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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: 2.7] [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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Flynn E, Chang A, Altman RB. Large-scale labeling and assessment of sex bias in publicly available expression data. BMC Bioinformatics 2021; 22:168. [PMID: 33784977 PMCID: PMC8011224 DOI: 10.1186/s12859-021-04070-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/08/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. RESULTS Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). CONCLUSIONS Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.
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Affiliation(s)
- Emily Flynn
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Annie Chang
- Program in Human Biology, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Medicine, Stanford University, Stanford, CA, USA.
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Zhang Y, Li Y, Li T, Shen X, Zhu T, Tao Y, Li X, Wang D, Ma Q, Hu Z, Liu J, Ruan J, Cai J, Wang HY, Lu X. Genetic Load and Potential Mutational Meltdown in Cancer Cell Populations. Mol Biol Evol 2019; 36:541-552. [PMID: 30649444 DOI: 10.1093/molbev/msy231] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Large genomes with elevated mutation rates are prone to accumulating deleterious mutations more rapidly than natural selection can purge (Muller's ratchet). As a consequence, it may lead to the extinction of small populations. Relative to most unicellular organisms, cancer cells, with large and nonrecombining genome and high mutation rate, could be particularly susceptible to such "mutational meltdown." However, the most common type of mutation in organismal evolution, namely, deleterious mutation, has received relatively little attention in the cancer biology literature. Here, by monitoring single-cell clones from HeLa cell lines, we characterize deleterious mutations that retard the rate of cell proliferation. The main mutation events are copy number variations (CNVs), which, estimated from fitness data, happen at a rate of 0.29 event per cell division on average. The mean fitness reduction, estimated reaching 18% per mutation, is very high. HeLa cell populations therefore have very substantial genetic load and, at this level, natural population would likely face mutational meltdown. We suspect that HeLa cell populations may avoid extinction only after the population size becomes large enough. Because CNVs are common in most cell lines and tumor tissues, the observations hint at cancer cells' vulnerability, which could be exploited by therapeutic strategies.
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Affiliation(s)
- Yuezheng Zhang
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Yawei Li
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Tao Li
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xu Shen
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Tianqi Zhu
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Yong Tao
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Xueying Li
- School of Life Sciences, Peking University, Beijing, China
| | - Di Wang
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Qin Ma
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Hu
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Jialin Liu
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Jue Ruan
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Jun Cai
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Hurng-Yi Wang
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan.,Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
| | - Xuemei Lu
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Beijing, China.,CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,University of Chinese Academy of Sciences, Beijing, China
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Endoplasmic reticulum stress-dependent ROS production mediates synovial myofibroblastic differentiation in the immobilization-induced rat knee joint contracture model. Exp Cell Res 2018; 369:325-334. [PMID: 29856991 DOI: 10.1016/j.yexcr.2018.05.036] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 05/28/2018] [Accepted: 05/29/2018] [Indexed: 01/25/2023]
Abstract
Joint contracture is a common complication for people with joint immobility that involves fibrosis structural alteration in the joint capsule. Considering that endoplasmic reticulum (ER) stress plays a prominent role in the promotion of tissue fibrosis, we investigated whether the unfolded protein response (UPR) contributes to the fibrotic development in immobilization-induced knee joint contractures. Using a non-traumatic rat knee joint contracture model, twelve female Sprague-Dawley rats received knee joint immobilization for a period of 8 weeks. We found that fibrosis protein markers (type I collagen, α-SMA) and UPR (GRP78, ATF6α, XBP1s) markers were parallelly upregulated in rat primary cultured synovial myofibroblasts. In the same cell types, pre-treatment with an ER stress inhibitor, 4-phenylbutyric acid (4-PBA), not only abrogated cytokine TGFβ1 stimulation but also reduced the protein level of UPR. Additionally, high reactive oxygen species (ROS) generation was detected in synovial myofibroblasts through flow cytometry, as expected. Notably, TGFβ1-induced UPR was significantly reduced through the inhibition of ROS with antioxidants. These data suggest that ER stress act as a pro-fibrotic stimulus through the overexpression of ROS in synovial fibroblasts. Interestingly, immunohistochemical results showed an increase in the UPR protein levels both in human acquired joint contractures capsule tissue and in animal knee joint contracture tissue. Together, our findings suggest that ER stress contributes to synovial myofibroblastic differentiation in joint capsule fibrosis and may also serve as a potential therapeutic target in joint contractures.
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