles of regulatory factors, including transcription factors (TFs) and micro RNAs (miRNAs) have often been separately studied for evaluating of NEB. However, adaptive response to NEB is controlled by complex gene networks and still not fully understood. In this study, we aimed to discover the integrated gene regulatory networks involved in NEB development in liver tissue. We downloaded data sets including mRNA and miRNA expression profiles related to three and four cows with severe and moderate NEB, respectively. Our method integrated two independent types of information: module inference network by TFs, miRNAs and mRNA expression profiles (RNA-seq data) and computational target predictions. In total, 176 modules were predicted by using gene expression data and 64 miRNAs and 63 TFs were assigned to these modules. By using our integrated computational approach, we identified 13 TF-module and 19 miRNA-module interactions. Most of these modules were associated with liver metabolic processes as well as immune and stress responses, which might play crucial roles in NEB development. Literature survey results also showed that several regulators and gene targets have already been characterized as important factors in liver metabolic processes. These results provided novel insights into regulatory mechanisms at the TF and miRNA levels during NEB. In addition, the method described in this study seems to be applicable to construct integrated regulatory networks for different diseases or disorders.
Collapse
|
66
|
Adeola HA, Van Wyk JC, Arowolo A, Ngwanya RM, Mkentane K, Khumalo NP. Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics. Proteomics Clin Appl 2017; 12. [PMID: 28960873 DOI: 10.1002/prca.201700048] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/09/2017] [Indexed: 01/22/2023]
Abstract
The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.
Collapse
Affiliation(s)
- Henry A Adeola
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Jennifer C Van Wyk
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Afolake Arowolo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Reginald M Ngwanya
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Khwezikazi Mkentane
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Nonhlanhla P Khumalo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| |
Collapse
|
67
|
Behdani E, Bakhtiarizadeh MR. Construction of an integrated gene regulatory network link to stress-related immune system in cattle. Genetica 2017; 145:441-454. [PMID: 28825201 DOI: 10.1007/s10709-017-9980-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 08/14/2017] [Indexed: 01/01/2023]
Abstract
The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.
Collapse
Affiliation(s)
- Elham Behdani
- Department of Animal Sciences, College of Agriculture and Natural Resources, Ramin University, Khozestan, Iran
| | | |
Collapse
|
68
|
Marchi FA, Martins DC, Barros-Filho MC, Kuasne H, Busso Lopes AF, Brentani H, Trindade Filho JCS, Guimarães GC, Faria EF, Scapulatempo-Neto C, Lopes A, Rogatto SR. Multidimensional integrative analysis uncovers driver candidates and biomarkers in penile carcinoma. Sci Rep 2017; 7:6707. [PMID: 28751665 PMCID: PMC5532302 DOI: 10.1038/s41598-017-06659-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 06/19/2017] [Indexed: 01/24/2023] Open
Abstract
Molecular data generation and their combination in penile carcinomas (PeCa), a significant public health problem in poor and underdeveloped countries, remain virtually unexplored. An integrativemethodology combin ing genome-wide copy number alteration, DNA methylation, miRNA and mRNA expression analysis was performed in a set of 20 usual PeCa. The well-ranked 16 driver candidates harboring genomic alterations and regulated by a set of miRNAs, including hsa-miR-31, hsa-miR-34a and hsa-miR-130b, were significantly associated with over-represented pathways in cancer, such as immune-inflammatory system, apoptosis and cell cycle. Modules of co-expressed genes generated from expression matrix were associated with driver candidates and classified according to the over-representation of passengers, thus suggesting an alteration of the pathway dynamics during the carcinogenesis. This association resulted in 10 top driver candidates (AR, BIRC5, DNMT3B, ERBB4, FGFR1, PML, PPARG, RB1, TNFSF10 and STAT1) selected and confirmed as altered in an independent set of 33 PeCa samples. In addition to the potential driver genes herein described, shorter overall survival was associated with BIRC5 and DNMT3B overexpression (log-rank test, P = 0.026 and P = 0.002, respectively) highlighting its potential as novel prognostic marker for penile cancer.
