1
|
Wang W, Zheng Y, Qiu L, Yang D, Zhao Z, Gao Y, Meng R, Zhao H, Zhang S. Genome-wide identification of the SAUR gene family and screening for SmSAURs involved in root development in Salvia miltiorrhiza. PLANT CELL REPORTS 2024; 43:165. [PMID: 38861173 DOI: 10.1007/s00299-024-03260-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
KEY MESSAGE SmSAUR4, SmSAUR18, SmSAUR28, SmSAUR37, and SmSAUR38 were probably involved in the auxin-mediated root development in Salvia miltiorrhiza. Salvia miltiorrhiza is a widely utilized medicinal plant in China. Its roots and rhizomes are the main medicinal portions and are closely related to the quality of this herb. Previous studies have revealed that auxin plays pivotal roles in S. miltiorrhiza root development. Whether small auxin-up RNA genes (SAURs), which are crucial early auxin response genes, are involved in auxin-mediated root development in S. miltiorrhiza is worthy of investigation. In this study, 55 SmSAUR genes in S. miltiorrhiza were identified, and their physical and chemical properties, gene structure, cis-acting elements, and evolutionary relationships were analyzed. The expression levels of SmSAUR genes in different organs of S. miltiorrhiza were detected using RNA-seq combined with qRT‒PCR. The root development of S. miltiorrhiza seedlings was altered by the application of indole-3-acetic acid (IAA), and Pearson correlation coefficient analysis was conducted to screen SmSAURs that potentially participate in this physiological process. The diameter of primary lateral roots was positively correlated with SmSAUR4. The secondary lateral root number was positively correlated with SmSAUR18 and negatively correlated with SmSAUR4. The root length showed a positive correlation with SmSAUR28 and SmSAUR37 and a negative correlation with SmSAUR38. The fresh root biomass exhibited a positive correlation with SmSAUR38 and a negative correlation with SmSAUR28. The aforementioned SmSAURs were likely involved in auxin-mediated root development in S. miltiorrhiza. Our study provides a comprehensive overview of SmSAURs and provides the groundwork for elucidating the molecular mechanism underlying root morphogenesis in this species.
Collapse
Affiliation(s)
- Wei Wang
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Yuwei Zheng
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Lin Qiu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Dongfeng Yang
- Key Laboratory of Plant Secondary Metabolism and Regulation of Zhejiang Province, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China
| | - Ziyang Zhao
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Yuanyuan Gao
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Ru Meng
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Hongguang Zhao
- Shaanxi Tasly Plants Pharmaceutical Co., Ltd., Shangluo, 726000, Shaanxi, China
| | - Shuncang Zhang
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, Jiangsu, China.
| |
Collapse
|
2
|
May L, Chu CF, Zielinski CE. Single-Cell RNA Sequencing Reveals HIF1A as a Severity-Sensitive Immunological Scar in Circulating Monocytes of Convalescent Comorbidity-Free COVID-19 Patients. Cells 2024; 13:300. [PMID: 38391913 PMCID: PMC10886588 DOI: 10.3390/cells13040300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/20/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by a wide range of clinical symptoms and a poorly predictable disease course. Although in-depth transcriptomic investigations of peripheral blood samples from COVID-19 patients have been performed, the detailed molecular mechanisms underlying an asymptomatic, mild or severe disease course, particularly in patients without relevant comorbidities, remain poorly understood. While previous studies have mainly focused on the cellular and molecular dissection of ongoing COVID-19, we set out to characterize transcriptomic immune cell dysregulation at the single-cell level at different time points in patients without comorbidities after disease resolution to identify signatures of different disease severities in convalescence. With single-cell RNA sequencing, we reveal a role for hypoxia-inducible factor 1-alpha (HIF1A) as a severity-sensitive long-term immunological scar in circulating monocytes of convalescent COVID-19 patients. Additionally, we show that circulating complexes formed by monocytes with either T cells or NK cells represent a characteristic cellular marker in convalescent COVID-19 patients irrespective of their preceding symptom severity. Together, these results provide cellular and molecular correlates of recovery from COVID-19 and could help in immune monitoring and in the design of new treatment strategies.
Collapse
Affiliation(s)
- Lilly May
- Leibniz Institute for Natural Products Research and Infection Biology, Department of Infection Immunology, 07745 Jena, Germany; (L.M.); (C.-F.C.)
- Center for Translational Cancer Research (TranslaTUM) & Institute of Virology, Technical University of Munich, 81675 Munich, Germany
| | - Chang-Feng Chu
- Leibniz Institute for Natural Products Research and Infection Biology, Department of Infection Immunology, 07745 Jena, Germany; (L.M.); (C.-F.C.)
- Center for Translational Cancer Research (TranslaTUM) & Institute of Virology, Technical University of Munich, 81675 Munich, Germany
| | - Christina E. Zielinski
- Leibniz Institute for Natural Products Research and Infection Biology, Department of Infection Immunology, 07745 Jena, Germany; (L.M.); (C.-F.C.)
- Center for Translational Cancer Research (TranslaTUM) & Institute of Virology, Technical University of Munich, 81675 Munich, Germany
- Department of Microbiology, Friedrich Schiller University, 07743 Jena, Germany
| |
Collapse
|
3
|
Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
Collapse
Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| |
Collapse
|
4
|
Wu C, Liu H, Zhan Z, Zhang X, Zhang M, You J, Ma J. Unveiling dysregulated lncRNAs and networks in non-syndromic cleft lip with or without cleft palate pathogenesis. Sci Rep 2024; 14:1047. [PMID: 38200098 PMCID: PMC10781966 DOI: 10.1038/s41598-024-51747-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/09/2024] [Indexed: 01/12/2024] Open
Abstract
Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common congenital facial malformation with a complex, incompletely understood origin. Long noncoding RNAs (lncRNAs) have emerged as pivotal regulators of gene expression, potentially shedding light on NSCL/P's etiology. This study aimed to identify critical lncRNAs and construct regulatory networks to unveil NSCL/P's underlying molecular mechanisms. Integrating gene expression profiles from the Gene Expression Omnibus (GEO) database, we pinpointed 30 dysregulated NSCL/P-associated lncRNAs. Subsequent analyses enabled the creation of competing endogenous RNA (ceRNA) networks, lncRNA-RNA binding protein (RBP) interaction networks, and lncRNA cis and trans regulation networks. RT-qPCR was used to examine the regulatory networks of lncRNA in vivo and in vitro. Furthermore, protein levels of lncRNA target genes were validated in human NSCL/P tissue samples and murine palatal shelves. Consequently, two lncRNAs and three mRNAs: FENDRR (log2FC = - 0.671, P = 0.040), TPT1-AS1 (log2FC = 0.854, P = 0.003), EIF3H (log2FC = - 1.081, P = 0.041), RBBP6 (log2FC = 0.914, P = 0.037), and SRSF1 (log2FC = 0.763, P = 0.026) emerged as potential contributors to NSCL/P pathogenesis. Functional enrichment analyses illuminated the biological functions and pathways associated with these lncRNA-related networks in NSCL/P. In summary, this study comprehensively delineates the dysregulated transcriptional landscape, identifies associated lncRNAs, and reveals pivotal sub-networks relevant to NSCL/P development, aiding our understanding of its molecular progression and setting the stage for further exploration of lncRNA and mRNA regulation in NSCL/P.
