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Mallawaarachchi S, Tonkin-Hill G, Pöntinen A, Calland J, Gladstone R, Arredondo-Alonso S, MacAlasdair N, Thorpe H, Top J, Sheppard S, Balding D, Croucher N, Corander J. Detecting co-selection through excess linkage disequilibrium in bacterial genomes. NAR Genom Bioinform 2024; 6:lqae061. [PMID: 38846349 PMCID: PMC11155488 DOI: 10.1093/nargab/lqae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/15/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024] Open
Abstract
Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.
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Affiliation(s)
| | | | - Anna K Pöntinen
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Norwegian National Advisory Unit on Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway
| | - Jessica K Calland
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | | | | | - Harry A Thorpe
- Department of Biostatistics, University of Oslo, Oslo, Norway
| | - Janetta Top
- Department of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands
| | - Samuel K Sheppard
- Ineos Oxford Institute of Antimicrobial Research, Department of Biology, University of Oxford, Oxford, United Kingdom
| | - David Balding
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne, Parkville, Victoria, Australia
| | - Nicholas J Croucher
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, United Kingdom
| | - Jukka Corander
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK
- Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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2
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DelaFuente J, Diaz-Colunga J, Sanchez A, San Millan A. Global epistasis in plasmid-mediated antimicrobial resistance. Mol Syst Biol 2024; 20:311-320. [PMID: 38409539 PMCID: PMC10987494 DOI: 10.1038/s44320-024-00012-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 02/28/2024] Open
Abstract
Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.
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Affiliation(s)
| | - Juan Diaz-Colunga
- Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
- Institute of Functional Biology & Genomics, IBFG - CSIC, Universidad de Salamanca, Salamanca, Spain
| | - Alvaro Sanchez
- Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.
- Institute of Functional Biology & Genomics, IBFG - CSIC, Universidad de Salamanca, Salamanca, Spain.
| | - Alvaro San Millan
- Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.
- Centro de Investigación Biológica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
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3
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Mosquera-Rendón J, Moreno-Herrera CX, Robledo J, Hurtado-Páez U. Genome-Wide Association Studies (GWAS) Approaches for the Detection of Genetic Variants Associated with Antibiotic Resistance: A Systematic Review. Microorganisms 2023; 11:2866. [PMID: 38138010 PMCID: PMC10745584 DOI: 10.3390/microorganisms11122866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 12/24/2023] Open
Abstract
Antibiotic resistance is a significant threat to public health worldwide. Genome-wide association studies (GWAS) have emerged as a powerful tool to identify genetic variants associated with this antibiotic resistance. By analyzing large datasets of bacterial genomes, GWAS can provide valuable insights into the resistance mechanisms and facilitate the discovery of new drug targets. The present study aimed to undertake a systematic review of different GWAS approaches used for detecting genetic variants associated with antibiotic resistance. We comprehensively searched the PubMed and Scopus databases to identify relevant studies published from 2013 to February 2023. A total of 40 studies met our inclusion criteria. These studies explored a wide range of bacterial species, antibiotics, and study designs. Notably, most of the studies were centered around human pathogens such as Mycobacterium tuberculosis, Escherichia coli, Neisseria gonorrhoeae, and Staphylococcus aureus. The review seeks to explore the several GWAS approaches utilized to investigate the genetic mechanisms associated with antibiotic resistance. Furthermore, it examines the contributions of GWAS approaches in identifying resistance-associated genetic variants through binary and continuous phenotypes. Overall, GWAS holds great potential to enhance our understanding of bacterial resistance and improve strategies to combat infectious diseases.
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Affiliation(s)
- Jeanneth Mosquera-Rendón
- Bacteriology and Mycobacteria Unit, Corporation for Biological Research (CIB), Medellín 050034, Colombia; (J.M.-R.); (J.R.)
- Microbiodiversity and Bioprospecting Group (Microbiop), Department of Biosciences, Faculty of Sciences, Universidad Nacional de Colombia, Medellín 050034, Colombia;
| | - Claudia Ximena Moreno-Herrera
- Microbiodiversity and Bioprospecting Group (Microbiop), Department of Biosciences, Faculty of Sciences, Universidad Nacional de Colombia, Medellín 050034, Colombia;
| | - Jaime Robledo
- Bacteriology and Mycobacteria Unit, Corporation for Biological Research (CIB), Medellín 050034, Colombia; (J.M.-R.); (J.R.)
| | - Uriel Hurtado-Páez
- Bacteriology and Mycobacteria Unit, Corporation for Biological Research (CIB), Medellín 050034, Colombia; (J.M.-R.); (J.R.)
