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Li W, Ballard J, Zhao Y, Long Q. Knowledge-guided learning methods for integrative analysis of multi-omics data. Comput Struct Biotechnol J 2024; 23:1945-1950. [PMID: 38736693 PMCID: PMC11087912 DOI: 10.1016/j.csbj.2024.04.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.
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Affiliation(s)
- Wenrui Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
| | - Jenna Ballard
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, 19104, PA, USA
| | - Yize Zhao
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, 06510, CT, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
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Truong TTT, Liu ZSJ, Panizzutti B, Kim JH, Dean OM, Berk M, Walder K. Network-based drug repurposing for schizophrenia. Neuropsychopharmacology 2024; 49:983-992. [PMID: 38321095 PMCID: PMC11039639 DOI: 10.1038/s41386-024-01805-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024]
Abstract
Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.
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Affiliation(s)
- Trang T T Truong
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Zoe S J Liu
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Bruna Panizzutti
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Jee Hyun Kim
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Olivia M Dean
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Michael Berk
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, The Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, University of Melbourne, Parkville, 3010, Australia
| | - Ken Walder
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia.
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Tang YC, Li R, Tang J, Zheng WJ, Jiang X. SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations. RESEARCH SQUARE 2024:rs.3.rs-4308618. [PMID: 38746131 PMCID: PMC11092851 DOI: 10.21203/rs.3.rs-4308618/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and context-dependent networks. This limitation constrains the applicability of current methods. Results We introduced SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. Conclusions SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Finally, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients.
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Affiliation(s)
- Yi-Ching Tang
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Rongbin Li
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - W Jim Zheng
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Xiaoqian Jiang
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
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Truong TTT, Liu ZSJ, Panizzutti B, Dean OM, Berk M, Kim JH, Walder K. Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder. J Affect Disord 2024; 350:230-239. [PMID: 38190860 DOI: 10.1016/j.jad.2024.01.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/03/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024]
Abstract
BACKGROUND Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. METHODS This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. RESULTS Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. LIMITATIONS Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. CONCLUSIONS Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.
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Affiliation(s)
- Trang T T Truong
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Zoe S J Liu
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Bruna Panizzutti
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Olivia M Dean
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Michael Berk
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, The Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, University of Melbourne, Parkville 3010, Australia
| | - Jee Hyun Kim
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Ken Walder
- Deakin University, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia.
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Pelissier A, Laragione T, Harris C, Martínez MR, Gulko PS. Gene Network Analyses Identify Co-regulated Transcription Factors and BACH1 as a Key Driver in Rheumatoid Arthritis Fibroblast-like Synoviocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573506. [PMID: 38234777 PMCID: PMC10793426 DOI: 10.1101/2023.12.28.573506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying Rheumatoid Arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks (GRNs) to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This is the first time that BACH1 is shown to have a central role in the regulation of FLS phenotypes, and gene expression signatures, as well as in ferroptosis and fatty acid metabolism. These new discoveries have the potential to become new targets for treatments aimed at selectively targeting the RA FLS.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Carolyn Harris
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
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Huang X, Song C, Zhang G, Li Y, Zhao Y, Zhang Q, Zhang Y, Fan S, Zhao J, Xie L, Li C. scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse. Nucleic Acids Res 2024; 52:D293-D303. [PMID: 37889053 PMCID: PMC10767939 DOI: 10.1093/nar/gkad885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/19/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.
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Affiliation(s)
- Xuemei Huang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chao Song
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Guorui Zhang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Ye Li
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shifan Fan
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jun Zhao
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Liyuan Xie
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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Fisher JL, Clark AD, Jones EF, Lasseigne BN. Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes. BMC Pharmacol Toxicol 2024; 25:5. [PMID: 38167211 PMCID: PMC10763002 DOI: 10.1186/s40360-023-00727-1] [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: 07/10/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. METHODS We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. RESULTS We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. CONCLUSIONS Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.
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Affiliation(s)
- Jennifer L Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Amanda D Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Emma F Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Brittany N Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.
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Xin J, Wang M, Qu L, Chen Q, Wang W, Wang Z. BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:188-199. [PMID: 38127613 DOI: 10.1109/tcbb.2023.3345317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.