Collapse
Affiliation(s)
- Caihong Wu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
- Stomatological Hospital affiliated Suzhou Vocational Health College, Suzhou, China
| | - Haojie Liu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Zhuorong Zhan
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Xinyu Zhang
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Mengnan Zhang
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Jiawen You
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Junqing Ma
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.
- Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China.
| |
Collapse
|
5
|
Singh V, Singh V. Inferring Interaction Networks from Transcriptomic Data: Methods and Applications. Methods Mol Biol 2024; 2812:11-37. [PMID: 39068355 DOI: 10.1007/978-1-0716-3886-6_2] [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] [Indexed: 07/30/2024]
Abstract
Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.
Collapse
Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.
| |
Collapse
|
6
|
Cao X, Zhang L, Islam MK, Zhao M, He C, Zhang K, Liu S, Sha Q, Wei H. TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization. NAR Genom Bioinform 2023; 5:lqad083. [PMID: 37711605 PMCID: PMC10498345 DOI: 10.1093/nargab/lqad083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/30/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023] Open
Abstract
Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF-TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.
Collapse
Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Ling Zhang
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| | - Md Khairul Islam
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| | - Mingxia Zhao
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Cheng He
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Kui Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Hairong Wei
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| |
Collapse
|
7
|
Pergola G, Parihar M, Sportelli L, Bharadwaj R, Borcuk C, Radulescu E, Bellantuono L, Blasi G, Chen Q, Kleinman JE, Wang Y, Sripathy SR, Maher BJ, Monaco A, Rossi F, Shin JH, Hyde TM, Bertolino A, Weinberger DR. Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions. SCIENCE ADVANCES 2023; 9:eade2812. [PMID: 37058565 PMCID: PMC10104472 DOI: 10.1126/sciadv.ade2812] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.
Collapse
Affiliation(s)
- Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Sportelli
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Rahul Bharadwaj
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Christopher Borcuk
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Eugenia Radulescu
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Loredana Bellantuono
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Srinidhi Rao Sripathy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Brady J. Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Fabiana Rossi
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
8
|
Liu Q, Li J, Dong M, Liu M, Chai Y. Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:399-409. [PMID: 35061589 DOI: 10.1109/tcbb.2022.3144418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.
Collapse
|
9
|
Sriraja LO, Werhli A, Petsalaki E. Phosphoproteomics data-driven signalling network inference: Does it work? Comput Struct Biotechnol J 2022; 21:432-443. [PMID: 36618990 PMCID: PMC9798138 DOI: 10.1016/j.csbj.2022.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.
Collapse
Affiliation(s)
- Lourdes O. Sriraja
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Adriano Werhli
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Centro de Ciências Computacionais - Universidade Federal do Rio Grande - FURG, Avenida Itália, km 8, s/n, Campus Carreiros, 96203-900 Rio Grande, Rio Grande do Sul, Brazil2
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| |
Collapse
|
10
|
Hu J, Zhou S, Guo W. Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis. Hum Genomics 2022; 16:38. [PMID: 36076300 PMCID: PMC9461120 DOI: 10.1186/s40246-022-00412-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 09/02/2022] [Indexed: 11/24/2022] Open
Abstract
Background Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves’ disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches. Results The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein–protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride. Conclusions For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-022-00412-0.
Collapse
Affiliation(s)
- Jinxing Hu
- Department of Endocrinology, HwaMei Hospital, University of Chinese Academy of Sciences, 41 Northwest Street Zhejiang Province, Ningbo, 315010, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315010, China
| | - Shan Zhou
- Department of Endocrinology, HwaMei Hospital, University of Chinese Academy of Sciences, 41 Northwest Street Zhejiang Province, Ningbo, 315010, China. .,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315010, China.
| | - Weiying Guo
- Department of Endocrinology, HwaMei Hospital, University of Chinese Academy of Sciences, 41 Northwest Street Zhejiang Province, Ningbo, 315010, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315010, China
| |
Collapse
|
11
|
Approaches in Gene Coexpression Analysis in Eukaryotes. BIOLOGY 2022; 11:biology11071019. [PMID: 36101400 PMCID: PMC9312353 DOI: 10.3390/biology11071019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it. Abstract Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.
Collapse
|
12
|
Identifying large scale interaction atlases using probabilistic graphs and external knowledge. J Clin Transl Sci 2022; 6:e27. [PMID: 35321220 PMCID: PMC8922291 DOI: 10.1017/cts.2022.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/29/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.
Collapse
|
13
|
Chen J, Hao X, Wang B, Ma L. Transcriptomics and coexpression network profiling of the effects of levamisole hydrochloride on Bursaphelenchus xylophilus. PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 2022; 181:105019. [PMID: 35082042 DOI: 10.1016/j.pestbp.2021.105019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/14/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
Bursaphelenchus xylophilus is one of the most dangerous forest pathogens in the world, causing devastating pine forest deaths with considerable economic losses. In this study, we investigated the B. xylophilus RNA sequence responses of two different concentrations of levamisole hydrochloride (LH). We observed that body-wall muscle twitching, paralysis and, ultimately, death. 2.5 mg/ml and 3.5 mg/ml LH have toxicological effects on B. xylophilus, with mortality increasing significantly with concentration (p < 0.05). RNA sequencing, differential gene expression analysis, and cluster analysis were performed, and 336, 384, 6 genes with significant variance in expression were identified. Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses of the 12 intersecting genes revealed that these genes are mostly involved in metabolism of xenobiotics and have essential roles in drug sensitivity. Through the trend analysis of DEGs, it was divided into 8 modules, and the significant modules were selected to construct the co-expression network as the central genes of the drug metabolism-cytochrome P450 pathway (ko00982) and metabolism of xenobiotics by cytochrome P450 (ko00980). Eight highly related genes were identified, including cuticle collagen, cystathionine beta-synthase, endochitinase, pyruvate dehydrogenase E1 component subunit beta, aldehyde dehydrogenase, lipase, and zinc metalloproteinase. The expression levels of these genes were upregulated significantly at low concentrations and were significantly related to the resistance of B. xylophilus to LH. This study shows that B. xylophilus gene family expansions occurred in xenobiotic detoxification pathways through gene expression and potential horizontal correlated gene transfer with LH and helps to elucidate LH lethality and the evolutionary mechanisms underlying the adaptations of B. xylophilus to the environment. These results contributing to our understanding of B. xylophilus under LH and provide a data platform to providing a basis for its control.