Collapse
Affiliation(s)
| | - David Correa Martins
- Center of Mathematics, Computing and Cognition, Federal University of ABC - UFABC, Santo André, SP, Brazil
| | | | | | | | - Helena Brentani
- Department of Psychiatry, Medical School, University of Sao Paulo - USP, São Paulo, SP, Brazil
| | | | | | - Eliney F Faria
- Department of Urology, Barretos Cancer Hospital, Barretos, São Paulo, Brazil
| | | | - Ademar Lopes
- A.C.Camargo Cancer Center, São Paulo, SP, Brazil
| | - Silvia Regina Rogatto
- Department of Urology, Faculty of Medicine, Sao Paulo State University - UNESP, Botucatu, SP, Brazil.
- Department of Clinical Genetics, Vejle Hospital and Institute of Regional Health, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
69
|
Huang S, Chaudhary K, Garmire LX. More Is Better: Recent Progress in Multi-Omics Data Integration Methods. Front Genet 2017; 8:84. [PMID: 28670325 PMCID: PMC5472696 DOI: 10.3389/fgene.2017.00084] [Citation(s) in RCA: 389] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 06/01/2017] [Indexed: 01/20/2023] Open
Abstract
Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.
Collapse
Affiliation(s)
- Sijia Huang
- Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, United States.,Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at ManoaHonolulu, HI, United States
| | - Kumardeep Chaudhary
- Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, United States
| | - Lana X Garmire
- Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, United States.,Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at ManoaHonolulu, HI, United States.,Department of Obstetrics, Gynecology, and Women's Health, John A. Burns School of Medicine, University of Hawaii at ManoaHonolulu, HI, United States
| |
Collapse
|
70
|
Malusa F, Taranta M, Zaki N, Cinti C, Capobianco E. Time-course gene profiling and networks in demethylated retinoblastoma cell line. Oncotarget 2016; 6:23688-707. [PMID: 26143641 PMCID: PMC4695145 DOI: 10.18632/oncotarget.4644] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 05/31/2015] [Indexed: 02/06/2023] Open
Abstract
Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.
Collapse
Affiliation(s)
- Federico Malusa
- Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology, CNR, Pisa, Italy
| | - Monia Taranta
- Experimental Oncology Unit, Institute of Clinical Physiology, CNR, Siena, Italy
| | - Nazar Zaki
- College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, UAE
| | - Caterina Cinti
- Experimental Oncology Unit, Institute of Clinical Physiology, CNR, Siena, Italy
| | - Enrico Capobianco
- Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology, CNR, Pisa, Italy.,Center for Computational Science (CCS), University of Miami, Miami, FL, USA
| |
Collapse
|
71
|
Arhondakis S, Bita CE, Perrakis A, Manioudaki ME, Krokida A, Kaloudas D, Kalaitzis P. In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening. FRONTIERS IN PLANT SCIENCE 2016; 7:1234. [PMID: 27625653 PMCID: PMC5003879 DOI: 10.3389/fpls.2016.01234] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Accepted: 08/03/2016] [Indexed: 05/18/2023]
Abstract
Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.
Collapse
|
72
|
Alexandre PA, Kogelman LJA, Santana MHA, Passarelli D, Pulz LH, Fantinato-Neto P, Silva PL, Leme PR, Strefezzi RF, Coutinho LL, Ferraz JBS, Eler JP, Kadarmideen HN, Fukumasu H. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC Genomics 2015; 16:1073. [PMID: 26678995 PMCID: PMC4683712 DOI: 10.1186/s12864-015-2292-8] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 12/14/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The selection of beef cattle for feed efficiency (FE) traits is very important not only for productive and economic efficiency but also for reduced environmental impact of livestock. Considering that FE is multifactorial and expensive to measure, the aim of this study was to identify biological functions and regulatory genes associated with this phenotype. RESULTS Eight genes were differentially expressed between high and low feed efficient animals (HFE and LFE, respectively). Co-expression analyses identified 34 gene modules of which 4 were strongly associated with FE traits. They were mainly enriched for inflammatory response or inflammation-related terms. We also identified 463 differentially co-expressed genes which were functionally enriched for immune response and lipid metabolism. A total of 8 key regulators of gene expression profiles affecting FE were found. The LFE animals had higher feed intake and increased subcutaneous and visceral fat deposition. In addition, LFE animals showed higher levels of serum cholesterol and liver injury biomarker GGT. Histopathology of the liver showed higher percentage of periportal inflammation with mononuclear infiltrate. CONCLUSION Liver transcriptomic network analysis coupled with other results demonstrated that LFE animals present altered lipid metabolism and increased hepatic periportal lesions associated with an inflammatory response composed mainly by mononuclear cells. We are now focusing to identify the causes of increased liver lesions in LFE animals.