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Wang L, Sesachalam PV, Chua R, Ghosh S. Interactome Analysis of Visceral Adipose Tissue Elucidates Gene Regulatory Networks and Novel Gene Candidates in Obesity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.21.572734. [PMID: 38187694 PMCID: PMC10769441 DOI: 10.1101/2023.12.21.572734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Objective Visceral adiposity is associated with increased proinflammatory activity, insulin resistance, diabetes risk and mortality rate. Numerous individual genes have been associated with obesity, but studies investigating gene-regulatory networks in human visceral obesity are lacking. Methods We analyzed gene-regulatory networks in human visceral adipose tissue (VAT) from 48 obese and 11 non-obese Chinese subjects using gene co-expression and network construction with RNA-sequencing data. We also conducted RNA interference-based tests on selected genes for adipocyte differentiation effects. Results A scale-free gene co-expression network was constructed from 360 differentially expressed genes between obese and non-obese VAT (absolute log fold-change >1, FDR<0.05) with edge probability >0.8. Gene regulatory network analysis identified candidate transcription factors associated with differentially expressed genes. Fifteen subnetworks (communities) displayed altered connectivity patterns between obese and non-obese networks. Genes in pro-inflammatory pathways showed increased network connectivities in obese VAT whereas the oxidative phosphorylation pathway displayed reduced connections (enrichment FDR<0.05). Functional screening via RNA interference identified SOX30 and OSBPL3 as potential network-derived gene candidates influencing adipocyte differentiation. Conclusions This interactome-based approach highlights the network architecture, identifies novel candidate genes, and leads to new hypotheses regarding network-assisted gene regulation in obese vs. non-obese VAT.What is already known about this subject?: Visceral adipose tissue (VAT) is associated with increased levels of proinflammatory activity, insulin resistance, diabetes risk and mortality rate.Gene expression studies have identified candidate genes associated with proinflammatory function in VAT.What are the new findings in your manuscript?: Using integrative network-science, we identified co-expression and gene regulatory networks that are differentially regulated in VAT samples from subjects with and without obesityWe used functional testing (adipocyte differentiation) to validate a subset of novel candidate genes with minimal prior reported associations to obesityHow might your results change the direction of research or the focus of clinical practice: Network biology-based investigation provides a new avenue to our understanding of gene function in visceral adiposityFunctional validation screen allows for the identification of novel gene candidates that may be targeted for the treatment of adipose tissue dysfunction in obesity.
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10
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Somers J, Fenner M, Kong G, Thirumalaisamy D, Yashar WM, Thapa K, Kinali M, Nikolova O, Babur Ö, Demir E. A framework for considering prior information in network-based approaches to omics data analysis. Proteomics 2023; 23:e2200402. [PMID: 37986684 DOI: 10.1002/pmic.202200402] [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: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/22/2023]
Abstract
For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
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Affiliation(s)
- Julia Somers
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Madeleine Fenner
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Garth Kong
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Dharani Thirumalaisamy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - William M Yashar
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Kisan Thapa
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Meric Kinali
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Olga Nikolova
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Emek Demir
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
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11
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Kumar S, Vindal V. Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues. Funct Integr Genomics 2023; 23:324. [PMID: 37878223 DOI: 10.1007/s10142-023-01251-5] [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: 08/14/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/26/2023]
Abstract
Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.
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Affiliation(s)
- Swapnil Kumar
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - Vaibhav Vindal
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.
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12
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Saha E, Guebila MB, Fanfani V, Fischer J, Shutta KH, Mandros P, DeMeo DL, Quackenbush J, Lopes-Ramos CM. Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559001. [PMID: 37790409 PMCID: PMC10543009 DOI: 10.1101/2023.09.22.559001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.
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Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jonas Fischer
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
| | - Panagiotis Mandros
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
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13
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Belova T, Biondi N, Hsieh PH, Lutsik P, Chudasama P, Kuijjer M. Heterogeneity in the gene regulatory landscape of leiomyosarcoma. NAR Cancer 2023; 5:zcad037. [PMID: 37492373 PMCID: PMC10365024 DOI: 10.1093/narcan/zcad037] [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: 01/09/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.
