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Chao P, Zhang X, Zhang L, Yang A, Wang Y, Chen X. Integration of molecular docking and molecular dynamics simulations with subtractive proteomics approach to identify the novel drug targets and their inhibitors in Streptococcus gallolyticus. Sci Rep 2024; 14:14755. [PMID: 38926437 PMCID: PMC11208513 DOI: 10.1038/s41598-024-64769-z] [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: 03/02/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Streptococcus gallolyticus (Sg) is a non-motile, gram-positive bacterium that causes infective endocarditis (inflammation of the heart lining). Because Sg has gained resistance to existing antibiotics and there is currently no drug available, developing effective anti-Sg drugs is critical. This study combined core proteomics with a subtractive proteomics technique to identify potential therapeutic targets for Sg. Several bioinformatics approaches were used to eliminate non-essential and human-specific homologous sequences from the bacterial proteome. Then, virulence, druggability, subcellular localization, and functional analyses were carried out to specify the participation of significant bacterial proteins in various cellular processes. The pathogen's genome contained three druggable proteins, glucosamine-1phosphate N-acetyltransferase (GlmU), RNA polymerase sigma factor (RpoD), and pantetheine-phosphate adenylyltransferase (PPAT) which could serve as effective targets for developing novel drugs. 3D structures of target protein were modeled through Swiss Model. A natural product library containing 10,000 molecules from the LOTUS database was docked against therapeutic target proteins. Following an evaluation of the docking results using the glide gscore, the top 10 compounds docked against each protein receptor were chosen. LTS001632, LTS0243441, and LTS0236112 were the compounds that exhibited the highest binding affinities against GlmU, PPAT, and RpoD, respectively, among the compounds that were chosen. To augment the docking data, molecular dynamics simulations and MM-GBSA binding free energy were also utilized. More in-vitro research is necessary to transform these possible inhibitors into therapeutic drugs, though computer validations were employed in this study. This combination of computational techniques paves the way for targeted antibiotic development, which addresses the critical need for new therapeutic strategies against S. gallolyticus infections.
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Affiliation(s)
- Peng Chao
- Department of Cardiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Xueqin Zhang
- Department of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Lei Zhang
- Department of Cardiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Aiping Yang
- Department of Traditional Chinese Medicine, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Yong Wang
- Department of Cardiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Xiaoyang Chen
- Department of Cardiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
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2
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Chen H, Li T, Gao R, Cheng M, Zhang Q, Liu X, Chen M, Liao X, Qin L. RNA editing landscape of adipose tissue in polycystic ovary syndrome provides insight into the obesity-related immune responses. Front Endocrinol (Lausanne) 2024; 15:1379293. [PMID: 38978626 PMCID: PMC11229675 DOI: 10.3389/fendo.2024.1379293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/03/2024] [Indexed: 07/10/2024] Open
Abstract
Background Polycystic ovary syndrome (PCOS) is the most common reproductive-endocrine disorder with wide-ranging metabolic implications, including obesity. RNA editing, a post-transcriptional modification, can fine-tune protein function and introduce heterogeneity. However, the role of RNA editing and its impact on adipose tissue function in PCOS remain poorly understood. Methods This study aimed to comprehensively analyze RNA-editing events in abdominal and subcutaneous adipose tissue of PCOS patients and healthy controls using high-throughput whole-genome sequencing (WGS) and RNA sequencing. Results Our results revealed that PCOS patients exhibited more RNA-editing sites, with adenosine-to-inosine (A-to-I) editing being prevalent. The expression of ADAR genes, responsible for A-to-I editing, was also higher in PCOS. Aberrant RNA-editing sites in PCOS adipose tissue was enriched in immune responses, and interleukin-12 biosynthetic process. Tumor necrosis factor (TNF) signaling, nuclear factor kappa B (NF-κB) signaling, Notch signaling, terminal uridylyl transferase 4 (TUT4), hook microtubule tethering protein 3 (HOOK3), and forkhead box O1 (FOXO1) were identified to be of significant differences. Differentially expressed genes (DEGs) in PCOS adipose tissue were enriched in immune responses compared with controls, and the DEGs between subcutaneous and abdominal adipose tissue were also enriched in immune responses suggesting the important role of subcutaneous adipose tissue. Furthermore, we identified the correlations between RNA editing levels and RNA expression levels of specific genes, such as ataxia-telangiectasia mutated (ATM) and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) in inflammation pathways and ATM, TUT4, and YTH N6-methyladenosine RNA-binding protein C2 (YTHDC2) in oocyte development pathway. Conclusions These findings suggest that RNA-editing dysregulation in PCOS adipose tissue may contribute to inflammatory dysregulations. Understanding the interplay between RNA editing and adipose tissue function may unveil potential therapeutic targets for PCOS management. However, further research and validation are required to fully elucidate the molecular mechanisms underlying these associations.
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Affiliation(s)
- Hanxiao Chen
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Tongtong Li
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- Department of Physiology School of Basic Medical Sciences, Cheeloo College of Medicine Shandong University, Jinan, Shandong, China
| | - Rui Gao
- Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
- The Reproductive Medical Center, Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Meng Cheng
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Qiong Zhang
- Department of Obstetrics and Gynecology, Sichuan Province Ziyang Maternal and Child Health Care Hospital, Ziyang, China
| | - Xiumei Liu
- Department of Obstetrics and Gynecology, Sichuan Province Ziyang Maternal and Child Health Care Hospital, Ziyang, China
| | - Mingli Chen
- Department of Obstetrics and Gynecology, Sichuan Province Ziyang Maternal and Child Health Care Hospital, Ziyang, China
| | - Xin Liao
- Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
- Department of Operation Room, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Lang Qin
- Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
- The Reproductive Medical Center, Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
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James C, Trevisan-Herraz M, Juan D, Rico D. Evolutionary analysis of gene ages across TADs associates chromatin topology with whole-genome duplications. Cell Rep 2024; 43:113895. [PMID: 38517894 DOI: 10.1016/j.celrep.2024.113895] [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: 08/20/2022] [Revised: 11/03/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024] Open
Abstract
Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.
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Affiliation(s)
- Caelinn James
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Scotland's Rural College (SRUC), The Roslin Institute Building, Easter Bush, Midlothian, UK
| | - Marco Trevisan-Herraz
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - David Juan
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain; Systems Biology Department, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Daniel Rico
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), CSIC-Universidad de Sevilla-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain.
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4
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Pan L, Wang H, Yang B, Li W. A protein network refinement method based on module discovery and biological information. BMC Bioinformatics 2024; 25:157. [PMID: 38643108 PMCID: PMC11031909 DOI: 10.1186/s12859-024-05772-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: 02/21/2024] [Accepted: 04/10/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.
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Affiliation(s)
- Li Pan
- Hunan Institute of Science and Technology, Yueyang, 414006, China
- Hunan Engineering Research Center of Multimodal Health Sensing and Intelligent Analysis, Yueyang, 414006, China
| | - Haoyue Wang
- Hunan Institute of Science and Technology, Yueyang, 414006, China.
| | - Bo Yang
- Hunan Institute of Science and Technology, Yueyang, 414006, China
- Hunan Engineering Research Center of Multimodal Health Sensing and Intelligent Analysis, Yueyang, 414006, China
| | - Wenbin Li
- Hunan Institute of Science and Technology, Yueyang, 414006, China.