Collapse
Affiliation(s)
- Jie Chen
- School of Forestry, Northeast Forestry University, Harbin 150040, China.
| | - Xin Hao
- School of Forestry, Northeast Forestry University, Harbin 150040, China.
| | - Buyong Wang
- School of Agriculture and Bioengineering, Heze University, Heze 274015, China.
| | - Ling Ma
- School of Forestry, Northeast Forestry University, Harbin 150040, China.
| |
Collapse
|
14
|
Zhang J, Chi Y, Zhang J. Analysis of CYP450 gene expression and function in white-rot fungus, Lenzites gibbosa, treated with Congo red. Biotech Histochem 2022; 97:519-535. [DOI: 10.1080/10520295.2022.2028307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Affiliation(s)
- Jian Zhang
- School of Forestry, Northeast Forestry University, Harbin, China
| | - YuJie Chi
- School of Forestry, Northeast Forestry University, Harbin, China
| | - Jun Zhang
- School of Forestry, Northeast Forestry University, Harbin, China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| |
Collapse
|
15
|
Tian Y, Huang B, Li J, Tian X, Zeng X. Identification of the Association Between Toll-Like Receptors and T-Cell Activation in Takayasu’s Arteritis. Front Immunol 2022; 12:792901. [PMID: 35126357 PMCID: PMC8812403 DOI: 10.3389/fimmu.2021.792901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/22/2021] [Indexed: 12/26/2022] Open
Abstract
To explore the relationships between Toll-like receptors (TLRs) and the activation and differentiation of T-cells in Takayasu’s arteritis (TAK), using real-time fluorescence quantitative polymerase chain reaction, mRNA abundance of 29 target genes in peripheral blood mononuclear cells (PBMCs) were detected from 27 TAK patients and 10 healthy controls. Compared with the healthy control group, the untreated TAK group and the treated TAK group had an increased mRNA level of TLR2 and TLR4. A sample-to-sample matrix revealed that 80% of healthy controls could be separated from the TAK patients. Correlation analysis showed that the inactive-treated TAK group exhibited a unique pattern of inverse correlations between the TLRs gene clusters (including TLR1/2/4/6/8, BCL6, TIGIT, NR4A1, etc) and the gene cluster associated with T-cell activation and differentiation (including TCR, CD28, T-bet, GATA3, FOXP3, CCL5, etc). The dynamic gene co-expression network indicated the TAK groups had more active communication between TLRs and T-cell activation than healthy controls. BCL6, CCL5, FOXP3, GATA3, CD28, T-bet, TIGIT, IκBα, and NR4A1 were likely to have a close functional relation with TLRs at the inactive stage. The co-expression of TLR4 and TLR6 could serve as a biomarker of disease activity in treated TAK (the area under curve/sensitivity/specificity, 0.919/100%/90.9%). The largest gene co-expression cluster of the inactive-treated TAK group was associated with TLR signaling pathways, while the largest gene co-expression cluster of the active-treated TAK group was associated with the activation and differentiation of T-cells. The miRNA sequencing of the plasma exosomes combining miRDB, DIANA-TarBase, and miRTarBase databases suggested that the miR-548 family miR-584, miR-3613, and miR-335 might play an important role in the cross-talk between TLRs and T-cells at the inactive stage. This study found a novel relation between TLRs and T-cell in the pathogenesis of autoimmune diseases, proposed a new concept of TLR-co-expression signature which might distinguish different disease activity of TAK, and highlighted the miRNA of exosomes in TLR signaling pathway in TAK.
Collapse
Affiliation(s)
- Yixiao Tian
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (PUMCH), Beijing, China
- National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Beijing, China
- Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
| | - Biqing Huang
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (PUMCH), Beijing, China
- National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Beijing, China
- Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
| | - Jing Li
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (PUMCH), Beijing, China
- National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Beijing, China
- Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
- *Correspondence: Jing Li, ; Xiaofeng Zeng,
| | - Xinping Tian
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (PUMCH), Beijing, China
- National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Beijing, China
- Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
| | - Xiaofeng Zeng
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (PUMCH), Beijing, China
- National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Beijing, China
- Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
- *Correspondence: Jing Li, ; Xiaofeng Zeng,
| |
Collapse
|
16
|
Ma C, Wu M, Ma S. Analysis of cancer omics data: a selective review of statistical techniques. Brief Bioinform 2022; 23:6510158. [PMID: 35039832 DOI: 10.1093/bib/bbab585] [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: 09/20/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.
Collapse
Affiliation(s)
- Chenjin Ma
- College of Statistics and Data Science, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| |
Collapse
|
17
|
Li D, Pan Z, Hu G, Anderson G, He S. Active Module Identification From Multilayer Weighted Gene Co-Expression Networks: A Continuous Optimization Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2239-2248. [PMID: 32011261 DOI: 10.1109/tcbb.2020.2970400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Searching for active modules, i.e., regions showing striking changes in molecular activity in biological networks is important to reveal regulatory and signaling mechanisms of biological systems. Most existing active modules identification methods are based on protein-protein interaction networks or metabolic networks, which require comprehensive and accurate prior knowledge. On the other hand, weighted gene co-expression networks (WGCNs) are purely constructed from gene expression profiles. However, existing WGCN analysis methods are designed for identifying functional modules but not capable of identifying active modules. There is an urgent need to develop an active module identification algorithm for WGCNs to discover regulatory and signaling mechanism associating with a given cellular response. To address this urgent need, we propose a novel algorithm called active modules on the multi-layer weighted (co-expression gene) network, based on a continuous optimization approach (AMOUNTAIN). The algorithm is capable of identifying active modules not only from single-layer WGCNs but also from multilayer WGCNs such as cross-species and dynamic WGCNs. We first validate AMOUNTAIN on a synthetic benchmark dataset. We then apply AMOUNTAIN to WGCNs constructed from Th17 differentiation gene expression datasets of human and mouse, which include a single layer, a cross-species two-layer and a multilayer dynamic WGCNs. The identified active modules from WGCNs are enriched by known protein-protein interactions, and more importantly, they reveal some interesting and important regulatory and signaling mechanisms of Th17 cell differentiation.