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Affiliation(s)
- Tatiana Belova
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Nicola Biondi
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Ping-Han Hsieh
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium
| | - Priya Chudasama
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Marieke L Kuijjer
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
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14
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Ben Guebila M, Wang T, Lopes-Ramos CM, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson JN, Altenbuchinger M, Shutta KH, Sonawane AR, Lim J, Calderer G, van IJzendoorn DGP, Morgan D, Marin A, Chen CY, Song Q, Saha E, DeMeo DL, Padi M, Platig J, Kuijjer ML, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biol 2023; 24:45. [PMID: 36894939 PMCID: PMC9999668 DOI: 10.1186/s13059-023-02877-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/15/2023] [Indexed: 03/11/2023] Open
Abstract
Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tian Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Biology Department, Boston College, Chestnut Hill, MA, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Des Weighill
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebekka Burkholz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Daniel Schlauch
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Genospace, LLC, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Michael Altenbuchinger
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
- Present Address: Monoceros Biosystems, LLC, San Diego, CA, USA
| | - Genis Calderer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - David G P van IJzendoorn
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Present Address: Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Daniel Morgan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: School of Biomedical Sciences, Hong Kong University, Pokfulam, Hong Kong
| | | | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Present Address: Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Qi Song
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marieke L Kuijjer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Center for Computational Oncology, Leiden University, Leiden, The Netherlands
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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15
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Kardynska M, Kogut D, Pacholczyk M, Smieja J. Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods. Comput Struct Biotechnol J 2023; 21:1523-1532. [PMID: 36851915 PMCID: PMC9958294 DOI: 10.1016/j.csbj.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.
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Affiliation(s)
- Malgorzata Kardynska
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland
| | - Daria Kogut
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Marcin Pacholczyk
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Jaroslaw Smieja
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
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16
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Ochoa S, Hernández-Lemus E. Functional impact of multi-omic interactions in breast cancer subtypes. Front Genet 2023; 13:1078609. [PMID: 36685900 PMCID: PMC9850112 DOI: 10.3389/fgene.2022.1078609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023] Open
Abstract
Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.
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Affiliation(s)
- Soledad Ochoa
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico,Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico,Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico,*Correspondence: Enrique Hernández-Lemus,
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17
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He Z, Gao K, Dong L, Liu L, Qu X, Zou Z, Wu Y, Bu D, Guo JC, Zhao Y. Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network. Comput Struct Biotechnol J 2023; 21:1557-1572. [PMID: 36879883 PMCID: PMC9984461 DOI: 10.1016/j.csbj.2023.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/19/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023] Open
Abstract
A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.
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Affiliation(s)
- Zihao He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Kai Gao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lei Dong
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Liu Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xinchi Qu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhengkai Zou
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Jin-Cheng Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yi Zhao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.,Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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18
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Chaudhuri S, Srivastava A. Network approach to understand biological systems: From single to multilayer networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Yao S, Campbell PT, Ugai T, Gierach G, Abubakar M, Adalsteinsson V, Almeida J, Brennan P, Chanock S, Golub T, Hanash S, Harris C, Hathaway CA, Kelsey K, Landi MT, Mahmood F, Newton C, Quackenbush J, Rodig S, Schultz N, Tearney G, Tworoger SS, Wang M, Zhang X, Garcia-Closas M, Rebbeck TR, Ambrosone CB, Ogino S. Proceedings of the fifth international Molecular Pathological Epidemiology (MPE) meeting. Cancer Causes Control 2022; 33:1107-1120. [PMID: 35759080 PMCID: PMC9244289 DOI: 10.1007/s10552-022-01594-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/20/2022] [Indexed: 01/19/2023]
Abstract
Cancer heterogeneities hold the key to a deeper understanding of cancer etiology and progression and the discovery of more precise cancer therapy. Modern pathological and molecular technologies offer a powerful set of tools to profile tumor heterogeneities at multiple levels in large patient populations, from DNA to RNA, protein and epigenetics, and from tumor tissues to tumor microenvironment and liquid biopsy. When coupled with well-validated epidemiologic methodology and well-characterized epidemiologic resources, the rich tumor pathological and molecular tumor information provide new research opportunities at an unprecedented breadth and depth. This is the research space where Molecular Pathological Epidemiology (MPE) emerged over a decade ago and has been thriving since then. As a truly multidisciplinary field, MPE embraces collaborations from diverse fields including epidemiology, pathology, immunology, genetics, biostatistics, bioinformatics, and data science. Since first convened in 2013, the International MPE Meeting series has grown into a dynamic and dedicated platform for experts from these disciplines to communicate novel findings, discuss new research opportunities and challenges, build professional networks, and educate the next-generation scientists. Herein, we share the proceedings of the Fifth International MPE meeting, held virtually online, on May 24 and 25, 2021. The meeting consisted of 21 presentations organized into the three main themes, which were recent integrative MPE studies, novel cancer profiling technologies, and new statistical and data science approaches. Looking forward to the near future, the meeting attendees anticipated continuous expansion and fruition of MPE research in many research fronts, particularly immune-epidemiology, mutational signatures, liquid biopsy, and health disparities.
