1
|
Zhang Q, He Y, Lu YP, Wei QH, Zhang HY, Quan Y. GETdb: A comprehensive database for genetic and evolutionary features of drug targets. Comput Struct Biotechnol J 2024; 23:1429-1438. [PMID: 38616961 PMCID: PMC11015738 DOI: 10.1016/j.csbj.2024.04.006] [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: 11/07/2023] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.
Collapse
Affiliation(s)
- Qi Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yang He
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Ya-Ping Lu
- Sinopharm Genomics Technology Co., Ltd., Wuhan 430030, PR China
- Sinopharm Medical Laboratory (Wuhan) Co., Ltd., Wuhan 430030, PR China
| | - Qi-Hao Wei
- Sinopharm (Wuhan) Precision Medical Technology Co., Ltd., Wuhan 430030, PR China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| |
Collapse
|
2
|
Yuan H, Mancuso CA, Johnson K, Braasch I, Krishnan A. Computational strategies for cross-species knowledge transfer and translational biomedicine. ARXIV 2024:arXiv:2408.08503v1. [PMID: 39184546 PMCID: PMC11343225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.
Collapse
Affiliation(s)
- Hao Yuan
- Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Christopher A. Mancuso
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus
| | - Kayla Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
| | - Ingo Braasch
- Department of Integrative Biology; Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
| |
Collapse
|
3
|
Kiaris H. Nontraditional models as research tools: the road not taken. Trends Mol Med 2024:S1471-4914(24)00191-6. [PMID: 39069395 DOI: 10.1016/j.molmed.2024.07.005] [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: 05/31/2024] [Revised: 07/07/2024] [Accepted: 07/10/2024] [Indexed: 07/30/2024]
Abstract
Historical reasons resulted in the almost exclusive use of a few species, most prominently Mus musculus, as the mainstream models in biomedical research. This selection was not based on Mus's distinctive relevance to human disease but rather to the pre-existing availability of resources and tools for the species that were used as models, which has enabled their adoption for research in health sciences. Unless the utilization and range of nontraditional research models expand considerably, progress in biomedical research will remain restricted within the trajectory that has been set by the existing models and their ability to provide clinically relevant information.
Collapse
Affiliation(s)
- Hippokratis Kiaris
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy and Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA.
| |
Collapse
|
4
|
Cacheiro P, Lawson S, Van den Veyver IB, Marengo G, Zocche D, Murray SA, Duyzend M, Robinson PN, Smedley D. Lethal phenotypes in Mendelian disorders. Genet Med 2024; 26:101141. [PMID: 38629401 PMCID: PMC11232373 DOI: 10.1016/j.gim.2024.101141] [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: 02/05/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
PURPOSE Existing resources that characterize the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders; however, there is a lack of comprehensive reporting on lethal phenotypes. METHODS We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterized the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery. RESULTS We developed the Lethal Phenotypes Portal to showcase this curated catalog of human essential genes. Differences in the mode of inheritance, physiological systems affected, and disease class were found for genes in different lethality categories, as well as discrepancies between the lethal phenotypes observed in mouse and human. CONCLUSION We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.