Collapse
|
18
|
Mohammadi E, Tahmoorespur M, Benfeitas R, Altay O, Javadmanesh A, Lam S, Mardinoglu A, Sekhavati MH. Improvement of the performance of anticancer peptides using a drug repositioning pipeline. Biotechnol J 2021; 17:e2100417. [PMID: 34657375 DOI: 10.1002/biot.202100417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/07/2021] [Accepted: 10/14/2021] [Indexed: 01/10/2023]
Abstract
The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional chemotherapy treatments has been shown to decrease drug resistance and/or severe side effects. However, the efficacy of the positively-charged ACP is inhibited by elevated levels of negatively-charged cell-surface components which trap the peptides and prevent their contact with the cell membrane. Consequently, this decreases ACP-mediated membrane pore formation and cell lysis. Negatively-charged heparan sulphate (HS) and chondroitin sulphate (CS) have been shown to inhibit the cytotoxic effect of ACPs. In this study, we propose a strategy to promote the broad utilization of ACPs. In this context, we developed a drug repositioning pipeline to analyse transcriptomics data generated for four different cancer cell lines (A549, HEPG2, HT29, and MCF7) treated with hundreds of drugs in the LINCS L1000 project. Based on previous studies identifying genes modulating levels of the glycosaminoglycans (GAGs) HS and CS at the cell surface, our analysis aimed at identifying drugs inhibiting genes correlated with high HS and CS levels. As a result, we identified six chemicals as likely repositionable drugs with the potential to enhance the performance of ACPs. The codes in R and Python programming languages are publicly available in https://github.com/ElyasMo/ACPs_HS_HSPGs_CS. As a conclusion, these six drugs are highlighted as excellent targets for synergistic studies with ACPs aimed at lowering the costs associated with ACP-treatment.
Collapse
Affiliation(s)
- Elyas Mohammadi
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran.,Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,3P-Medicine Laboratory, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Ozlem Altay
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Ali Javadmanesh
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Adil Mardinoglu
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | | |
Collapse
|
19
|
Arbet J, Zhuang Y, Litkowski E, Saba L, Kechris K. Comparing Statistical Tests for Differential Network Analysis of Gene Modules. Front Genet 2021; 12:630215. [PMID: 34093641 PMCID: PMC8170128 DOI: 10.3389/fgene.2021.630215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.
Collapse
Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Yaxu Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Elizabeth Litkowski
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laura Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora CO, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| |
Collapse
|
20
|
Assessment of TSPAN Expression Profile and Their Role in the VSCC Prognosis. Int J Mol Sci 2021; 22:ijms22095015. [PMID: 34065085 PMCID: PMC8125994 DOI: 10.3390/ijms22095015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 01/16/2023] Open
Abstract
The role and prognostic value of tetraspanins (TSPANs) in vulvar squamous cell carcinoma (VSCC) remain poorly understood. We sought to primarily determine, at both the molecular and tissue level, the expression profile of the TSPANs CD9, CD63, CD81, and CD82 in archived VSCC samples (n = 117) and further investigate their clinical relevance as prognostic markers. Our studies led us to identify CD63 as the most highly expressed TSPAN, at the gene and protein levels. Multicomparison studies also revealed that the expression of CD9 was associated with tumor size, whereas CD63 upregulation was associated with histological diagnosis and vascular invasion. Moreover, low expression of CD81 and CD82 was associated with worse prognosis. To determine the role of TSPANs in VSCC at the cellular level, we assessed the mRNA levels of CD63 and CD82 in established metastatic (SW962) and non-metastatic (SW954) VSCC human cell lines. CD82 was found to be downregulated in SW962 cells, thus supporting its metastasis suppressor role. However, CD63 was significantly upregulated in both cell lines. Silencing of CD63 by siRNA led to a significant decrease in proliferation of both SW954 and SW962. Furthermore, in SW962 particularly, CD63-siRNA also remarkably inhibited cell migration. Altogether, our data suggest that the differential expression of TSPANs represents an important feature for prognosis of VSCC patients and indicates that CD63 and CD82 are likely potential therapeutic targets in VSCC.
Collapse
|
21
|
Miller HE, Bishop AJR. Correlation AnalyzeR: functional predictions from gene co-expression correlations. BMC Bioinformatics 2021; 22:206. [PMID: 33879054 PMCID: PMC8056587 DOI: 10.1186/s12859-021-04130-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 04/13/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. RESULTS We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. CONCLUSIONS Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .
Collapse
Affiliation(s)
- Henry E Miller
- Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. .,Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.
| | - Alexander J R Bishop
- Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.,Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.,Mays Cancer Center, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA
| |
Collapse
|
22
|
Wu W, Li J, Wang Q, Lv K, Du K, Zhang W, Li Q, Kang X, Wei H. Growth-regulating factor 5 (GRF5)-mediated gene regulatory network promotes leaf growth and expansion in poplar. THE NEW PHYTOLOGIST 2021; 230:612-628. [PMID: 33423287 PMCID: PMC8048564 DOI: 10.1111/nph.17179] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 12/28/2020] [Indexed: 05/07/2023]
Abstract
Although polyploid plants have larger leaves than their diploid counterparts, the molecular mechanisms underlying this difference (or trait) remain elusive. Differentially expressed genes (DEGs) between triploid and full-sib diploid poplar trees were identified from two transcriptomic data sets followed by a gene association study among DEGs to identify key leaf growth regulators. Yeast one-hybrid system, electrophoretic mobility shift assay, and dual-luciferase assay were employed to substantiate that PpnGRF5-1 directly regulated PpnCKX1. The interactions between PpnGRF5-1 and growth-regulating factor (GRF)-interacting factors (GIFs) were experimentally validated and a multilayered hierarchical regulatory network (ML-hGRN)-mediated by PpnGRF5-1 was constructed with top-down graphic Gaussian model (GGM) algorithm by combining RNA-sequencing data from its overexpression lines and DAP-sequencing data. PpnGRF5-1 is a negative regulator of PpnCKX1. Overexpression of PpnGRF5-1 in diploid transgenic lines resulted in larger leaves resembling those of triploids, and significantly increased zeatin and isopentenyladenine in the apical buds and third leaves. PpnGRF5-1 also interacted with GIFs to increase its regulatory diversity and capacity. An ML-hGRN-mediated by PpnGRF5-1 was obtained and could largely elucidate larger leaves. PpnGRF5-1 and the ML-hGRN-mediated by PpnGRF5-1 were underlying the leaf growth and development.
Collapse
Affiliation(s)
- Wenqi Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijing100083China
| | - Jiang Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijing100083China
| | - Qiao Wang
- State Key Laboratory of Tree Genetics and BreedingChinese Academy of ForestryBeijing100091China
| | - Kaiwen Lv
- State Key Laboratory of Tree Genetics and BreedingNortheast Forestry UniversityHarbinHeilongjiang150040China
| | - Kang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijing100083China
| | - Wenli Zhang
- State Key Laboratory for Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingJiangsu210095China
| | - Quanzi Li
- State Key Laboratory of Tree Genetics and BreedingChinese Academy of ForestryBeijing100091China
| | - Xiangyang Kang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijing100083China
| | - Hairong Wei
- College of Forest Resources and Environmental ScienceMichigan Technological UniversityHoughtonMI49931USA
| |
Collapse
|
23
|
Källberg D, Vidman L, Rydén P. Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes. Front Genet 2021; 12:632620. [PMID: 33719342 PMCID: PMC7943624 DOI: 10.3389/fgene.2021.632620] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (-0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.