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Zhang J, Chen W, Chen G, Flannick J, Fikse E, Smerin G, Degner K, Yang Y, Xu C, Li Y, Hanover JA, Simonds WF. Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes. Hum Mol Genet 2024; 33:655-666. [PMID: 36255737 PMCID: PMC11000659 DOI: 10.1093/hmg/ddac255] [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: 05/04/2022] [Revised: 09/28/2022] [Accepted: 10/10/2022] [Indexed: 11/15/2022] Open
Abstract
How ancestry-associated genetic variance affects disparities in the risk of polygenic diseases and influences the identification of disease-associated genes warrants a deeper understanding. We hypothesized that the discovery of genes associated with polygenic diseases may be limited by the overreliance on single-nucleotide polymorphism (SNP)-based genomic investigation, as most significant variants identified in genome-wide SNP association studies map to introns and intergenic regions of the genome. To overcome such potential limitations, we developed a gene-constrained, function-based analytical method centered on high-risk variants (hrV) that encode frameshifts, stopgains or splice site disruption. We analyzed the total number of hrV per gene in populations of different ancestry, representing a total of 185 934 subjects. Using this analysis, we developed a quantitative index of hrV (hrVI) across 20 428 genes within each population. We then applied hrVI analysis to the discovery of genes associated with type 2 diabetes mellitus (T2DM), a polygenic disease with ancestry-related disparity. HrVI profiling and gene-to-gene comparisons of ancestry-specific hrV between the case (20 781 subjects) and control (24 440 subjects) populations in the T2DM national repository identified 57 genes associated with T2DM, 40 of which were discoverable only by ancestry-specific analysis. These results illustrate how a function-based, ancestry-specific analysis of genetic variations can accelerate the identification of genes associated with polygenic diseases. Besides T2DM, such analysis may facilitate our understanding of the genetic basis for other polygenic diseases that are also greatly influenced by environmental and behavioral factors, such as obesity, hypertension and Alzheimer's disease.
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Affiliation(s)
- Jianhua Zhang
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - Weiping Chen
- Genomic Core, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
- Laboratory of Cell and Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, United States
| | - Jason Flannick
- Metabolism Program, Broad Institute, Cambridge, MA 02142, United States
| | - Emma Fikse
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - Glenda Smerin
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - Katherine Degner
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - Yanqin Yang
- Laboratory of Transplantation Genomics, National Heart Lung and Blood Institute; National Institutes of Health, Bethesda, MD 20892, United States
| | - Catherine Xu
- Genomic Core, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | | | - Yulong Li
- Milton S. Hershey Medical Center, Division of Endocrinology, Diabetes and Metabolism, Penn State University, Hershey, PA 17033, United States
| | - John A Hanover
- Laboratory of Cell and Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
| | - William F Simonds
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, United States
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Lucena-Padros H, Bravo-Gil N, Tous C, Rojano E, Seoane-Zonjic P, Fernández RM, Ranea JAG, Antiñolo G, Borrego S. Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease. Biomolecules 2024; 14:164. [PMID: 38397401 PMCID: PMC10886964 DOI: 10.3390/biom14020164] [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: 12/05/2023] [Revised: 01/15/2024] [Accepted: 01/27/2024] [Indexed: 02/25/2024] Open
Abstract
Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.
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Affiliation(s)
- Helena Lucena-Padros
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
| | - Nereida Bravo-Gil
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Cristina Tous
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
| | - Pedro Seoane-Zonjic
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
| | - Raquel María Fernández
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Juan A. G. Ranea
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - Guillermo Antiñolo
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Salud Borrego
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
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7
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Wang H, Zhao L, Yang L, Ge M, Yang X, Gao Z, Cun Y, Xiao F, Kong Q. Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity. Aging Cell 2024; 23:e13916. [PMID: 37400997 PMCID: PMC10776117 DOI: 10.1111/acel.13916] [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: 10/26/2022] [Revised: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 07/05/2023] Open
Abstract
Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.
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Affiliation(s)
- Hao‐Tian Wang
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Long Zhao
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life ScienceUniversity of Chinese Academy of SciencesKunmingChina
| | - Li‐Qin Yang
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Ming‐Xia Ge
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life ScienceUniversity of Chinese Academy of SciencesKunmingChina
| | - Xing‐Li Yang
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life ScienceUniversity of Chinese Academy of SciencesKunmingChina
| | - Zong‐Liang Gao
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life ScienceUniversity of Chinese Academy of SciencesKunmingChina
| | - Yu‐Peng Cun
- Pediatric Research Institute/Ministry of Education Key Laboratory of Child Development and Disorders/National Clinical Research Center for Child Health and DisordersChildren's Hospital of Chongqing Medical UniversityChongqingChina
| | - Fu‐Hui Xiao
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Qing‐Peng Kong
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- CAS Center for Excellence in Animal Evolution and GeneticsChinese Academy of SciencesKunmingChina
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8
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Heydari N, Mahdizadeh M, Jafari SM. The evolving landscape of involvement of DTYMK enzymes in cancer. Med Oncol 2023; 40:213. [PMID: 37358701 DOI: 10.1007/s12032-023-02086-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/13/2023] [Indexed: 06/27/2023]
Abstract
Cancer cells require continuous synthesis of nucleotides for their uncontrolled proliferation. Deoxy thymidylate kinase (DTYMK) belongs to the thymidylate kinase family and is concerned with pyrimidine metabolism. DTYMK catalyzes the ATP-based conversion of deoxy-TMP to deoxy-TDP in both de novo and salvage pathways. Different studies demonstrated that DTYMK was increased in various types of cancers such as hepatocellular carcinoma, colon cancer, lung cancer, etc. Increased level of DTYMK was associated with poorer survival and prognosis, stage, grade and size of tumor, cell proliferation, colony formation, enhanced sensitivity to chemotherapy drugs, migration. Some studies were showed that knockdown of DTYMK reduced the signaling pathway of PI3K/AKT and downregulated expression of CART, MAPKAPK2, AKT1 and NRF1. Moreover, some microRNAs could suppress DTYMK expressions. On the other hand based on the TIMER database, the infiltration of macrophages, dendritic cells, neutrophils, B cells, CD4+ T cell and CD8+ T cell is affected by DTYMK. In the present review, we describe the genomic location, protein structure and isoforms of DTYMK and focus on its role in cancer development.
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Affiliation(s)
- Nadia Heydari
- Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran
- Department of Biochemistry and Biophysics, Faculty of Medicine, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran
| | - Mahsa Mahdizadeh
- Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran
- Department of Biochemistry and Biophysics, Faculty of Medicine, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran
| | - Seyyed Mehdi Jafari
- Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran.
- Department of Biochemistry and Biophysics, Faculty of Medicine, Golestan University of Medical Sciences, Gorgan, Islamic Republic of Iran.
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9
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Besselink N, Keijer J, Vermeulen C, Boymans S, de Ridder J, van Hoeck A, Cuppen E, Kuijk E. The genome-wide mutational consequences of DNA hypomethylation. Sci Rep 2023; 13:6874. [PMID: 37106015 PMCID: PMC10140063 DOI: 10.1038/s41598-023-33932-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 04/21/2023] [Indexed: 04/29/2023] Open
Abstract
DNA methylation is important for establishing and maintaining cell identity and for genomic stability. This is achieved by regulating the accessibility of regulatory and transcriptional elements and the compaction of subtelomeric, centromeric, and other inactive genomic regions. Carcinogenesis is accompanied by a global loss in DNA methylation, which facilitates the transformation of cells. Cancer hypomethylation may also cause genomic instability, for example through interference with the protective function of telomeres and centromeres. However, understanding the role(s) of hypomethylation in tumor evolution is incomplete because the precise mutational consequences of global hypomethylation have thus far not been systematically assessed. Here we made genome-wide inventories of all possible genetic variation that accumulates in single cells upon the long-term global hypomethylation by CRISPR interference-mediated conditional knockdown of DNMT1. Depletion of DNMT1 resulted in a genomewide reduction in DNA methylation. The degree of DNA methylation loss was similar to that observed in many cancer types. Hypomethylated cells showed reduced proliferation rates, increased transcription of genes, reactivation of the inactive X-chromosome and abnormal nuclear morphologies. Prolonged hypomethylation was accompanied by increased chromosomal instability. However, there was no increase in mutational burden, enrichment for certain mutational signatures or accumulation of structural variation to the genome. In conclusion, the primary consequence of hypomethylation is genomic instability, which in cancer leads to increased tumor heterogeneity and thereby fuels cancer evolution.