Collapse
Affiliation(s)
- Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Samantha Lawson
- ITS Research, Queen Mary University of London, London, United Kingdom
| | - Ignatia B Van den Veyver
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX
| | - Gabriel Marengo
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park and St Mark's Hospitals, London, United Kingdom
| | | | - Michael Duyzend
- Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA; Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Peter N Robinson
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
| |
Collapse
|
5
|
Cacheiro P, Lawson S, Van den Veyver IB, Marengo G, Zocche D, Murray SA, Duyzend M, Robinson PN, Smedley D. Lethal phenotypes in Mendelian disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301168. [PMID: 38260283 PMCID: PMC10802756 DOI: 10.1101/2024.01.12.24301168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Essential genes are those whose function is required for cell proliferation and/or organism survival. A gene's intolerance to loss-of-function can be allocated within a spectrum, as opposed to being considered a binary feature, since this function might be essential at different stages of development, genetic backgrounds or other contexts. Existing resources that collect and characterise the essentiality status of genes are based on either proliferation assessment in human cell lines, embryonic and postnatal viability evaluation in different model organisms, and gene metrics such as intolerance to variation scores derived from human population sequencing studies. There are also several repositories available that document phenotypic annotations for rare disorders in humans such as the Online Mendelian Inheritance in Man (OMIM) and the Human Phenotype Ontology (HPO) knowledgebases. This raises the prospect of being able to use clinical data, including lethality as the most severe phenotypic manifestation, to further our characterisation of gene essentiality. Here we queried OMIM for terms related to lethality and classified all Mendelian genes into categories, according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. To showcase this curated catalogue of human essential genes, we developed the Lethal Phenotypes Portal (https://lethalphenotypes.research.its.qmul.ac.uk), where we also explore the relationships between these lethality categories, constraint metrics and viability in cell lines and mouse. Further analysis of the genes in these categories reveals differences in the mode of inheritance of the associated disorders, physiological systems affected and disease class. We highlight how the phenotypic similarity between genes in the same lethality category combined with gene family/group information can be used for novel disease gene discovery. Finally, we explore the overlaps and discrepancies between the lethal phenotypes observed in mouse and human and discuss potential explanations that include differences in transcriptional regulation, functional compensation and molecular disease mechanisms. We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.
Collapse
Affiliation(s)
- Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Ignatia B. Van den Veyver
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA
| | - Gabriel Marengo
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park & St Mark’s Hospitals, London, UK
| | | | | | - Peter N. Robinson
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, UK
| |
Collapse
|
6
|
Wright SN, Leger BS, Rosenthal SB, Liu SN, Jia T, Chitre AS, Polesskaya O, Holl K, Gao J, Cheng R, Garcia Martinez A, George A, Gileta AF, Han W, Netzley AH, King CP, Lamparelli A, Martin C, St Pierre CL, Wang T, Bimschleger H, Richards J, Ishiwari K, Chen H, Flagel SB, Meyer P, Robinson TE, Solberg Woods LC, Kreisberg JF, Ideker T, Palmer AA. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep 2023; 42:112873. [PMID: 37527041 PMCID: PMC10546330 DOI: 10.1016/j.celrep.2023.112873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.
Collapse
Affiliation(s)
- Sarah N Wright
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Program in Biomedical Sciences, University of California San Diego, La Jolla, CA 93093, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology & Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sophie N Liu
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Tongqiu Jia
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Katie Holl
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Riyan Cheng
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Angel Garcia Martinez
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Anthony George
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Alexander F Gileta
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Wenyan Han
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Alesa H Netzley
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher P King
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Connor Martin
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Tengfei Wang
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Hannah Bimschleger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Jerry Richards
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Keita Ishiwari
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY 14203, USA
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Shelly B Flagel
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | - Terry E Robinson
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jason F Kreisberg
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
7
|
Xiong W, Zhang X, Peng B, Zhu H, Huang L, He S. Pan-glioma analyses reveal species- and tumor-specific regulation of neuron-glioma synapse genes by lncRNAs. Front Genet 2023; 14:1218408. [PMID: 37693314 PMCID: PMC10484416 DOI: 10.3389/fgene.2023.1218408] [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: 05/07/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Gliomas are highly heterogeneous and aggressive. Malignant cells in gliomas can contact normal neurons through a synapse-like structure (called neuron-to-glioma synapse, NGS) to promote their proliferation, but it is unclear whether NGS gene expression and regulation show species- and tumor-specificity. This question is important in that many anti-cancer drugs are developed upon mouse models. To address this question, we conducted a pan-glioma analysis using nine scRNA-seq datasets from humans and mice. We also experimentally validated the key element of our methods and verified a key result using TCGA datasets of the same glioma types. Our analyses revealed that NGS gene expression and regulation by lncRNAs are highly species- and tumor-specific. Importantly, simian-specific lncRNAs are more involved in NGS gene regulation than lncRNAs conserved in mammals, and transgenic mouse gliomas have little in common with PDX mouse models and human gliomas in terms of NGS gene regulation. The analyses suggest that simian-specific lncRNAs are a new and rich class of potential targets for tumor-specific glioma treatment, and provide pertinent data for further experimentally and clinically exmining the targets.
