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Li X, Zan X, Liu T, Dong X, Zhang H, Li Q, Bao Z, Lin J. Integrated edge information and pathway topology for drug-disease associations. iScience 2024; 27:110025. [PMID: 38974972 PMCID: PMC11226970 DOI: 10.1016/j.isci.2024.110025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/06/2024] [Accepted: 05/15/2024] [Indexed: 07/09/2024] Open
Abstract
Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.
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Affiliation(s)
- Xianbin Li
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong 520000, China
| | - Tao Liu
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Xiwei Dong
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Haqi Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Qizhang Li
- Innovative Drug R&D Center, School of Life Sciences, Huaibei Normal University, Huaibei, Anhui 235000, China
| | - Zhenshen Bao
- College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu, China
| | - Jie Lin
- Department of Pharmacy, the Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325200, Zhejiang Province, China
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Fisher JL, Wilk EJ, Oza VH, Gary SE, Howton TC, Flanary VL, Clark AD, Hjelmeland AB, Lasseigne BN. Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio 2024; 14:803-830. [PMID: 38531616 PMCID: PMC11073506 DOI: 10.1002/2211-5463.13796] [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: 02/11/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Vishal H. Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Sam E. Gary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Anita B. Hjelmeland
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
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Ezquerra Condeminas P, Mallol M, Font R, Tremps V, Gutiérrez JA, Rodas G, Perera Lluna A, Soria JM. Unraveling athletic performance: Transcriptomics and external load monitoring in handball competition. PLoS One 2024; 19:e0299556. [PMID: 38466679 PMCID: PMC10927131 DOI: 10.1371/journal.pone.0299556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE This study aims to comprehend the impact of handball practice on sub-elite athletes by investigating transcriptomic changes that occur during a match. The primary focus encompasses a dual objective: firstly, to identify and characterize these transcriptomic alterations, and secondly, to establish correlations between internal factors (gene expression), and external loads measured through Electronic Performance and Tracking Systems (EPTS variables). Ultimately, this comprehensive analysis seeks to evaluate both acute and chronic responses to exercise within the context of handball training. METHODS The study included sixteen elite male athletes from the FC Barcelona handball second team. Blood samples were extracted at three different time points: before the match at baseline levels (T1), immediately upon completion (T2), and 24 hours after completion (T3). Differential gene expression, Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted in two comparisons: Comparison 1 (T1 vs T2) and Comparison 2 (T1 vs T3). Further, the correlation between gene expression levels and training variables (external load) was conducted. RESULTS In T1 vs T2, 3717 of the 14632 genes detected were differentially expressed (adjusted p-value < 0.05), and enrichment of terms related to the immune system, mitochondria, and metabolic processes was found. Further, significant linear correlations were obtained between High-Speed running (HSR) and high-intensity variables such as acceleration ACC and deceleration DEC values with amino acids, and inflammatory and oxidative environment-related pathways, both in chronic and acute response. CONCLUSIONS This research highlights the effects of external workload on elite athletes during a handball match and throughout the season. The study identifies deregulation in the immune system, mitochondrial functions, and various metabolic pathways during the match. Additionally, it establishes correlations between the external load and pathways associated with amino acids, inflammation, oxidative environment, and regulation. These findings offer insights into the immediate and chronic responses of athletes to physical effort.