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Affiliation(s)
- Nicolle Besselink
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Janneke Keijer
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Carlo Vermeulen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sander Boymans
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Arne van Hoeck
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Hartwig Medical Foundation, Amsterdam, The Netherlands
| | - Ewart Kuijk
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
- Division of Pediatric Gastroenterology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands.
- Regenerative Medicine Center Utrecht, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.
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10
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Alternative splicing liberates a cryptic cytoplasmic isoform of mitochondrial MECR that antagonizes influenza virus. PLoS Biol 2022; 20:e3001934. [PMID: 36542656 PMCID: PMC9815647 DOI: 10.1371/journal.pbio.3001934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/05/2023] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Viruses must balance their reliance on host cell machinery for replication while avoiding host defense. Influenza A viruses are zoonotic agents that frequently switch hosts, causing localized outbreaks with the potential for larger pandemics. The host range of influenza virus is limited by the need for successful interactions between the virus and cellular partners. Here we used immunocompetitive capture-mass spectrometry to identify cellular proteins that interact with human- and avian-style viral polymerases. We focused on the proviral activity of heterogenous nuclear ribonuclear protein U-like 1 (hnRNP UL1) and the antiviral activity of mitochondrial enoyl CoA-reductase (MECR). MECR is localized to mitochondria where it functions in mitochondrial fatty acid synthesis (mtFAS). While a small fraction of the polymerase subunit PB2 localizes to the mitochondria, PB2 did not interact with full-length MECR. By contrast, a minor splice variant produces cytoplasmic MECR (cMECR). Ectopic expression of cMECR shows that it binds the viral polymerase and suppresses viral replication by blocking assembly of viral ribonucleoprotein complexes (RNPs). MECR ablation through genome editing or drug treatment is detrimental for cell health, creating a generic block to virus replication. Using the yeast homolog Etr1 to supply the metabolic functions of MECR in MECR-null cells, we showed that specific antiviral activity is independent of mtFAS and is reconstituted by expressing cMECR. Thus, we propose a strategy where alternative splicing produces a cryptic antiviral protein that is embedded within a key metabolic enzyme.
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11
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Ba Q, Hei Y, Dighe A, Li W, Maziarz J, Pak I, Wang S, Wagner GP, Liu Y. Proteotype coevolution and quantitative diversity across 11 mammalian species. SCIENCE ADVANCES 2022; 8:eabn0756. [PMID: 36083897 PMCID: PMC9462687 DOI: 10.1126/sciadv.abn0756] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.
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Affiliation(s)
- Qian Ba
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Yuanyuan Hei
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Anasuya Dighe
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Jamie Maziarz
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Irene Pak
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Günter P. Wagner
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06510, USA
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12
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LeBlanc N, Charles TC. Bacterial genome reductions: Tools, applications, and challenges. Front Genome Ed 2022; 4:957289. [PMID: 36120530 PMCID: PMC9473318 DOI: 10.3389/fgeed.2022.957289] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/29/2022] [Indexed: 11/16/2022] Open
Abstract
Bacterial cells are widely used to produce value-added products due to their versatility, ease of manipulation, and the abundance of genome engineering tools. However, the efficiency of producing these desired biomolecules is often hindered by the cells’ own metabolism, genetic instability, and the toxicity of the product. To overcome these challenges, genome reductions have been performed, making strains with the potential of serving as chassis for downstream applications. Here we review the current technologies that enable the design and construction of such reduced-genome bacteria as well as the challenges that limit their assembly and applicability. While genomic reductions have shown improvement of many cellular characteristics, a major challenge still exists in constructing these cells efficiently and rapidly. Computational tools have been created in attempts at minimizing the time needed to design these organisms, but gaps still exist in modelling these reductions in silico. Genomic reductions are a promising avenue for improving the production of value-added products, constructing chassis cells, and for uncovering cellular function but are currently limited by their time-consuming construction methods. With improvements to and the creation of novel genome editing tools and in silico models, these approaches could be combined to expedite this process and create more streamlined and efficient cell factories.
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Affiliation(s)
- Nicole LeBlanc
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
- *Correspondence: Nicole LeBlanc,
| | - Trevor C. Charles
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
- Metagenom Bio Life Science Inc., Waterloo, ON, Canada
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13
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Multiplex base editing to convert TAG into TAA codons in the human genome. Nat Commun 2022; 13:4482. [PMID: 35918324 PMCID: PMC9345975 DOI: 10.1038/s41467-022-31927-8] [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: 08/09/2021] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Whole-genome recoding has been shown to enable nonstandard amino acids, biocontainment and viral resistance in bacteria. Here we take the first steps to extend this to human cells demonstrating exceptional base editing to convert TAG to TAA for 33 essential genes via a single transfection, and examine base-editing genome-wide (observing ~40 C-to-T off-target events in essential gene exons). We also introduce GRIT, a computational tool for recoding. This demonstrates the feasibility of recoding, and highly multiplex editing in mammalian cells. Whole-genome recoding has been shown to enable nonstandard amino acids, biocontainment and viral resistance in bacteria. Here the authors extend this to human cells using base editing to convert TAG to TAA for 33 essential genes via a single transfection followed by examining base-editing genome-wide.
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14
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Gu X. A Simple Evolutionary Model of Genetic Robustness After Gene Duplication. J Mol Evol 2022; 90:352-361. [PMID: 35913597 DOI: 10.1007/s00239-022-10065-1] [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: 03/01/2022] [Accepted: 06/23/2022] [Indexed: 10/16/2022]
Abstract
When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O+), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [PE(O-)] was much higher than that for those with ancestral dispensability [PE(O+)]. While it was negligible in the yeast duplicate pairs, PE(O+) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.
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Affiliation(s)
- Xun Gu
- The Laurence H. Baker Center in Bioinformatics on Biological Statistics, Department of Genetics, Development and Cell Biology, Program of Ecological and Evolutionary Biology, Iowa State University, Ames, IA, 50011, USA.
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15
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Gu J, Dai J, Lu H, Zhao H. Comprehensive analysis of ubiquitously expressed genes in human, from a data-driven perspective. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00042-0. [PMID: 35569803 PMCID: PMC10373092 DOI: 10.1016/j.gpb.2021.08.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 07/18/2021] [Accepted: 09/27/2021] [Indexed: 01/08/2023]
Abstract
Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering the regulatory codes of the human genome and understanding the molecular mechanism of human disease. The ubiquitously expressed genes (UEGs) refer to those genes expressed across a majority, if not all, phenotypic and physiological conditions of an organism. It is known that many human genes are broadly expressed across tissues. However, most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns, thus limiting the potential use of UEG information. In this article, we proposed a novel data-driven framework to leverage the extensive collection of ∼40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns, which offers a valuable resource to further characterize human transcriptome. Our results suggest that about half (12,234; 49.01%) of the human genes are expressed in at least 80% of human transcriptomes, and the median size of the human transcriptome is 16,342 (65.44%). Through gene clustering, we identified a set of UEGs, named LoVarUEGs, that have stable expression across human transcriptomes and can be used as internal reference genes for expression measurement. To further demonstrate the usefulness of this resource, we evaluated the global expression patterns for 16 previously predicted disallowed genes in islets beta cells and found that seven of these genes showed relatively more varied expression patterns, suggesting that the repression of these genes may not be unique to islets beta cells.