Collapse
Affiliation(s)
- David Källberg
- Department of Statistics, USBE, Umeå University, Umeå, Sweden
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
| | - Linda Vidman
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Patrik Rydén
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
| |
Collapse
|
24
|
Wang YXR, Li L, Li JJ, Huang H. Network Modeling in Biology: Statistical Methods for Gene and Brain Networks. Stat Sci 2021; 36:89-108. [PMID: 34305304 DOI: 10.1214/20-sts792] [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: 12/14/2022]
Abstract
The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.
Collapse
Affiliation(s)
- Y X Rachel Wang
- School of Mathematics and Statistics, University of Sydney, Australia
| | - Lexin Li
- Department of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley
| | | | - Haiyan Huang
- Department of Statistics, University of California, Berkeley
| |
Collapse
|
25
|
Iliopoulos A, Beis G, Apostolou P, Papasotiriou I. Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017093504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In this brief survey, various aspects of cancer complexity and how this complexity can
be confronted using modern complex networks’ theory and gene expression datasets, are described.
In particular, the causes and the basic features of cancer complexity, as well as the challenges
it brought are underlined, while the importance of gene expression data in cancer research
and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction
to the corresponding theoretical and mathematical framework of graph theory and complex
networks is provided. The basics of network reconstruction along with the limitations of gene
network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades
in complex networks, are described. Finally, an indicative and suggestive example of a cancer
gene co-expression network inference and analysis is given.
Collapse
Affiliation(s)
- A.C. Iliopoulos
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - G. Beis
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - P. Apostolou
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - I. Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug, Switzerland
| |
Collapse
|
26
|
Delgado-Chaves FM, Gómez-Vela F, Divina F, García-Torres M, Rodriguez-Baena DS. Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks. Genes (Basel) 2020; 11:E831. [PMID: 32708319 PMCID: PMC7397019 DOI: 10.3390/genes11070831] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 06/26/2020] [Accepted: 07/13/2020] [Indexed: 12/21/2022] Open
Abstract
Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.
Collapse
|
27
|
Randhawa V, Pathania S. Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. Brief Funct Genomics 2020; 19:364-376. [PMID: 32678894 DOI: 10.1093/bfgp/elaa015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/31/2020] [Accepted: 06/15/2020] [Indexed: 01/17/2023] Open
Abstract
Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.
Collapse
Affiliation(s)
- Vinay Randhawa
- Department of Biochemistry, Panjab University, Chandigarh, 160014, India
| | - Shivalika Pathania
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
| |
Collapse
|
28
|
Small-world networks of prognostic genes associated with lung adenocarcinoma development. Genomics 2020; 112:4078-4088. [PMID: 32659327 DOI: 10.1016/j.ygeno.2020.07.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/26/2020] [Accepted: 07/07/2020] [Indexed: 11/23/2022]
Abstract
The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.
Collapse
|
29
|
Chowdhury HA, Bhattacharyya DK, Kalita JK. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1154-1173. [PMID: 30668502 DOI: 10.1109/tcbb.2019.2893170] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.
Collapse
|
30
|
Wu SY, Lee CF, Lai HT, Yu CT, Lee JE, Zuo H, Tsai SY, Tsai MJ, Ge K, Wan Y, Chiang CM. Opposing Functions of BRD4 Isoforms in Breast Cancer. Mol Cell 2020; 78:1114-1132.e10. [PMID: 32446320 DOI: 10.1016/j.molcel.2020.04.034] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/12/2020] [Accepted: 04/28/2020] [Indexed: 12/21/2022]
Abstract
Bromodomain-containing protein 4 (BRD4) is a cancer therapeutic target in ongoing clinical trials disrupting primarily BRD4-regulated transcription programs. The role of BRD4 in cancer has been attributed mainly to the abundant long isoform (BRD4-L). Here we show, by isoform-specific knockdown and endogenous protein detection, along with transgene expression, the less abundant BRD4 short isoform (BRD4-S) is oncogenic while BRD4-L is tumor-suppressive in breast cancer cell proliferation and migration, as well as mammary tumor formation and metastasis. Through integrated RNA-seq, genome-wide ChIP-seq, and CUT&RUN association profiling, we identify the Engrailed-1 (EN1) homeobox transcription factor as a key BRD4-S coregulator, particularly in triple-negative breast cancer. BRD4-S and EN1 comodulate the extracellular matrix (ECM)-associated matrisome network, including type II cystatin gene cluster, mucin 5, and cathepsin loci, via enhancer regulation of cancer-associated genes and pathways. Our work highlights the importance of targeted therapies for the oncogenic, but not tumor-suppressive, activity of BRD4.
Collapse
Affiliation(s)
- Shwu-Yuan Wu
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Chien-Fei Lee
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hsien-Tsung Lai
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Cheng-Tai Yu
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ji-Eun Lee
- Adipocyte Biology and Gene Regulation Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hao Zuo
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Sophia Y Tsai
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ming-Jer Tsai
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kai Ge
- Adipocyte Biology and Gene Regulation Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yihong Wan
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Cheng-Ming Chiang
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| |
Collapse
|
31
|
Wei H. Construction of a hierarchical gene regulatory network centered around a transcription factor. Brief Bioinform 2020; 20:1021-1031. [PMID: 29186304 DOI: 10.1093/bib/bbx152] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 10/11/2017] [Indexed: 12/24/2022] Open
Abstract
We have modified a multitude of transcription factors (TFs) in numerous plant species and some animal species, and obtained transgenic lines that exhibit phenotypic alterations. Whenever we observe phenotypic changes in a TF's transgenic lines, we are always eager to identify its target genes, collaborative regulators and even upstream high hierarchical regulators. This issue can be addressed by establishing a multilayered hierarchical gene regulatory network (ML-hGRN) centered around a given TF. In this article, a practical approach for constructing an ML-hGRN centered on a TF using a combined approach of top-down and bottom-up network construction methods is described. Strategies for constructing ML-hGRNs are vitally important, as these networks provide key information to advance our understanding of how biological processes are regulated.
Collapse
Affiliation(s)
- Hairong Wei
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang, China.,School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA
| |
Collapse
|
32
|
Singh U, Hur M, Dorman K, Wurtele ES. MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets. Nucleic Acids Res 2020; 48:e23. [PMID: 31956905 PMCID: PMC7039010 DOI: 10.1093/nar/gkz1209] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 12/05/2019] [Accepted: 12/17/2019] [Indexed: 12/17/2022] Open
Abstract
The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/.