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Affiliation(s)
- Pol Ezquerra Condeminas
- Department of Innovation and Development Area, Beyond You (Exheus S.L.), Barcelona, Spain
- b2slab, Universitat Politècnica de Barcelona (UPC), Barcelona, Spain
| | - Milos Mallol
- Performance Department of Football Club Barcelona, Barcelona, Spain
- Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain
| | - Roger Font
- Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain
- Department of Health Sciences, Research group in Technology Applied to High Performance and Health (TAARS), Tecnocampus, Pompeu Fabra University, Mataró, Spain
- National Institut of Physical Education of Catalonia (INEFC), GRCE Research Group, Barcelona, Spain
| | - Victor Tremps
- Performance Department of Football Club Barcelona, Barcelona, Spain
- Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain
- Department of Sports Sciences, Ramon Llull University, FPCEE Blanquerna, Barcelona, Spain
| | - Jose Antonio Gutiérrez
- Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain
- Medical Department of Football Club Barcelona (FIFA Medical Centre of Excellence), Barcelona, Spain
| | - Gil Rodas
- Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain
- Medical Department of Football Club Barcelona (FIFA Medical Centre of Excellence), Barcelona, Spain
| | - Alexandre Perera Lluna
- Department of Innovation and Development Area, Beyond You (Exheus S.L.), Barcelona, Spain
- b2slab, Universitat Politècnica de Barcelona (UPC), Barcelona, Spain
- Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
- Institut de Recerca Sant Joan de Deu, Barcelona, Spain
| | - José Manuel Soria
- Department of Innovation and Development Area, Beyond You (Exheus S.L.), Barcelona, Spain
- Unit of Genomics of Complex Disease, Research Institute of Sant Pau Hospital (IIB Sant Pau), Barcelona, Spain
- Instituto de Salud Carlos III, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Madrid, Spain
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Odongo R, Demiroglu-Zergeroglu A, Çakır T. A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer. BioData Min 2024; 17:5. [PMID: 38378612 PMCID: PMC10880212 DOI: 10.1186/s13040-024-00357-1] [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: 10/16/2023] [Accepted: 02/12/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes. RESULTS By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer. CONCLUSION This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.
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Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey.
| | - Asuman Demiroglu-Zergeroglu
- Department of Molecular Biology & Genetics, Faculty of Science, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey
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Wilk EJ, Howton TC, Fisher JL, Oza VH, Brownlee RT, McPherson KC, Cleary HL, Yoder BK, George JF, Mrug M, Lasseigne BN. Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion. Mol Med 2023; 29:67. [PMID: 37217845 PMCID: PMC10201779 DOI: 10.1186/s10020-023-00664-z] [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: 12/06/2022] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed. METHODS As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates' target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis. RESULTS With this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo. CONCLUSION Collectively, these results indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD.
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Affiliation(s)
- Elizabeth J. Wilk
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Timothy C. Howton
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Jennifer L. Fisher
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Vishal H. Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Ryan T. Brownlee
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
- Department of Biomedical Sciences, Mercer University, Macon, GA USA
| | - Kasi C. McPherson
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Hannah L. Cleary
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
- University of Kentucky College of Medicine, Lexington, KY USA
| | - Bradley K. Yoder
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - James F. George
- The Department of Surgery, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
| | - Michal Mrug
- The Department of Medicine, HeersinkSchool of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
- Department of Veterans Affairs Medical Center, Birmingham, AL USA
| | - Brittany N. Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL USA
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Liang S, Liu D, Xiao Z, Greenbaum J, Shen H, Xiao H, Deng H. Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans. Pharmaceuticals (Basel) 2023; 16:ph16040607. [PMID: 37111364 PMCID: PMC10145476 DOI: 10.3390/ph16040607] [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: 03/06/2023] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.