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Affiliation(s)
- Jianlei Gu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai 200040, China; Department of Biostatistics, Yale University, New Haven, CT, 06511, United States
| | - Jiawei Dai
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai 200040, China.
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, 06511, United States.
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16
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Schapke J, Tavares A, Recamonde-Mendoza M. EPGAT: Gene Essentiality Prediction With Graph Attention Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1615-1626. [PMID: 33497339 DOI: 10.1109/tcbb.2021.3054738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for Essentiality Prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs), operating on graph-structured data. Our model directly learns gene essentiality patterns from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with ROC AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.
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17
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Köferle A, Schlattl A, Hörmann A, Thatikonda V, Popa A, Spreitzer F, Ravichandran MC, Supper V, Oberndorfer S, Puchner T, Wieshofer C, Corcokovic M, Reiser C, Wöhrle S, Popow J, Pearson M, Martinez J, Weitzer S, Mair B, Neumüller RA. Interrogation of cancer gene dependencies reveals paralog interactions of autosome and sex chromosome-encoded genes. Cell Rep 2022; 39:110636. [PMID: 35417719 DOI: 10.1016/j.celrep.2022.110636] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 12/22/2021] [Accepted: 03/16/2022] [Indexed: 02/07/2023] Open
Abstract
Genetic networks are characterized by extensive buffering. During tumor evolution, disruption of functional redundancies can create de novo vulnerabilities that are specific to cancer cells. Here, we systematically search for cancer-relevant paralog interactions using CRISPR screens and publicly available loss-of-function datasets. Our analysis reveals >2,000 candidate dependencies, several of which we validate experimentally, including CSTF2-CSTF2T, DNAJC15-DNAJC19, FAM50A-FAM50B, and RPP25-RPP25L. We provide evidence that RPP25L can physically and functionally compensate for the absence of RPP25 as a member of the RNase P/MRP complexes in tRNA processing. Our analysis also reveals unexpected redundancies between sex chromosome genes. We show that chrX- and chrY-encoded paralogs, such as ZFX-ZFY, DDX3X-DDX3Y, and EIF1AX-EIF1AY, are functionally linked. Tumor cell lines from male patients with loss of chromosome Y become dependent on the chrX-encoded gene. We propose targeting of chrX-encoded paralogs as a general therapeutic strategy for human tumors that have lost the Y chromosome.
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Affiliation(s)
- Anna Köferle
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Andreas Schlattl
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Alexandra Hörmann
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Venu Thatikonda
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Alexandra Popa
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Fiona Spreitzer
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | | | - Verena Supper
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Sarah Oberndorfer
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Teresa Puchner
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Corinna Wieshofer
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Maja Corcokovic
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Christoph Reiser
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Simon Wöhrle
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Johannes Popow
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Mark Pearson
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
| | - Javier Martinez
- Max Perutz Labs, Medical University of Vienna, Vienna BioCenter (VBC), Dr. Bohr-Gasse 9/2, 1030 Vienna, Austria
| | - Stefan Weitzer
- Max Perutz Labs, Medical University of Vienna, Vienna BioCenter (VBC), Dr. Bohr-Gasse 9/2, 1030 Vienna, Austria
| | - Barbara Mair
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria.
| | - Ralph A Neumüller
- Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria.
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18
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Marques de Castro G, Hastenreiter Z, Silva Monteiro TA, Martins da Silva TT, Pereira Lobo F. Cross-species prediction of essential genes in insects. Bioinformatics 2022; 38:1504-1513. [PMID: 34999756 DOI: 10.1093/bioinformatics/btac009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/12/2021] [Accepted: 01/04/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Insects possess a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and disease vectors. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions, even though some of this information may not be available for the majority of insect species with genomic sequences available. RESULTS Here, we present and validate a machine learning strategy to predict essential genes in insects using sequence-based intrinsic attributes (statistical and physicochemical data) together with the predictions of subcellular location and transcriptomic data, if available. We gathered information available in public databases describing essential and non-essential genes for Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We proceeded by computing intrinsic and extrinsic attributes that were used to train statistical models in one species and tested by their capability of predicting essential genes in the other. Even models trained using only intrinsic attributes are capable of predicting genes in the other insect species, including the prediction of lineage-specific essential genes. Furthermore, the inclusion of RNA-Seq data is a major factor to increase classifier performance. AVAILABILITY AND IMPLEMENTATION The code, data and final models produced in this study are freely available at https://github.com/g1o/GeneEssentiality/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giovanni Marques de Castro
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Zandora Hastenreiter
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Thiago Augusto Silva Monteiro
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Thieres Tayroni Martins da Silva
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Francisco Pereira Lobo
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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19
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Epigenomic signatures on paralogous genes reveal underappreciated universality of active histone codes adopted across animals. Comput Struct Biotechnol J 2022; 20:353-367. [PMID: 35035788 PMCID: PMC8741409 DOI: 10.1016/j.csbj.2021.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/15/2021] [Accepted: 12/18/2021] [Indexed: 11/21/2022] Open
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20
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Beder T, Aromolaran O, Dönitz J, Tapanelli S, Adedeji E, Adebiyi E, Bucher G, Koenig R. Identifying essential genes across eukaryotes by machine learning. NAR Genom Bioinform 2021; 3:lqab110. [PMID: 34859210 PMCID: PMC8634067 DOI: 10.1093/nargab/lqab110] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/09/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.
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Affiliation(s)
- Thomas Beder
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Institute of Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Department of Internal Medicine II, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Olufemi Aromolaran
- Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jürgen Dönitz
- Department of Evolutionary Developmental Genetics, GZMB, University of Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
- Department of Medical Bioinformatics, University Medical Center Göttingen (UMG), 37099 Göttingen, Germany
| | - Sofia Tapanelli
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Eunice O Adedeji
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
- Department of Biochemistry, Covenant University, Ota, Ogun State, Nigeria
| | - Ezekiel Adebiyi
- Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Gregor Bucher
- Department of Evolutionary Developmental Genetics, GZMB, University of Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Rainer Koenig
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Institute of Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
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21
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A Method to Map Gene Essentiality of Human Pluripotent Stem Cells by Genome-Scale CRISPR Screens with Inducible Cas9. Methods Mol Biol 2021. [PMID: 34709608 DOI: 10.1007/978-1-0716-1720-5_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]
Abstract
Human pluripotent stem cells (hPSCs) have the capacity for self-renewal and differentiation into most cell types and, in contrast to widely used cell lines, are karyotypically normal and non-transformed. Hence, hPSCs are considered the gold-standard system for modelling diseases, especially in the field of regenerative medicine. Despite widespread research use of hPSCs and induced pluripotent stem cells (iPSCs), the systematic understanding of pluripotency and lineage differentiation mechanisms are still incomplete. Before tackling the complexities of lineage differentiation with genetic screens, it is critical to catalogue the general genetic requirements for cell fitness and proliferation in the pluripotent state and assess their plasticity under commonly used culture conditions.We describe a method to map essential genetic determinants of hPSC fitness and pluripotency, herein defined as cell reproduction, by genome-scale loss-of-function CRISPR screens in an inducible S. pyogenes Cas9 H1 hPSC line. To address questions of context-dependent gene essentiality, we include protocols for screening hPSCs cultured on feeder cells and laminin, two commonly used growth substrates. This method establishes parameters for genome-wide screens in hPSCs, making human stem cells amenable for functional genomics approaches to facilitate investigation of hPSC biology.