Collapse
Affiliation(s)
- Wei Xiong
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xuecong Zhang
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bin Peng
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hao Zhu
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lijin Huang
- Neurosurgery Department, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Sha He
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| |
Collapse
|
8
|
Park M, Kim D, Kim I, Im SH, Kim S. Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans. EBioMedicine 2023; 94:104705. [PMID: 37453362 PMCID: PMC10366401 DOI: 10.1016/j.ebiom.2023.104705] [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: 02/19/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. METHODS Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. FINDINGS Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. INTERPRETATION The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. FUNDING S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).
Collapse
Affiliation(s)
- Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea
| | - Inhae Kim
- ImmunoBiome Inc., Pohang, South Korea
| | - Sin-Hyeog Im
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea; ImmunoBiome Inc., Pohang, South Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea.
| |
Collapse
|
9
|
Ha D, Kong J, Kim D, Lee K, Lee J, Park M, Ahn H, Oh Y, Kim S. Development of bioinformatics and multi-omics analyses in organoids. BMB Rep 2023; 56:43-48. [PMID: 36284440 PMCID: PMC9887100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Indexed: 01/28/2023] Open
Abstract
Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].
Collapse
Affiliation(s)
- Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Hyunsoo Ahn
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Youngchul Oh
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea,Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea,Corresponding author. Tel: +82-54-279-2348; Fax: +82-54-279-2199; E-mail:
| |
Collapse
|
10
|
Shi K, Wang B, Dou L, Wang S, Fu X, Yu H. Integrated bioinformatics analysis of the transcription factor-mediated gene regulatory networks in the formation of spermatogonial stem cells. Front Physiol 2022; 13:949486. [PMID: 36569748 PMCID: PMC9773208 DOI: 10.3389/fphys.2022.949486] [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: 05/21/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
Background: In vitro induction of spermatogonial stem cells (SSCs) from embryonic stem cells (ESCs) provides a promising tool for the treatment of male infertility. A variety of molecules are involved in this complex process, which needs to be further clarified. Undoubtedly, the increased knowledge of SSC formation will be beneficial to facilitate the currently complex induction process. Methods: Based on ATAC-seq, DNase-seq, RNA-seq, and microarray data from GEO datasets, chromatin property data (ATAC-seq, DNase-seq) and gene expression data (RNA-seq, microarray data) were combined to search for SSC-specific transcription factors (TFs) and hub SSC-specific genes by using the WGCNA method. Then, we applied RNA-seq and microarray data screening for key SSC-specific TFs and constructed key SSC-specific TF-mediated gene regulatory networks (GRNs) using ChIP-seq data. Results: First, after analysis of the ATAC-seq and DNase-seq data of mouse ESCs, primordial germ cells (PGCs), and SSCs, 33 SSC-specific TFs and 958 targeting genes were obtained. RNA-seq and WGCNA revealed that the key modules (turquoise and red) were the most significantly related to 958 SSC-specific genes, and a total of 10 hub SSC-specific genes were identified. Next, when compared with the cell-specific TFs in human ESCs, PGCs, and SSCs, we obtained five overlapping SSC-specific TF motifs, including the NF1 family TF motifs (NFIA, NFIB, NFIC, and NFIX), GRE, Fox:Ebox, PGR, and ARE. Among these, Nfib and Nfix exhibited abnormally high expression levels relative to mouse ESCs and PGCs. Moreover, Nfib and Nfix were upregulated in the testis sample with impaired spermatogenesis when compared with the normal group. Finally, the ChIP-seq data results showed that NFIB most likely targeted the hub SSC-specific genes of the turquoise module (Rpl36al, Rps27, Rps21, Nedd8, and Sec61b) and the red module (Vcam1 and Ccl2). Conclusion: Our findings preliminarily revealed cell-specific TFs and cell-specific TF-mediated GRNs in the process of SSC formation. The hub SSC-specific genes and the key SSC-specific TFs were identified and suggested complex network regulation, which may play key roles in optimizing the induction efficiency of the differentiation of ESCs into SSCs in vitro.
Collapse
|