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Affiliation(s)
- Shuang Liang
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Danyang Liu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410013, China
| | - Zhengwu Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Jonathan Greenbaum
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
| | - Hui Shen
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
| | - Hongmei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Hongwen Deng
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
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Raghavachari N, Wilmot B, Dutta C. Optimizing Translational Research for Exceptional Health and Life Span: A Systematic Narrative of Studies to Identify Translatable Therapeutic Target(s) for Exceptional Health Span in Humans. J Gerontol A Biol Sci Med Sci 2022; 77:2272-2280. [PMID: 35279027 PMCID: PMC9678194 DOI: 10.1093/gerona/glac065] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Exceptional longevity as manifested by the lower incidence and delayed onset of age-related disabilities/diseases that include cardiovascular disease, Alzheimer's disease, cancer is believed to be influenced by inherent protective molecular factors in exceptionally long-lived individuals. Unraveling these protective factors could lead to the discovery of therapeutic target(s) and interventions to promote healthy aging. METHODS In this context, the National Institute on Aging has established a collection of translational longevity research projects (ie, the Long-Life Family Study, the Longevity Consortium, Longevity Genomics, and the Integrative Longevity Omics) which are generating large omics data sets spanning the human genome to phenome and have embarked on cross-species multiomic data analyses integrating human and nonhuman species that display wide variation in their life spans. RESULTS It is expected that these studies will discover key signaling pathways that influence exceptional health span and identify therapeutic targets for translation to enhance health and life span. Other efforts related to translational longevity research include the "Comprehensive Evaluation of Aging-Related Clinical Outcomes and Geroproteins study," which focuses on potential effects in humans of polypeptides/proteins whose circulating levels change with age, and for which experimental evidence indicates reversal or acceleration of aging changes. The "Predictive Human Mechanistic Markers Network" is devoted to the development of predictive markers of aging, for target engagement when testing novel interventions for healthy aging. CONCLUSION We describe here the significance, the unique study design, categories of data sets, analytical strategies, and a data portal to facilitate open science and sharing of resources from these longevity studies to identify and validate potential therapeutic targets for healthy aging.
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Affiliation(s)
- Nalini Raghavachari
- Division of Geriatrics & Clinical Gerontology, National Institute on Aging, NIH, Bethesda, Maryland, USA
| | - Beth Wilmot
- Division of Geriatrics & Clinical Gerontology, National Institute on Aging, NIH, Bethesda, Maryland, USA
| | - Chhanda Dutta
- Division of Geriatrics & Clinical Gerontology, National Institute on Aging, NIH, Bethesda, Maryland, USA
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Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma. Cell Discov 2022; 8:68. [PMID: 35853872 PMCID: PMC9296597 DOI: 10.1038/s41421-022-00415-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/26/2022] [Indexed: 01/01/2023] Open
Abstract
The clear cell renal cell carcinoma (ccRCC) microenvironment consists of many different cell types and structural components that play critical roles in cancer progression and drug resistance, but the cellular architecture and underlying gene regulatory features of ccRCC have not been fully characterized. Here, we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to generate transcriptional and epigenomic landscapes of ccRCC. We identified tumor cell-specific regulatory programs mediated by four key transcription factors (TFs) (HOXC5, VENTX, ISL1, and OTP), and these TFs have prognostic significance in The Cancer Genome Atlas (TCGA) database. Targeting these TFs via short hairpin RNAs (shRNAs) or small molecule inhibitors decreased tumor cell proliferation. We next performed an integrative analysis of chromatin accessibility and gene expression for CD8+ T cells and macrophages to reveal the different regulatory elements in their subgroups. Furthermore, we delineated the intercellular communications mediated by ligand–receptor interactions within the tumor microenvironment. Taken together, our multiomics approach further clarifies the cellular heterogeneity of ccRCC and identifies potential therapeutic targets.
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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Lee H, Choi JY, Joung JG, Joh JW, Kim JM, Hyun SH. Metabolism-Associated Gene Signatures for FDG Avidity on PET/CT and Prognostic Validation in Hepatocellular Carcinoma. Front Oncol 2022; 12:845900. [PMID: 35174098 PMCID: PMC8841806 DOI: 10.3389/fonc.2022.845900] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionThe prognostic value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in hepatocellular carcinoma (HCC) was established in previous reports. However, there is no evidence suggesting the prognostic value of transcriptomes associated with tumor FDG uptake in HCC. It was aimed to elucidate metabolic genes and functions associated with FDG uptake, followed by assessment of those prognostic value.MethodsSixty HCC patients with Edmondson–Steiner grade II were included. FDG PET/CT scans were performed before any treatment. RNA sequencing data were obtained from tumor and normal liver tissue. Associations between each metabolism-associated gene and tumor FDG uptake were investigated by Pearson correlation analyses. A novel score between glucose and lipid metabolism-associated gene expression was calculated. In The Cancer Genome Atlas Liver Hepatocellular Carcinoma dataset, the prognostic power of selected metabolism-associated genes and a novel score was evaluated for external validation.ResultsNine genes related to glycolysis and the HIF-1 signaling pathway showed positive correlations with tumor FDG uptake; 21 genes related to fatty acid metabolism and the PPAR signaling pathway demonstrated negative correlations. Seven potential biomarker genes, PFKFB4, ALDOA, EGLN3, EHHADH, GAPDH, HMGCS2, and ENO2 were identified. A metabolic gene expression balance score according to the dominance between glucose and lipid metabolism demonstrated good prognostic value in HCC.ConclusionsThe transcriptomic evidence of this study strongly supports the prognostic power of FDG PET/CT and indicates the potential usefulness of FDG PET/CT imaging biomarkers to select appropriate patients for metabolism-targeted therapy in HCC.