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22
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Mukherjee S, Cogan JD, Newman JH, Phillips JA, Hamid R, Meiler J, Capra JA. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am J Hum Genet 2021; 108:1946-1963. [PMID: 34529933 PMCID: PMC8546038 DOI: 10.1016/j.ajhg.2021.08.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.
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Affiliation(s)
- Souhrid Mukherjee
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Joy D Cogan
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - John H Newman
- Pulmonary Hypertension Center, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John A Phillips
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Rizwan Hamid
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04103, Germany; Department of Chemistry, Leipzig University, Leipzig 04109, Germany; Department of Computer Science, Leipzig University, Leipzig 04109, Germany.
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA.
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23
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Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines. Cell Syst 2021; 12:1144-1159.e6. [PMID: 34529928 DOI: 10.1016/j.cels.2021.08.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/08/2021] [Accepted: 08/18/2021] [Indexed: 12/15/2022]
Abstract
Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analyzing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific. A record of this paper's transparent peer review process is included in the supplemental information.
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24
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Campos TL, Korhonen PK, Hofmann A, Gasser RB, Young ND. Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications. Biotechnol Adv 2021; 54:107822. [PMID: 34461202 DOI: 10.1016/j.biotechadv.2021.107822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/17/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022]
Abstract
The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.
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Affiliation(s)
- Tulio L Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia; Bioinformatics Core Facility, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil
| | - Pasi K Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Andreas Hofmann
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
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25
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Reed CJ, Hutinet G, de Crécy-Lagard V. Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase. Biomolecules 2021; 11:1282. [PMID: 34572495 PMCID: PMC8469502 DOI: 10.3390/biom11091282] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/20/2021] [Accepted: 08/24/2021] [Indexed: 12/12/2022] Open
Abstract
Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as "GTP cyclohydrolase I type 2" through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.
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Affiliation(s)
- Colbie J. Reed
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA; (C.J.R.); (G.H.)
| | - Geoffrey Hutinet
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA; (C.J.R.); (G.H.)
| | - Valérie de Crécy-Lagard
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA; (C.J.R.); (G.H.)
- Genetics Institute, University of Florida, Gainesville, FL 32611, USA
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26
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Aromolaran O, Beder T, Adedeji E, Ajamma Y, Oyelade J, Adebiyi E, Koenig R. Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning. Comput Struct Biotechnol J 2021; 19:4581-4592. [PMID: 34471501 PMCID: PMC8385402 DOI: 10.1016/j.csbj.2021.08.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 11/25/2022] Open
Abstract
Pathogens causing infections, and particularly when invading the host cells, require the host cell machinery for efficient regeneration and proliferation during infection. For their life cycle, host proteins are needed and these Host Dependency Factors (HDF) may serve as therapeutic targets. Several attempts have approached screening for HDF producing large lists of potential HDF with, however, only marginal overlap. To get consistency into the data of these experimental studies, we developed a machine learning pipeline. As a case study, we used publicly available lists of experimentally derived HDF from twelve different screening studies based on gene perturbation in Drosophila melanogaster cells or in vivo upon bacterial or protozoan infection. A total of 50,334 gene features were generated from diverse categories including their functional annotations, topology attributes in protein interaction networks, nucleotide and protein sequence features, homology properties and subcellular localization. Cross-validation revealed an excellent prediction performance. All feature categories contributed to the model. Predicted and experimentally derived HDF showed a good consistency when investigating their common cellular processes and function. Cellular processes and molecular function of these genes were highly enriched in membrane trafficking, particularly in the trans-Golgi network, cell cycle and the Rab GTPase binding family. Using our machine learning approach, we show that HDF in organisms can be predicted with high accuracy evidencing their common investigated characteristics. We elucidated cellular processes which are utilized by invading pathogens during infection. Finally, we provide a list of 208 novel HDF proposed for future experimental studies.
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Affiliation(s)
- Olufemi Aromolaran
- Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Thomas Beder
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Institute of Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Eunice Adedeji
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
- Department of Biochemistry, Covenant University, Ota, Ogun State, Nigeria
| | - Yvonne Ajamma
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jelili Oyelade
- Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Ezekiel Adebiyi
- Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Rainer Koenig
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Institute of Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
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27
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Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A 2021; 118:e2102344118. [PMID: 34282017 PMCID: PMC8325244 DOI: 10.1073/pnas.2102344118] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.
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Affiliation(s)
- Hao Wang
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, 405 30 Gothenburg, Sweden
| | - Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Pinar Kocabas
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Pierre-Etienne Cholley
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Shan Huang
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Johan Gobom
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, 431 30 Mölndal, Sweden
| | - Thomas Svensson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Mattias Uhlen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
- Wallenberg Center for Protein Research, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, 431 30 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 30 Mölndal, Sweden
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London WC1E 6BT, United Kingdom
- UK Dementia Research Institute, University College London, London WC1E 6BT, United Kingdom
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden;
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- BioInnovation Institute, DK2200 Copenhagen, Denmark
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28
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Caldu-Primo JL, Verduzco-Martínez JA, Alvarez-Buylla ER, Davila-Velderrain J. In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints. NAR Genom Bioinform 2021; 3:lqab063. [PMID: 34268495 PMCID: PMC8276763 DOI: 10.1093/nargab/lqab063] [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: 05/06/2021] [Revised: 06/18/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.
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Affiliation(s)
- Jose Luis Caldu-Primo
- Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, CDMX., 04510, México
| | - Jorge Armando Verduzco-Martínez
- Departamento de Biología Celular y Genética, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León, 66400, México
| | - Elena R Alvarez-Buylla
- Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, CDMX., 04510, México
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29
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Huang CJ, Wang LHC, Wang YC. Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes. J Pers Med 2021; 11:jpm11070649. [PMID: 34357116 PMCID: PMC8307716 DOI: 10.3390/jpm11070649] [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: 06/17/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/16/2022] Open
Abstract
The hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and hepatocellular carcinoma. Most infected individuals become lifelong carriers of HBV as the drugs currently used to treat the patients can only control the disease, thereby achieving functional cure (loss of the hepatitis B surface antigen) but not complete cure (elimination of infected hepatocytes). Therefore, we aimed to identify the target genes for the selective killing of HBV-positive hepatocytes to develop a novel therapy for the treatment of HBV infection. Our strategy was to recognize the conditionally essential genes that are essential for the survival of HBV-positive hepatocytes, but non-essential for the HBV-negative hepatocytes. Using microarray gene expression data curated from the Gene Expression Omnibus database and the known essential genes from the Online GEne Essentiality database, we used two approaches, comprising the random walk with restart algorithm and the support vector machine approach, to determine the potential targets for the selective killing of HBV-positive hepatocytes. The final candidate genes list obtained using these two approaches consisted of 36 target genes, which may be conditionally essential for the cell survival of HBV-positive hepatocytes; however, this requires further experimental validation. Therefore, the genes identified in this study can be used as potential drug targets to develop novel therapeutic strategies for the treatment of HBV, and may ultimately help in achieving the elusive goal of a complete cure for hepatitis B.