Collapse
Affiliation(s)
- Pamela A Alexandre
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil. .,Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Lisette J A Kogelman
- Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Miguel H A Santana
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Danielle Passarelli
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Lidia H Pulz
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Paulo Fantinato-Neto
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Paulo L Silva
- Department of Animal Sciences, School of Animal Science and Food Engineering, University of São Paulo, Pirassunung, Sao Paulo, Brazil.
| | - Paulo R Leme
- Department of Animal Sciences, School of Animal Science and Food Engineering, University of São Paulo, Pirassunung, Sao Paulo, Brazil.
| | - Ricardo F Strefezzi
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Luiz L Coutinho
- Department of Animal Sciences, ESALQ, University of Sao Paulo, Piracicaba, Sao Paulo, Brazil.
| | - José B S Ferraz
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Joanie P Eler
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| | - Haja N Kadarmideen
- Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Heidge Fukumasu
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Av. Duque de Caxias Norte, 225, Pirassununga, São Paulo, 13635-900, Brazil.
| |
Collapse
|
73
|
Bai Y, Dougherty L, Cheng L, Zhong GY, Xu K. Uncovering co-expression gene network modules regulating fruit acidity in diverse apples. BMC Genomics 2015; 16:612. [PMID: 26276125 PMCID: PMC4537561 DOI: 10.1186/s12864-015-1816-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2015] [Accepted: 08/05/2015] [Indexed: 11/10/2022] Open
Abstract
Background Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Results Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P < 0.001) correlation with malate. Of these, the Ma1 containing module (Turquoise) of 336 genes showed the highest correlation (0.79). We also identified 12 intramodular hub genes from each of the five modules and 18 enriched gene ontology (GO) terms and MapMan sub-bines, including two GO terms (GO:0015979 and GO:0009765) and two MapMap sub-bins (1.3.4 and 1.1.1.1) related to photosynthesis in module Turquoise. Using Lemon-Tree algorithms, we identified 12 regulator genes of probabilistic scores 35.5–81.0, including MDP0000525602 (a LLR receptor kinase), MDP0000319170 (an IQD2-like CaM binding protein) and MDP0000190273 (an EIN3-like transcription factor) of greater interest for being one of the 18 MSAGs or one of the 12 intramodular hub genes in Turquoise, and/or a regulator to the cluster containing Ma1. Conclusions The most relevant finding of this study is the identification of the MSAGs, intramodular hub genes, enriched photosynthesis related processes, and regulator genes in a WGCNA module Turquoise that not only encompasses Ma1 but also shows the highest modular correlation with acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1816-6) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Yang Bai
- Horticulture Section, School of Integrative Plant Science, Cornell University, New York State Agricultural Experiment Station, Geneva, NY, 14456, USA.
| | - Laura Dougherty
- Horticulture Section, School of Integrative Plant Science, Cornell University, New York State Agricultural Experiment Station, Geneva, NY, 14456, USA.
| | - Lailiang Cheng
- Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Gan-Yuan Zhong
- USDA-ARS, Plant Genetic resource and Grape Genetic Research Units, Geneva, NY, 14456, USA.
| | - Kenong Xu
- Horticulture Section, School of Integrative Plant Science, Cornell University, New York State Agricultural Experiment Station, Geneva, NY, 14456, USA.
| |
Collapse
|