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Affiliation(s)
- Hyunjong Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Je-Gun Joung
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Biomedical Science, College of Life Science, CHA University, Seongnam, South Korea
| | - Jae-Won Joh
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong Man Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- *Correspondence: Seung Hyup Hyun,
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Moolamalla STR, Balasubramanian R, Chauhan R, Priyakumar UD, Vinod PK. Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach. Microb Pathog 2021; 158:105114. [PMID: 34333072 PMCID: PMC8321700 DOI: 10.1016/j.micpath.2021.105114] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/17/2021] [Accepted: 07/23/2021] [Indexed: 02/08/2023]
Abstract
Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.
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Affiliation(s)
- S T R Moolamalla
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Rami Balasubramanian
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Ruchi Chauhan
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - P K Vinod
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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12
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Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform 2021; 22:6278144. [PMID: 34013329 PMCID: PMC8597919 DOI: 10.1093/bib/bbab161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
The basis of several recent methods for drug repurposing is the key principle that an
efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects.
This principle was defined and popularized by the influential ‘connectivity map’ study in
2006 regarding reversal relationships between disease- and drug-induced gene expression
profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years,
several studies have proposed variations in calculating connectivity scores toward
improving accuracy and robustness in light of massive growth in reference drug profiles.
However, these variations have been formulated inconsistently using various notations and
terminologies even though they are based on a common set of conceptual and statistical
ideas. Therefore, we present a systematic reconciliation of multiple disease-drug
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}{}$EMUDRA$\end{document}) by defining them using consistent
notation and terminology. In addition to providing clarity and deeper insights, this
coherent definition of connectivity scores and their relationships provides a unified
scheme that newer methods can adopt, enabling the computational drug-development community
to compare and investigate different approaches easily. To facilitate the continuous and
transparent integration of newer methods, this article will be available as a live
document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub
repository (https://github.com/jravilab/connectivity_scores) that any researcher can
build on and push changes to.
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Affiliation(s)
- Kewalin Samart
- Computational Mathematics, and Computational Math, Science & Engineering at Michigan State University, East Lansing, MI, USA
| | - Phoebe Tuyishime
- College of Agriculture and Natural Resources at Michigan State University, East Lansing, MI, USA
| | - Arjun Krishnan
- Departments of Computational Math, Science & Engineering, and Biochemistry & Molecular Biology at Michigan State University, East Lansing, MI, USA
| | - Janani Ravi
- Pathobiology and Diagnostic Investigation at Michigan State University, East Lansing, MI, USA
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13
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Gelemanović A, Vidović T, Stepanić V, Trajković K. Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach. Pharmaceuticals (Basel) 2021; 14:87. [PMID: 33504008 PMCID: PMC7912585 DOI: 10.3390/ph14020087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.
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Affiliation(s)
- Andrea Gelemanović
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
| | - Tinka Vidović
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
| | - Višnja Stepanić
- Ruđer Bošković Institute, Bijenička Cesta 54, 10000 Zagreb, Croatia;
| | - Katarina Trajković
- Mediterranean Institute for Life Sciences (MedILS), Šetalište Ivana Meštrovića 45, 21000 Split, Croatia;
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