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Affiliation(s)
- Chien-Jung Huang
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan;
| | - Lily Hui-Ching Wang
- Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu 300044, Taiwan;
- Department of Medical Science, National Tsing Hua University, Hsinchu 300044, Taiwan
| | - Yu-Chao Wang
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan;
- Correspondence:
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30
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Schonfeld E, Vendrow E, Vendrow J, Schonfeld E. On the relation of gene essentiality to intron structure: a computational and deep learning approach. Life Sci Alliance 2021; 4:4/6/e202000951. [PMID: 33906938 PMCID: PMC8127325 DOI: 10.26508/lsa.202000951] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 04/12/2021] [Accepted: 04/15/2021] [Indexed: 11/24/2022] Open
Abstract
Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3' end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.
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Affiliation(s)
| | | | - Joshua Vendrow
- University of California, Los Angeles, Los Angeles, CA, USA
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31
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Kuang S, Wei Y, Wang L. Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells. Bioinformatics 2021; 37:396-403. [PMID: 32790840 DOI: 10.1093/bioinformatics/btaa717] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/21/2020] [Accepted: 08/06/2020] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION Essential genes are required for the reproductive success at either cellular or organismal level. The identification of essential genes is important for understanding the core biological processes and identifying effective therapeutic drug targets. However, experimental identification of essential genes is costly, time consuming and labor intensive. Although several machine learning models have been developed to predict essential genes, these models are not readily applicable to lncRNAs. Moreover, the currently available models cannot be used to predict essential genes in a specific cancer type. RESULTS In this study, we have developed a new machine learning approach, XGEP (eXpression-based Gene Essentiality Prediction), to predict essential genes and candidate lncRNAs in cancer cells. The novelty of XGEP lies in the utilization of relevant features derived from the TCGA transcriptome dataset through collaborative embedding. When evaluated on the pan-cancer dataset, XGEP was able to accurately predict human essential genes and achieve significantly higher performance than previous models. Notably, several candidate lncRNAs selected by XGEP are reported to promote cell proliferation and inhibit cell apoptosis. Moreover, XGEP also demonstrated superior performance on cancer-type-specific datasets to identify essential genes. The comprehensive lists of candidate essential genes in specific cancer types may be used to guide experimental characterization and facilitate the discovery of drug targets for cancer therapy. AVAILABILITY AND IMPLEMENTATION The source code and datasets used in this study are freely available at https://github.com/BioDataLearning/XGEP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shuzhen Kuang
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.,Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA
| | - Yanzhang Wei
- Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA
| | - Liangjiang Wang
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.,Center for Human Genetics, Clemson University, Clemson, SC 29634, USA
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32
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Correa M, Lerat E, Birmelé E, Samson F, Bouillon B, Normand K, Rizzon C. The Transposable Element Environment of Human Genes Differs According to Their Duplication Status and Essentiality. Genome Biol Evol 2021; 13:6273345. [PMID: 33973013 PMCID: PMC8155550 DOI: 10.1093/gbe/evab062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2021] [Indexed: 12/13/2022] Open
Abstract
Transposable elements (TEs) are major components of eukaryotic genomes and represent approximately 45% of the human genome. TEs can be important sources of novelty in genomes and there is increasing evidence that TEs contribute to the evolution of gene regulation in mammals. Gene duplication is an evolutionary mechanism that also provides new genetic material and opportunities to acquire new functions. To investigate how duplicated genes are maintained in genomes, here, we explored the TE environment of duplicated and singleton genes. We found that singleton genes have more short-interspersed nuclear elements and DNA transposons in their vicinity than duplicated genes, whereas long-interspersed nuclear elements and long-terminal repeat retrotransposons have accumulated more near duplicated genes. We also discovered that this result is highly associated with the degree of essentiality of the genes with an unexpected accumulation of short-interspersed nuclear elements and DNA transposons around the more-essential genes. Our results underline the importance of taking into account the TE environment of genes to better understand how duplicated genes are maintained in genomes.
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Affiliation(s)
- Margot Correa
- Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), UMR CNRS 8071, ENSIIE, USC INRA, Université d'Evry Val d'Essonne, Evry, France
| | - Emmanuelle Lerat
- Laboratoire de Biométrie et Biologie Evolutive, UMR 5558, Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | - Etienne Birmelé
- Laboratoire MAP5 UMR 8145, Université de Paris, Paris, France
| | - Franck Samson
- Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), UMR CNRS 8071, ENSIIE, USC INRA, Université d'Evry Val d'Essonne, Evry, France
| | - Bérengère Bouillon
- Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), UMR CNRS 8071, ENSIIE, USC INRA, Université d'Evry Val d'Essonne, Evry, France
| | - Kévin Normand
- Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), UMR CNRS 8071, ENSIIE, USC INRA, Université d'Evry Val d'Essonne, Evry, France
| | - Carène Rizzon
- Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), UMR CNRS 8071, ENSIIE, USC INRA, Université d'Evry Val d'Essonne, Evry, France
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Aromolaran O, Aromolaran D, Isewon I, Oyelade J. Machine learning approach to gene essentiality prediction: a review. Brief Bioinform 2021; 22:6219158. [PMID: 33842944 DOI: 10.1093/bib/bbab128] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/04/2021] [Accepted: 03/17/2021] [Indexed: 12/17/2022] Open
Abstract
Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. SHORT ABSTRACT Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.
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Affiliation(s)
- Olufemi Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Damilare Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jelili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.,Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
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Parise D, Teixeira Dornelles Parise M, Pinto Gomide AC, Figueira Aburjaile F, Bentes Kato R, Salgado-Albarrán M, Tauch A, Ariston de Carvalho Azevedo V, Baumbach J. The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis. Microorganisms 2021; 9:microorganisms9020415. [PMID: 33671149 PMCID: PMC7923171 DOI: 10.3390/microorganisms9020415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/11/2021] [Accepted: 02/14/2021] [Indexed: 12/26/2022] Open
Abstract
Corynebacterium pseudotuberculosis is a Gram-positive, facultative intracellular, pathogenic bacterium that infects several different hosts, yielding serious economic losses in livestock farming. It causes several diseases including oedematous skin disease (OSD) in buffaloes, ulcerative lymphangitis (UL) in horses, and caseous lymphadenitis (CLA) in sheep, goats and humans. Despite its economic and medical-veterinary importance, our understanding concerning this organism’s transcriptional regulatory mechanisms is still limited. Here, we review the state of the art knowledge on transcriptional regulatory mechanisms of this pathogenic species, covering regulatory interactions mediated by two-component systems, transcription factors and sigma factors. Key transcriptional regulatory players involved in virulence and pathogenicity of C. pseudotuberculosis, such as the PhoPR system and DtxR, are in the focus of this review, as these regulators are promising targets for future vaccine design and drug development. We conclude that more experimental studies are needed to further understand the regulatory repertoire of this important zoonotic pathogen, and that regulators are promising targets for future vaccine design and drug development.
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Affiliation(s)
- Doglas Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
- Correspondence: or
| | - Mariana Teixeira Dornelles Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Anne Cybelle Pinto Gomide
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | | | - Rodrigo Bentes Kato
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Departamento de Ciencias Naturales, Universidad Autónoma Metropolitana Cuajimalpa, Mexico City 05348, Mexico
| | - Andreas Tauch
- Center for Biotechnology (CeBiTec), Bielefeld University, 33615 Bielefeld, Germany;
| | - Vasco Ariston de Carvalho Azevedo
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Computational BioMedicine lab, Institute of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
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35
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Zondervan NA, Martins Dos Santos VAP, Suarez-Diez M, Saccenti E. Phenotype and multi-omics comparison of Staphylococcus and Streptococcus uncovers pathogenic traits and predicts zoonotic potential. BMC Genomics 2021; 22:102. [PMID: 33541265 PMCID: PMC7860044 DOI: 10.1186/s12864-021-07388-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 01/13/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Staphylococcus and Streptococcus species can cause many different diseases, ranging from mild skin infections to life-threatening necrotizing fasciitis. Both genera consist of commensal species that colonize the skin and nose of humans and animals, and of which some can display a pathogenic phenotype. RESULTS We compared 235 Staphylococcus and 315 Streptococcus genomes based on their protein domain content. We show the relationships between protein persistence and essentiality by integrating essentiality predictions from two metabolic models and essentiality measurements from six large-scale transposon mutagenesis experiments. We identified clusters of strains within species based on proteins associated to similar biological processes. We built Random Forest classifiers that predicted the zoonotic potential. Furthermore, we identified shared attributes between of Staphylococcus aureus and Streptococcus pyogenes that allow them to cause necrotizing fasciitis. CONCLUSIONS Differences observed in clustering of strains based on functional groups of proteins correlate with phenotypes such as host tropism, capability to infect multiple hosts and drug resistance. Our method provides a solid basis towards large-scale prediction of phenotypes based on genomic information.