Collapse
Affiliation(s)
- Urminder Singh
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Center for Metabolic Biology, Iowa State University, Ames, IA 50011, USA
- Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Manhoi Hur
- Center for Metabolic Biology, Iowa State University, Ames, IA 50011, USA
| | - Karin Dorman
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Eve Syrkin Wurtele
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Center for Metabolic Biology, Iowa State University, Ames, IA 50011, USA
- Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
| |
Collapse
|
33
|
Lambert I, Paysant-Le Roux C, Colella S, Martin-Magniette ML. DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models. PLANT METHODS 2020; 16:68. [PMID: 32426025 PMCID: PMC7216733 DOI: 10.1186/s13007-020-00611-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 05/03/2020] [Indexed: 05/08/2023]
Abstract
BACKGROUND RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. RESULTS DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. CONCLUSIONS DiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results.
Collapse
Affiliation(s)
- Ilana Lambert
- LSTM, Laboratoire des Symbioses Tropicales et Méditerranéennes, IRD, CIRAD, INRAE, SupAgro, Univ Montpellier, Montpellier, France
| | - Christine Paysant-Le Roux
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Univ Evry, Bat. 630, 91405 Orsay, France
- Institute of Plant Sciences Paris Saclay (IPS2), Université de Paris, CNRS, INRAE, Bat. 630, 91405 Orsay, France
| | - Stefano Colella
- LSTM, Laboratoire des Symbioses Tropicales et Méditerranéennes, IRD, CIRAD, INRAE, SupAgro, Univ Montpellier, Montpellier, France
| | - Marie-Laure Martin-Magniette
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Univ Evry, Bat. 630, 91405 Orsay, France
- Institute of Plant Sciences Paris Saclay (IPS2), Université de Paris, CNRS, INRAE, Bat. 630, 91405 Orsay, France
- UMR MIA-Paris, AgroParisTech, INRAE, Université Paris-Saclay, 75005 Paris, France
| |
Collapse
|
34
|
Delgado-Chaves FM, Gómez-Vela F, García-Torres M, Divina F, Vázquez Noguera JL. Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach. Genes (Basel) 2019; 10:E962. [PMID: 31766738 PMCID: PMC6947459 DOI: 10.3390/genes10120962] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 10/28/2019] [Accepted: 10/31/2019] [Indexed: 12/22/2022] Open
Abstract
Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.
Collapse
Affiliation(s)
- Fernando M. Delgado-Chaves
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Francisco Gómez-Vela
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Miguel García-Torres
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Federico Divina
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | | |
Collapse
|
35
|
de Almeida BC, dos Anjos LG, Uno M, da Cunha IW, Soares FA, Baiocchi G, Baracat EC, Carvalho KC. Let-7 miRNA's Expression Profile and Its Potential Prognostic Role in Uterine Leiomyosarcoma. Cells 2019; 8:cells8111452. [PMID: 31744257 PMCID: PMC6912804 DOI: 10.3390/cells8111452] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/06/2019] [Accepted: 11/14/2019] [Indexed: 02/06/2023] Open
Abstract
The lethal-7 (let-7) family is an important microRNA (miRNA) group that usually exerts functions as a tumor suppressor. We aimed to evaluate the expression profile of let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, let-7g, and let-7i and to assess their value as prognostic markers in uterine leiomyosarcoma (LMS) patients. The miRNAs expression profile was assessed in 34 LMS and 13 normal myometrium (MM) paraffin-embedded samples. All let-7 family members showed downregulation in LMS. Our findings showed that patients with let-7e downregulation had worse overall survival (OS) and is an independent prognostic factor (hazard ratio [HR] = 2.24). In addition, almost half the patients had distant metastasis. LMS patients with downregulated let-7b and let-7d had worse disease-free survival (DFS); they are not independent prognostic factors (HR = 2.65). Patients’ ages were associated with let-7d, let-7e and let-7f (p = 0.0160) downregulation. In conclusion, all the let-7 family members were downregulated in LMS patients, and the greater the loss of expression of these molecules, the greater their relationship with worse prognosis of patients. Let-7e expression might influence the OS, while let-7b and le-7d might influence the DFS. The lowest expression levels of let-7d, let-7e, and let-7f were associated with the oldest patients. Our findings indicate strong evidence of let-7’s role as a potential prognostic biomarker in LMS.
Collapse
Affiliation(s)
- Bruna Cristine de Almeida
- Laboratório de Ginecologia Estrutural e Molecular (LIM 58), Disciplina de Ginecologia, Departamento de Obstetricia e Ginecologia, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, HCFMUSP, SP, BR Av. Dr Arnaldo 455, sala 4121, Cerqueira Cesar, São Paulo 05403-010, Brazil; (B.C.d.A.); (L.G.d.A.); (E.C.B.)
| | - Laura Gonzalez dos Anjos
- Laboratório de Ginecologia Estrutural e Molecular (LIM 58), Disciplina de Ginecologia, Departamento de Obstetricia e Ginecologia, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, HCFMUSP, SP, BR Av. Dr Arnaldo 455, sala 4121, Cerqueira Cesar, São Paulo 05403-010, Brazil; (B.C.d.A.); (L.G.d.A.); (E.C.B.)
| | - Miyuki Uno
- Centro de Investigação Translacional em Oncologia (LIM 24), Instituto do Câncer do Estado de São Paulo (CTO/ICESP) Av Dr Arnaldo 251 sala 23 8 andar, São Paulo 01246000, Brazil;
| | - Isabela Werneck da Cunha
- Department of Pathology, Rede D’OR-São Luiz, Rua das Perobas, 344-Jabaquara, São Paulo 04321-120, Brazil; (I.W.d.C.); (F.A.S.)
- Hospital A C Camargo Cancer Center, SP, BR R. Tamandaré, 753 Liberdade, São Paulo 05403-010, Brazil
- National Institute for Science and Technology in Oncogenomics and Therapeutic Innovation, SP, BR R. Tamandaré, 753 Liberdade, São Paulo 05403-010, Brazil
| | - Fernando Augusto Soares
- Department of Pathology, Rede D’OR-São Luiz, Rua das Perobas, 344-Jabaquara, São Paulo 04321-120, Brazil; (I.W.d.C.); (F.A.S.)
- Hospital A C Camargo Cancer Center, SP, BR R. Tamandaré, 753 Liberdade, São Paulo 05403-010, Brazil
- National Institute for Science and Technology in Oncogenomics and Therapeutic Innovation, SP, BR R. Tamandaré, 753 Liberdade, São Paulo 05403-010, Brazil
| | - Glauco Baiocchi
- Department of Gynecology Oncology, A.C.Camargo Cancer Center, Rua Prof Antonio Prudente 211, São Paulo 01509-001, Brazil;
| | - Edmund Chada Baracat
- Laboratório de Ginecologia Estrutural e Molecular (LIM 58), Disciplina de Ginecologia, Departamento de Obstetricia e Ginecologia, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, HCFMUSP, SP, BR Av. Dr Arnaldo 455, sala 4121, Cerqueira Cesar, São Paulo 05403-010, Brazil; (B.C.d.A.); (L.G.d.A.); (E.C.B.)
| | - Katia Candido Carvalho
- Laboratório de Ginecologia Estrutural e Molecular (LIM 58), Disciplina de Ginecologia, Departamento de Obstetricia e Ginecologia, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, HCFMUSP, SP, BR Av. Dr Arnaldo 455, sala 4121, Cerqueira Cesar, São Paulo 05403-010, Brazil; (B.C.d.A.); (L.G.d.A.); (E.C.B.)