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4
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Zhang JX, Yuan Y, Hu QH, Jin DZ, Bai Y, Xin WW, Kang L, Wang JL. Identification of potential pathogenic targets and survival strategies of Vibrio vulnificus through population genomics. Front Cell Infect Microbiol 2023; 13:1254379. [PMID: 37692161 PMCID: PMC10485832 DOI: 10.3389/fcimb.2023.1254379] [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: 07/07/2023] [Accepted: 07/27/2023] [Indexed: 09/12/2023] Open
Abstract
Vibrio vulnificus, a foodborne pathogen, has a high mortality rate. Despite its relevance to public health, the identification of virulence genes associated with the pathogenicity of currently known clinical isolates of V. vulnificus is incomplete and its synergistic pathogenesis remains unclear. Here, we integrate whole genome sequencing (WGS), genome-wide association studies (GWAS), and genome-wide epistasis studies (GWES), along with phenotype characterization to investigate the pathogenesis and survival strategies of V. vulnificus. GWAS and GWES identified a total of six genes (purH, gmr, yiaV, dsbD, ramA, and wbpA) associated with the pathogenicity of clinical isolates related to nucleotide/amino acid transport and metabolism, cell membrane biogenesis, signal transduction mechanisms, and protein turnover. Of these, five were newly discovered potential specific virulence genes of V. vulnificus in this study. Furthermore, GWES combined with phenotype experiments indicated that V. vulnificus isolates were clustered into two ecological groups (EGs) that shared distinct biotic and abiotic factors, and ecological strategies. Our study reveals pathogenic mechanisms and their evolution in V. vulnificus to provide a solid foundation for designing new vaccines and therapeutic targets.
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Affiliation(s)
- Jia-Xin Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing, China
| | - Yuan Yuan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing, China
| | - Qing-hua Hu
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Da-zhi Jin
- Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Yao Bai
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Wen-Wen Xin
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing, China
| | - Lin Kang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing, China
| | - Jing-Lin Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing, China
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5
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Green AG, Vargas R, Marin MG, Freschi L, Xie J, Farhat MR. Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations. Mol Biol Evol 2023; 40:msad131. [PMID: 37352142 PMCID: PMC10292908 DOI: 10.1093/molbev/msad131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 05/12/2023] [Accepted: 05/23/2023] [Indexed: 06/25/2023] Open
Abstract
Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.
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Affiliation(s)
- Anna G Green
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Roger Vargas
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
| | - Maximillian G Marin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Luca Freschi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jiaqi Xie
- Department of Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Maha R Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
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6
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Loiseau C, Windels EM, Gygli SM, Jugheli L, Maghradze N, Brites D, Ross A, Goig G, Reinhard M, Borrell S, Trauner A, Dötsch A, Aspindzelashvili R, Denes R, Reither K, Beisel C, Tukvadze N, Avaliani Z, Stadler T, Gagneux S. The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot. Nat Commun 2023; 14:1988. [PMID: 37031225 PMCID: PMC10082831 DOI: 10.1038/s41467-023-37719-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/28/2023] [Indexed: 04/10/2023] Open
Abstract
Multidrug-resistant tuberculosis (MDR-TB) is among the most frequent causes of death due to antimicrobial resistance. Although only 3% of global TB cases are MDR, geographical hotspots with up to 40% of MDR-TB have been observed in countries of the former Soviet Union. While the quality of TB control and patient-related factors are known contributors to such hotspots, the role of the pathogen remains unclear. Here we show that in the country of Georgia, a known hotspot of MDR-TB, MDR Mycobacterium tuberculosis strains of lineage 4 (L4) transmit less than their drug-susceptible counterparts, whereas most MDR strains of L2 suffer no such defect. Our findings further indicate that the high transmission fitness of these L2 strains results from epistatic interactions between the rifampicin resistance-conferring mutation RpoB S450L, compensatory mutations in the RNA polymerase, and other pre-existing genetic features of L2/Beijing clones that circulate in Georgia. We conclude that the transmission fitness of MDR M. tuberculosis strains is heterogeneous, but can be as high as drug-susceptible forms, and that such highly drug-resistant and transmissible strains contribute to the emergence and maintenance of hotspots of MDR-TB. As these strains successfully overcome the metabolic burden of drug resistance, and given the ongoing rollout of new treatment regimens against MDR-TB, proper surveillance should be implemented to prevent these strains from acquiring resistance to the additional drugs.