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Affiliation(s)
- Niels A Zondervan
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708WE, Wageningen, Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708WE, Wageningen, Netherlands
- LifeGlimmer GmBH, Markelstraße 38, 12163, Berlin, Germany
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708WE, Wageningen, Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708WE, Wageningen, Netherlands.
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36
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Abstract
The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others. The two-component system (TCS) helps bacteria sense and respond to environmental stimuli through histidine kinases and response regulators. TCSs are the largest family of multistep signal transduction processes, and they are involved in many important cellular processes such as antibiotic resistance, pathogenicity, quorum sensing, osmotic stress, and biofilms. Here, we perform the first comprehensive study to highlight the role of TCSs as potential drug targets against ESKAPEE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) pathogens through annotation, mapping, pangenomic status, gene orientation, and sequence variation analysis. The distribution of the TCSs is group specific with regard to Gram-positive and Gram-negative bacteria, except for KdpDE. The TCSs among ESKAPEE pathogens form closed pangenomes, except for Pseudomonas aeruginosa. Furthermore, their conserved nature due to closed pangenomes might make them good drug targets. Fitness score analysis suggests that any mutation in some TCSs such as BaeSR, ArcBA, EvgSA, and AtoSC, etc., might be lethal to the cell. Taken together, the results of this pangenomic assessment of TCSs reveal a range of strategies deployed by the ESKAPEE pathogens to manifest pathogenicity and antibiotic resistance. This study further suggests that the conserved features of TCSs might make them an attractive group of potential targets with which to address antibiotic resistance. IMPORTANCE The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others. This study provides a comprehensive overview of the pangenomic status of the TCSs among ESKAPEE pathogens. The annotation and pangenomic analysis of TCSs show that they are significantly distributed and conserved among the pathogens, as most of them form closed pangenomes. Furthermore, our analysis also reveals that the removal of the TCSs significantly affects the fitness of the cell. Hence, they may be used as promising drug targets against bacteria.
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37
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Integrative computational approach identifies drug targets in CD4 + T-cell-mediated immune disorders. NPJ Syst Biol Appl 2021; 7:4. [PMID: 33483502 PMCID: PMC7822845 DOI: 10.1038/s41540-020-00165-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open
Abstract
CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune-related diseases. CD4+ T cells’ metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naïve, Th1, Th2, and Th17 CD4+ T-cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4+ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4+ T-cell metabolism.
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38
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Internetwork connectivity of molecular networks across species of life. Sci Rep 2021; 11:1168. [PMID: 33441907 PMCID: PMC7806680 DOI: 10.1038/s41598-020-80745-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/23/2020] [Indexed: 01/29/2023] Open
Abstract
Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.
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39
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Wang T, Ruan S, Zhao X, Shi X, Teng H, Zhong J, You M, Xia K, Sun Z, Mao F. OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers. Nucleic Acids Res 2021; 49:D1289-D1301. [PMID: 33179738 PMCID: PMC7778899 DOI: 10.1093/nar/gkaa1033] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.
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Affiliation(s)
- Tao Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Shasha Ruan
- Department of Clinical Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430072, China
| | - Xiaolu Zhao
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
| | - Xiaohui Shi
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Jianing Zhong
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases of Ministry of Education, Gannan Medical University, Ganzhou 341000, China
| | | | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China
- CAS Center for Excellence in Brain Science and Intelligences Technology (CEBSIT), Shanghai 200031, China
- School of Basic Medical Science, Central South University, Changsha, Hunan 410078, China
| | - Zhongsheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Chinese Academy of Sciences, Beijing 100101, China
| | - Fengbiao Mao
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
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40
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Gurumayum S, Jiang P, Hao X, Campos TL, Young ND, Korhonen PK, Gasser RB, Bork P, Zhao XM, He LJ, Chen WH. OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines. Nucleic Acids Res 2021; 49:D998-D1003. [PMID: 33084874 PMCID: PMC7779042 DOI: 10.1093/nar/gkaa884] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 09/24/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022] Open
Abstract
OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.
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Affiliation(s)
- Sanathoi Gurumayum
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074 Wuhan, Hubei, China
| | - Puzi Jiang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074 Wuhan, Hubei, China
| | - Xiaowen Hao
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074 Wuhan, Hubei, China
| | - Tulio L Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
- Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Pasi K Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Peer Bork
- European molecular biology laboratory (EMBL), Meyerhof Strasse 1, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany
- Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Straße 10, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 200433 Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China
| | - Li-jie He
- Department of Medical Oncology, People's Hospital of Liaoning Province, 110016 Shenyang, China
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074 Wuhan, Hubei, China
- College of Life Science, Henan Normal University, 453007 Xinxiang, Henan, China
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41
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Kuang D, Weile J, Li R, Ouellette TW, Barber JA, Roth FP. MaveQuest: a web resource for planning experimental tests of human variant effects. Bioinformatics 2020; 36:3938-3940. [PMID: 32251504 PMCID: PMC7320626 DOI: 10.1093/bioinformatics/btaa228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 01/27/2020] [Accepted: 04/01/2020] [Indexed: 11/22/2022] Open
Abstract
Summary Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes. Availability and implementation MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Da Kuang
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Roujia Li
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Tom W Ouellette
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jarry A Barber
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
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42
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Liu S, Wang SX, Liu W, Wang C, Zhang FZ, Ye YN, Wu CS, Zheng WX, Rao N, Guo FB. CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:6031000. [PMID: 33306800 PMCID: PMC7731928 DOI: 10.1093/database/baaa112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/12/2020] [Accepted: 12/02/2020] [Indexed: 02/06/2023]
Abstract
Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand–protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics. Database URL:http://cefg.uestc.cn/ceg
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Affiliation(s)
- Shuo Liu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
| | - Shu-Xuan Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Liu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Chen Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fa-Zhan Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan-Nong Ye
- Bioinformatics and BioMedical Bigdata Mining Laboratory, Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
| | - Candy-S Wu
- Thomas Worthington High School, 300 West Granville Road, Worthington, OH 43085, USA
| | - Wen-Xin Zheng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Nini Rao
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Feng-Biao Guo
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
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Lyu J, Li JJ, Su J, Peng F, Chen YE, Ge X, Li W. DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features. SCIENCE ADVANCES 2020; 6:6/46/eaba6784. [PMID: 33177077 PMCID: PMC7673741 DOI: 10.1126/sciadv.aba6784] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 09/29/2020] [Indexed: 05/09/2023]
Abstract
Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.
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Affiliation(s)
- Jie Lyu
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Jianzhong Su
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fanglue Peng
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yiling Elaine Chen
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xinzhou Ge
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697, USA.