- Correspondence: ; Tel.: +55-011-3061-7486
| |
Collapse
|
36
|
Wang Y, Zhang S, Yang L, Yang S, Tian Y, Ma Q. Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network. Front Genet 2019; 10:1009. [PMID: 31695723 PMCID: PMC6818468 DOI: 10.3389/fgene.2019.01009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 09/23/2019] [Indexed: 11/13/2022] Open
Abstract
Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN), to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene–gene interactions collected from the COXPRESdb, KEGG, and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from https://bmbl.bmi.osumc.edu/FCNN.
Collapse
Affiliation(s)
- Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.,School of Artificial Intelligence, Jilin University, Changchun, China
| | - Shuangquan Zhang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Lili Yang
- Department of Obstetrics, The First Hospital of Jilin University, Changchun, China
| | - Sen Yang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yuan Tian
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
37
|
Liang Y, Xie W, Luan Y. Developmental expression and evolution of hexamerin and haemocyanin from Folsomia candida (Collembola). INSECT MOLECULAR BIOLOGY 2019; 28:716-727. [PMID: 30953580 PMCID: PMC6850205 DOI: 10.1111/imb.12585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Haemocyanins constitute a group of copper-containing respiratory proteins, and hexamerins were derived from hexapod haemocyanin but lost the ability to transport oxygen and serve as storage proteins. Although hexamerins have been reported in most insect species, none of them has been identified in Collembola, one of the most primitive hexapod lineages, thereby preventing us from exploring relevant evolutionary scenarios regarding the origin and evolution of hexamerins in hexapods. Here we report on collembolan hexamerins for the first time, and investigated the temporal expression profiles of hexamerin and haemocyanin in the collembolan Folsomia candida. Haemocyanin was expressed over the entire life cycle, with higher expression at the embryonic stage than at other stages, whereas hexamerin expression was restricted to embryos, unlike insect hexamerins, which are generally expressed from larval to adult stages. A phylogenetic analysis and molecular clock estimation suggested that all investigated hexapod hexamerins have a single and ancient origin (~423 Ma), coincident with the rise of atmospheric oxygen levels in the Silurian-Devonian period, indicating a physiological link between molecular evolution and Palaeozoic oxygen changes.
Collapse
Affiliation(s)
- Y. Liang
- Key Laboratory of Insect Developmental and Evolutionary BiologyShanghai Institute of Plant Physiology and Ecology, Chinese Academy of SciencesShanghaiChina
- School of Biological and Chemical Sciences, Queen Mary University of LondonLondonUK
| | - W. Xie
- Key Laboratory of Insect Developmental and Evolutionary BiologyShanghai Institute of Plant Physiology and Ecology, Chinese Academy of SciencesShanghaiChina
| | - Y.‐X. Luan
- Key Laboratory of Insect Developmental and Evolutionary BiologyShanghai Institute of Plant Physiology and Ecology, Chinese Academy of SciencesShanghaiChina
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied TechnologyInstitute of Insect Science and Technology, School of Life Sciences, South China Normal UniversityGuangzhouChina
| |
Collapse
|
38
|
Sumithra B, Saxena U, Das AB. A comprehensive study on genome-wide coexpression network of KHDRBS1/Sam68 reveals its cancer and patient-specific association. Sci Rep 2019; 9:11083. [PMID: 31366900 PMCID: PMC6668649 DOI: 10.1038/s41598-019-47558-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 07/19/2019] [Indexed: 12/11/2022] Open
Abstract
Human KHDRBS1/Sam68 is an oncogenic splicing factor involved in signal transduction and pre-mRNA splicing. We explored the molecular mechanism of KHDRBS1 to be a prognostic marker in four different cancers. Within specific cancer, including kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), acute myeloid leukemia (LAML), and ovarian cancer (OV), KHDRBS1 expression is heterogeneous and patient specific. In KIRP and LUAD, higher expression of KHDRBS1 affects the patient survival, but not in LAML and OV. Genome-wide coexpression analysis reveals genes and transcripts which are coexpressed with KHDRBS1 in KIRP and LUAD, form the functional modules which are majorly involved in cancer-specific events. However, in case of LAML and OV, such modules are absent. Irrespective of the higher expression of KHDRBS1, the significant divergence of its biological roles and prognostic value is due to its cancer-specific interaction partners and correlation networks. We conclude that rewiring of KHDRBS1 interactions in cancer is directly associated with patient prognosis.
Collapse
Affiliation(s)
- B Sumithra
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
| | - Urmila Saxena
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
| | - Asim Bikas Das
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India.
| |
Collapse
|
39
|
Masnadi-Shirazi M, Maurya MR, Pao G, Ke E, Verma IM, Subramaniam S. Time varying causal network reconstruction of a mouse cell cycle. BMC Bioinformatics 2019; 20:294. [PMID: 31142274 PMCID: PMC6542064 DOI: 10.1186/s12859-019-2895-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 05/13/2019] [Indexed: 12/21/2022] Open
Abstract
Background Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms. Results In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle. Conclusions The temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle. Electronic supplementary material The online version of this article (10.1186/s12859-019-2895-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Maryam Masnadi-Shirazi
- Department of Electrical and Computer Engineering and Bioengineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Mano R Maurya
- Department of Bioengineering and San Diego Supercomputer center, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Gerald Pao
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Eugene Ke
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Inder M Verma
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Shankar Subramaniam
- Department of Bioengineering, Departments of Computer Science and Engineering, Cellular and Molecular Medicine, and the Graduate Program in Bioinformatics, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| |
Collapse
|
40
|
Somekh J, Shen-Orr SS, Kohane IS. Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset. BMC Bioinformatics 2019; 20:268. [PMID: 31138121 PMCID: PMC6537327 DOI: 10.1186/s12859-019-2855-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 04/26/2019] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher. RESULTS We describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project. CONCLUSIONS Our framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package.