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Affiliation(s)
- Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, 14263, USA.
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gretchen Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mustapha Abubakar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | - Jonas Almeida
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Paul Brennan
- International Agency for Research On Cancer (IARC/WHO), Genomic Epidemiology Branch, Lyon, France
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Todd Golub
- Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Samir Hanash
- Department of Clinical Cancer Prevention, MD Anderson Cancer Institute, Houston, TX, USA
| | - Curtis Harris
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Cassandra A Hathaway
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Karl Kelsey
- Department of Epidemiology, Brown School of Public Health, Brown University, Providence, RI, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Christina Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Scott Rodig
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolaus Schultz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guillermo Tearney
- Department of Pathology and Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Timothy R Rebbeck
- Zhu Family Center for Global Cancer Prevention, Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, 14263, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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20
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Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:pharmaceutics14071464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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21
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Ziv M, Gruber G, Sharon M, Vinogradov E, Yeger-Lotem E. The TissueNet v.3 database: Protein-protein interactions in adult and embryonic human tissue contexts. J Mol Biol 2022; 434:167532. [DOI: 10.1016/j.jmb.2022.167532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/03/2022] [Accepted: 03/03/2022] [Indexed: 12/28/2022]
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22
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Weighill D, Ben Guebila M, Glass K, Quackenbush J, Platig J. Predicting genotype-specific gene regulatory networks. Genome Res 2022; 32:524-533. [PMID: 35193937 PMCID: PMC8896459 DOI: 10.1101/gr.275107.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/11/2022] [Indexed: 11/25/2022]
Abstract
Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.
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Affiliation(s)
- Deborah Weighill
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | | | - Kimberly Glass
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John Quackenbush
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
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23
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Guebila MB, Morgan DC, Glass K, Kuijjer ML, DeMeo DL, Quackenbush J. gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit. NAR Genom Bioinform 2022; 4:lqac002. [PMID: 35156023 PMCID: PMC8826808 DOI: 10.1093/nargab/lqac002] [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: 07/13/2021] [Revised: 12/28/2021] [Accepted: 02/02/2022] [Indexed: 11/14/2022] Open
Abstract
Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Daniel C Morgan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Marieke L Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
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24
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Lin CX, Li HD, Deng C, Guan Y, Wang J. TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data. Nucleic Acids Res 2021; 50:D710-D718. [PMID: 34850130 PMCID: PMC8728275 DOI: 10.1093/nar/gkab1133] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/10/2021] [Accepted: 11/18/2021] [Indexed: 01/02/2023] Open
Abstract
Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.
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Affiliation(s)
- Cui-Xiang Lin
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Chao Deng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
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25
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Weighill D, Guebila MB, Lopes-Ramos C, Glass K, Quackenbush J, Platig J, Burkholz R. Gene regulatory network inference as relaxed graph matching. PROCEEDINGS OF THE ... AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE. AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE 2021; 35:10263-10272. [PMID: 34707916 PMCID: PMC8546743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.
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Affiliation(s)
- Deborah Weighill
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Camila Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
- Channing Division of Network Medicine, Brigham and Women's Hospital
- Harvard Medical School, Boston, MA 02115
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
- Channing Division of Network Medicine, Brigham and Women's Hospital
- Harvard Medical School, Boston, MA 02115
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital
- Harvard Medical School, Boston, MA 02115
| | - Rebekka Burkholz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
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