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Affiliation(s)
- Chloé Loiseau
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Etthel M Windels
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Sebastian M Gygli
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Levan Jugheli
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Center for Tuberculosis and Lung Diseases (NCTLD), Tbilisi, Georgia
| | - Nino Maghradze
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Center for Tuberculosis and Lung Diseases (NCTLD), Tbilisi, Georgia
| | - Daniela Brites
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Amanda Ross
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Galo Goig
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Miriam Reinhard
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Sonia Borrell
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Andrej Trauner
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Anna Dötsch
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Rebecca Denes
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Klaus Reither
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Nestani Tukvadze
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Center for Tuberculosis and Lung Diseases (NCTLD), Tbilisi, Georgia
| | - Zaza Avaliani
- National Center for Tuberculosis and Lung Diseases (NCTLD), Tbilisi, Georgia
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sebastien Gagneux
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
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7
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Dichio V, Zeng HL, Aurell E. Statistical genetics in and out of quasi-linkage equilibrium. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:052601. [PMID: 36944245 DOI: 10.1088/1361-6633/acc5fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.
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Affiliation(s)
- Vito Dichio
- Sorbonne Université, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Hong-Li Zeng
- School of Science, Nanjing University of Posts and Telecommunications, New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing 210023, People's Republic of China
| | - Erik Aurell
- Department of Computational Science and Technology, KTH-Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden
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8
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Zeng HL, Liu Y, Dichio V, Aurell E. Temporal epistasis inference from more than 3 500 000 SARS-CoV-2 genomic sequences. Phys Rev E 2022; 106:044409. [PMID: 36397507 DOI: 10.1103/physreve.106.044409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
We use direct coupling analysis (DCA) to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling. We use full-length, high-quality genomes from the GISAID repository up to October 2021 for a total of over 3 500 000 genomes. We find that DCA terms are more stable over time than correlations but nevertheless change over time as mutations disappear from the global population or reach fixation. Correlations are enriched for phylogenetic effects, and in particularly statistical dependencies at short genomic distances, while DCA brings out links at longer genomic distance. We discuss the validity of a DCA analysis under these conditions in terms of a transient auasilinkage equilibrium state. We identify putative epistatic interaction mutations involving loci in spike.
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Affiliation(s)
- Hong-Li Zeng
- School of Science, Nanjing University of Posts and Telecommunications, New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing 210023, China
| | - Yue Liu
- School of Science, Nanjing University of Posts and Telecommunications, New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing 210023, China
| | - Vito Dichio
- Inria Paris, Aramis Project Team, Paris 75013, France
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Erik Aurell
- Department of Computational Science and Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden
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9
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Dirmeier S, Beerenwinkel N. Structured hierarchical models for probabilistic inference from perturbation screening data. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Simon Dirmeier
- Department of Biosystems Science and Engineering, ETH Zurich
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10
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Boeck L, Burbaud S, Skwark M, Pearson WH, Sangen J, Wuest AW, Marshall EKP, Weimann A, Everall I, Bryant JM, Malhotra S, Bannerman BP, Kierdorf K, Blundell TL, Dionne MS, Parkhill J, Andres Floto R. Mycobacterium abscessus pathogenesis identified by phenogenomic analyses. Nat Microbiol 2022; 7:1431-1441. [PMID: 36008617 PMCID: PMC9418003 DOI: 10.1038/s41564-022-01204-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/19/2022] [Indexed: 12/12/2022]
Abstract
The medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome. We combined genome-wide association studies with proteome-wide computational structural modelling to define likely causal variants, and employed direct coupling analysis to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings and discover clinically relevant M. abscessus virulence factors including a secretion system, thus illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.
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Affiliation(s)
- Lucas Boeck
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Sophie Burbaud
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Marcin Skwark
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Will H Pearson
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Jasper Sangen
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Andreas W Wuest
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Eleanor K P Marshall
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Aaron Weimann
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | | | - Josephine M Bryant
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Sony Malhotra
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Scientific Computing Department, Science and Technology Facilities Council, Harwell, UK
| | - Bridget P Bannerman
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Katrin Kierdorf
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Marc S Dionne
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - R Andres Floto
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK.
- Cambridge Centre for AI in Medicine, Cambridge, UK.
- Cambridge Centre for Lung Infection, Royal Papworth Hospital, Cambridge, UK.