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44
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Vihinen M. Functional effects of protein variants. Biochimie 2020; 180:104-120. [PMID: 33164889 DOI: 10.1016/j.biochi.2020.10.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 12/11/2022]
Abstract
Genetic and other variations frequently affect protein functions. Scientific articles can contain confusing descriptions about which function or property is affected, and in many cases the statements are pure speculation without any experimental evidence. To clarify functional effects of protein variations of genetic or non-genetic origin, a systematic conceptualisation and framework are introduced. This framework describes protein functional effects on abundance, activity, specificity and affinity, along with countermeasures, which allow cells, tissues and organisms to tolerate, avoid, repair, attenuate or resist (TARAR) the effects. Effects on abundance discussed include gene dosage, restricted expression, mis-localisation and degradation. Enzymopathies, effects on kinetics, allostery and regulation of protein activity are subtopics for the effects of variants on activity. Variation outcomes on specificity and affinity comprise promiscuity, specificity, affinity and moonlighting. TARAR mechanisms redress variations with active and passive processes including chaperones, redundancy, robustness, canalisation and metabolic and signalling rewiring. A framework for pragmatic protein function analysis and presentation is introduced. All of the mechanisms and effects are described along with representative examples, most often in relation to diseases. In addition, protein function is discussed from evolutionary point of view. Application of the presented framework facilitates unambiguous, detailed and specific description of functional effects and their systematic study.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184, Lund, Sweden.
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45
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Parry DA, Tamayo-Orrego L, Carroll P, Marsh JA, Greene P, Murina O, Uggenti C, Leitch A, Káposzta R, Merő G, Nagy A, Orlik B, Kovács-Pászthy B, Quigley AJ, Riszter M, Rankin J, Reijns MAM, Szakszon K, Jackson AP. PRIM1 deficiency causes a distinctive primordial dwarfism syndrome. Genes Dev 2020; 34:1520-1533. [PMID: 33060134 PMCID: PMC7608753 DOI: 10.1101/gad.340190.120] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 09/18/2020] [Indexed: 12/26/2022]
Abstract
DNA replication is fundamental for cell proliferation in all organisms. Nonetheless, components of the replisome have been implicated in human disease, and here we report PRIM1 encoding the catalytic subunit of DNA primase as a novel disease gene. Using a variant classification agnostic approach, biallelic mutations in PRIM1 were identified in five individuals. PRIM1 protein levels were markedly reduced in patient cells, accompanied by replication fork asymmetry, increased interorigin distances, replication stress, and prolonged S-phase duration. Consequently, cell proliferation was markedly impaired, explaining the patients' extreme growth failure. Notably, phenotypic features distinct from those previously reported with DNA polymerase genes were evident, highlighting differing developmental requirements for this core replisome component that warrant future investigation.
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Affiliation(s)
- David A Parry
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Lukas Tamayo-Orrego
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Paula Carroll
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Joseph A Marsh
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Philip Greene
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Olga Murina
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Carolina Uggenti
- Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Andrea Leitch
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | | | - Rita Káposzta
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Gabriella Merő
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Andrea Nagy
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Brigitta Orlik
- Institute of Pathology, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Balázs Kovács-Pászthy
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, United Kingdom
| | - Magdolna Riszter
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Julia Rankin
- Department Clinical Genetics, Royal Devon and Exeter NHS Foundation Trust, Exeter EX1 2ED, United Kingdom
| | - Martin A M Reijns
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Katalin Szakszon
- Institute of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen H-4032, Hungary
| | - Andrew P Jackson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, the University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
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46
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Mangangcha IR, Malik MZ, Kucuk O, Ali S, Singh RKB. Kinless hubs are potential target genes in prostate cancer network. Genomics 2020; 112:5227-5239. [PMID: 32976977 DOI: 10.1016/j.ygeno.2020.09.033] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 08/28/2020] [Accepted: 09/14/2020] [Indexed: 02/06/2023]
Abstract
Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.
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Affiliation(s)
- Irengbam Rocky Mangangcha
- School of Interdisciplinary Sciences and Technology, Jamia Hamdard, New Delhi 110062, India; Bioinformatics Infrastructure Facility, BIF & Department of Biochemistry, School of Chemical and Life Sciences Jamia Hamdard, New Delhi 110062, India; Department of Zoology, Deshbandhu College, University of Delhi, New Delhi 110019, India; School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Md Zubbair Malik
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Omer Kucuk
- Winship Cancer Institute of Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, USA
| | - Shakir Ali
- Bioinformatics Infrastructure Facility, BIF & Department of Biochemistry, School of Chemical and Life Sciences Jamia Hamdard, New Delhi 110062, India
| | - R K Brojen Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
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Møller AF, Natarajan KN. Predicting gene regulatory networks from cell atlases. Life Sci Alliance 2020; 3:3/11/e202000658. [PMID: 32958603 PMCID: PMC7536823 DOI: 10.26508/lsa.202000658] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 08/24/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022] Open
Abstract
Integrated single-cell gene regulatory network from three mouse cell atlases captures global and cell type–specific regulatory modules and crosstalk, important for cellular identity. Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.
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Affiliation(s)
- Andreas Fønss Møller
- Department of Biochemistry and Molecular Biology, Functional Genomics and Metabolism Unit, University of Southern Denmark, Odense, Denmark
| | - Kedar Nath Natarajan
- Department of Biochemistry and Molecular Biology, Functional Genomics and Metabolism Unit, University of Southern Denmark, Odense, Denmark .,Danish Institute of Advanced Study, University of Southern Denmark, Odense, Denmark
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Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020; 12:E2694. [PMID: 32967266 PMCID: PMC7563533 DOI: 10.3390/cancers12092694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.
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Affiliation(s)
- Elyas Mohammadi
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, 25240 Erzurum, Turkey;
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden;
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
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49
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Liu J, Frochaux M, Gardeux V, Deplancke B, Robinson-Rechavi M. Inter-embryo gene expression variability recapitulates the hourglass pattern of evo-devo. BMC Biol 2020; 18:129. [PMID: 32950053 PMCID: PMC7502200 DOI: 10.1186/s12915-020-00842-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The evolution of embryological development has long been characterized by deep conservation. In animal development, the phylotypic stage in mid-embryogenesis is more conserved than either early or late stages among species within the same phylum. Hypotheses to explain this hourglass pattern have focused on purifying the selection of gene regulation. Here, we propose an alternative-genes are regulated in different ways at different stages and have different intrinsic capacities to respond to perturbations on gene expression. RESULTS To eliminate the influence of natural selection, we quantified the expression variability of isogenetic single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that the expression variability is lower at the phylotypic stage, supporting that the underlying regulatory architecture in this stage is more robust to stochastic variation on gene expression. We present evidence that the phylotypic stage is also robust to genetic variations on gene expression. Moreover, chromatin regulation appears to play a key role in the variation and evolution of gene expression. CONCLUSIONS We suggest that a phylum-level pattern of embryonic conservation can be explained by the intrinsic difference of gene regulatory mechanisms in different stages.
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Affiliation(s)
- Jialin Liu
- Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
| | - Michael Frochaux
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Gardeux
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bart Deplancke
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
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50
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Khorsand B, Savadi A, Naghibzadeh M. Comprehensive host-pathogen protein-protein interaction network analysis. BMC Bioinformatics 2020; 21:400. [PMID: 32912135 PMCID: PMC7488060 DOI: 10.1186/s12859-020-03706-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 07/31/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. RESULTS As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. CONCLUSIONS Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.
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Affiliation(s)
- Babak Khorsand
- Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Abdorreza Savadi
- Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
- Ferdowsi University of Mashhad, Azadi Square, Mashhad, 9177948974 Iran
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