Collapse
Affiliation(s)
- Judith Somekh
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
- Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Shai S Shen-Orr
- Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| |
Collapse
|
41
|
Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression. Brain Struct Funct 2019; 224:1331-1344. [DOI: 10.1007/s00429-019-01843-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 01/31/2019] [Indexed: 01/11/2023]
|
42
|
Genome-Wide Identification and Comparative Analysis for OPT Family Genes in Panax ginseng and Eleven Flowering Plants. Molecules 2018; 24:molecules24010015. [PMID: 30577553 PMCID: PMC6337337 DOI: 10.3390/molecules24010015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 12/06/2018] [Accepted: 12/17/2018] [Indexed: 02/06/2023] Open
Abstract
Herb genomics and comparative genomics provide a global platform to explore the genetics and biology of herbs at the genome level. Panax ginseng C.A. Meyer is an important medicinal plant for a variety of bioactive chemical compounds of which the biosynthesis may involve transport of a wide range of substrates mediated by oligopeptide transporters (OPT). However, information about the OPT family in the plant kingdom is still limited. Only 17 and 18 OPT genes have been characterized for Oryza sativa and Arabidopsisthaliana, respectively. Additionally, few comprehensive studies incorporating the phylogeny, gene structure, paralogs evolution, expression profiling, and co-expression network between transcription factors and OPT genes have been reported for ginseng and other species. In the present study, we performed those analyses comprehensively with both online tools and standalone tools. As a result, we identified a total of 268 non-redundant OPT genes from 12 flowering plants of which 37 were from ginseng. These OPT genes were clustered into two distinct clades in which clade-specific motif compositions were considerably conservative. The distribution of OPT paralogs was indicative of segmental duplication and subsequent structural variation. Expression patterns based on two sources of RNA-Sequence datasets suggested that some OPT genes were expressed in both an organ-specific and tissue-specific manner and might be involved in the functional development of plants. Further co-expression analysis of OPT genes and transcription factors indicated 141 positive and 11 negative links, which shows potent regulators for OPT genes. Overall, the data obtained from our study contribute to a better understanding of the complexity of the OPT gene family in ginseng and other flowering plants. This genetic resource will help improve the interpretation on mechanisms of metabolism transportation and signal transduction during plant development for Panax ginseng.
Collapse
|
43
|
Affiliation(s)
- Hélio Amante Miot
- Universidade Estadual Paulista - UNESP, Faculdade de Medicina de Botucatu, Departamento de Dermatologia e Radioterapia, Botucatu, SP, Brasil
| |
Collapse
|
44
|
Veras PST, Ramos PIP, de Menezes JPB. In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages. Front Cell Infect Microbiol 2018; 8:326. [PMID: 30283744 PMCID: PMC6157484 DOI: 10.3389/fcimb.2018.00326] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/27/2018] [Indexed: 12/12/2022] Open
Abstract
Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.
Collapse
Affiliation(s)
- Patricia Sampaio Tavares Veras
- Laboratory of Host-Parasite Interaction and Epidemiology, Gonçalo Moniz Institute, Fiocruz-Bahia, Salvador, Brazil.,National Institute of Tropical Disease, Brasilia, Brazil
| | - Pablo Ivan Pereira Ramos
- Center for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Fiocruz-Bahia, Salvador, Brazil
| | | |
Collapse
|
45
|
van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19:575-592. [PMID: 28077403 PMCID: PMC6054162 DOI: 10.1093/bib/bbw139] [Citation(s) in RCA: 430] [Impact Index Per Article: 71.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 12/01/2016] [Indexed: 01/06/2023] Open
Abstract
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.
Collapse
Affiliation(s)
- Sipko van Dam
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | - Urmo Võsa
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | | | - Lude Franke
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | | |
Collapse
|
46
|
Villaverde AF, Becker K, Banga JR. PREMER: A Tool to Infer Biological Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1193-1202. [PMID: 28981423 DOI: 10.1109/tcbb.2017.2758786] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features-such as distinguishing between direct and indirect interactions or determining the direction of a causal link-requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end, we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux, and OSX (https://sites.google.com/site/premertoolbox/).
Collapse
|
47
|
Gunasekara C, Zhang K, Deng W, Brown L, Wei H. TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction. Nucleic Acids Res 2018; 46:e67. [PMID: 29579312 PMCID: PMC6009660 DOI: 10.1093/nar/gky210] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 03/07/2018] [Accepted: 03/12/2018] [Indexed: 12/20/2022] Open
Abstract
Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.
Collapse
Affiliation(s)
- Chathura Gunasekara
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
- Program of Computational Science and Engineering, Michigan Technological University, MI 49931, USA
| | - Kui Zhang
- Department of Mathematical Sciences Michigan Technological University, Houghton, MI 49931, USA
| | - Wenping Deng
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| | - Laura Brown
- Department of Computer Science, Michigan Technological University, MI 49931, USA
| | - Hairong Wei
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
- Program of Computational Science and Engineering, Michigan Technological University, MI 49931, USA
- Department of Computer Science, Michigan Technological University, MI 49931, USA
- Life Science and Technology Institute, Michigan Technological University, Houghton, MI 49931, USA
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
| |
Collapse
|
48
|
Isaza C, Rosas JF, Lorenzo E, Marrero A, Ortiz C, Ortiz MR, Perez L, Cabrera‐Ríos M. Biological signaling pathways and potential mathematical network representations: biological discovery through optimization. Cancer Med 2018; 7:1875-1895. [PMID: 29635835 PMCID: PMC5943441 DOI: 10.1002/cam4.1301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 11/17/2017] [Accepted: 11/21/2017] [Indexed: 01/04/2023] Open
Abstract
Establishing the role that different genes play in the development of cancer is a daunting task. A step toward this end is the detection of genes that are important in the illness from high-throughput biological experiments. Furthermore, it is safe to say that it is highly unlikely that these show expression changes independently, even with a list of potentially important genes. A biological signaling pathway is a more plausible underlying mechanism as favored in the literature. This work attempts to build a mathematical network problem through the analysis of microarray experiments. A preselection of genes is carried out with a multiple criteria optimization framework previously published by our research group . Afterward, application of the Traveling Salesperson Problem and Minimum Spanning Tree network optimization models are proposed to identify potential signaling pathways via the most correlated path among the genes of interest. Biological evidencing is provided to assess the effectiveness of the proposed methods. The capability of our analysis strategy is also demonstrated through the undertaking of meta-analysis studies. Three important aspects are novel in this work: (1) our joint analyses of different groups of lung cancer states reveal new correlations, biologically evidenced, and previously undocumented; (2) computation of the correlation coefficients from expression differences leads to an effective use of network optimization methods; and (3) the methods yield mathematically optimal correlation structures: no other configuration is better correlated using the available information.
Collapse
Affiliation(s)
- Clara Isaza
- Public Health ProgramPonce Health Sciences UniversityPonce00732‐7004Puerto Rico
| | - Juan F. Rosas
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Enery Lorenzo
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Arlette Marrero
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Cristina Ortiz
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Michael R. Ortiz
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Lynn Perez
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Mauricio Cabrera‐Ríos
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| |
Collapse
|
49
|
Zhang Y, Cao S, Zhao J, Alsaihati B, Ma Q, Zhang C. MRHCA: a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network. QUANTITATIVE BIOLOGY 2018. [DOI: 10.1007/s40484-018-0131-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
50
|
Data Wisdom in Computational Genomics Research. STATISTICS IN BIOSCIENCES 2017. [DOI: 10.1007/s12561-016-9173-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|