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11
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Fukunaga T, Iwasaki W. Inverse Potts model improves accuracy of phylogenetic profiling. Bioinformatics 2022; 38:1794-1800. [PMID: 35060594 PMCID: PMC8963296 DOI: 10.1093/bioinformatics/btac034] [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: 10/18/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias. RESULTS To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https://github.com/fukunagatsu/Ipm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Wataru Iwasaki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 2770882, Japan,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 1130032, Japan,Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 2770882, Japan,Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 2770882, Japan,Institute for Quantitative Biosciences, The University of Tokyo, Tokyo 1130032, Japan,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 1130032, Japan
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12
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Chewapreecha C, Pensar J, Chattagul S, Pesonen M, Sangphukieo A, Boonklang P, Potisap C, Koosakulnirand S, Feil EJ, Dunachie S, Chantratita N, Limmathurotsakul D, Peacock SJ, Day NPJ, Parkhill J, Thomson NR, Sermswan RW, Corander J. Co-evolutionary Signals Identify Burkholderia pseudomallei Survival Strategies in a Hostile Environment. Mol Biol Evol 2022; 39:6400259. [PMID: 34662416 PMCID: PMC8760936 DOI: 10.1093/molbev/msab306] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis and a significant cause of human morbidity and mortality in many tropical and subtropical countries. The species notoriously survives harsh environmental conditions but the genetic architecture for these adaptations remains unclear. Here we employed a powerful combination of genome-wide epistasis and co-selection studies (2,011 genomes), condition-wide transcriptome analyses (82 diverse conditions), and a gene knockout assay to uncover signals of "co-selection"-that is a combination of genetic markers that have been repeatedly selected together through B. pseudomallei evolution. These enabled us to identify 13,061 mutation pairs under co-selection in distinct genes and noncoding RNA. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions, highlighting the conditions as one of the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed a BPSL1661 role under nutrient deprivation, and explored the functional basis of co-selection gene network surrounding BPSL1661 in facilitating the bacterial survival under nutrient depletion. Our findings suggest that nutrient-limited conditions have been the common selection pressure acting on this species, and allelic variation of BPSL1661 may have promoted B. pseudomallei survival during harsh environmental conditions by facilitating bacterial adherence to different surfaces, cells, or living hosts.
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Affiliation(s)
- Claire Chewapreecha
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Parasites and Microbes Programme, Wellcome Sanger Insitute, Hinxton, United Kingdom
- Bioinformatics & Systems Biology Program, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
- Corresponding authors: E-mails: ; ;
| | - Johan Pensar
- Department of Mathematics, University of Oslo, Oslo, Norway
- Department of Mathematics and Statistics, Helsinki Institute of Information Technology, University of Helsinki, Helsinki, Finland
| | - Supaksorn Chattagul
- Melioidosis Research Center, Khon Kaen University, Khon Kaen, Thailand
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Maiju Pesonen
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Apiwat Sangphukieo
- Bioinformatics & Systems Biology Program, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
| | - Phumrapee Boonklang
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Chotima Potisap
- Melioidosis Research Center, Khon Kaen University, Khon Kaen, Thailand
| | - Sirikamon Koosakulnirand
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Edward J Feil
- Department of Biology and Biochemistry, The Milner Centre for Evolution, University of Bath, Bath, United Kingdom
| | - Susanna Dunachie
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Narisara Chantratita
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Direk Limmathurotsakul
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nick P J Day
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas R Thomson
- Parasites and Microbes Programme, Wellcome Sanger Insitute, Hinxton, United Kingdom
| | - Rasana W Sermswan
- Melioidosis Research Center, Khon Kaen University, Khon Kaen, Thailand
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Corresponding authors: E-mails: ; ;
| | - Jukka Corander
- Parasites and Microbes Programme, Wellcome Sanger Insitute, Hinxton, United Kingdom
- Department of Mathematics and Statistics, Helsinki Institute of Information Technology, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Corresponding authors: E-mails: ; ;
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13
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Exact simulation of coupled Wright–Fisher diffusions. ADV APPL PROBAB 2021. [DOI: 10.1017/apr.2021.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractIn this paper an exact rejection algorithm for simulating paths of the coupled Wright–Fisher diffusion is introduced. The coupled Wright–Fisher diffusion is a family of multivariate Wright–Fisher diffusions that have drifts depending on each other through a coupling term and that find applications in the study of networks of interacting genes. The proposed rejection algorithm uses independent neutral Wright–Fisher diffusions as candidate proposals, which are only needed at a finite number of points. Once a candidate is accepted, the remainder of the path can be recovered by sampling from neutral multivariate Wright–Fisher bridges, for which an exact sampling strategy is also provided. Finally, the algorithm’s complexity is derived and its performance demonstrated in a simulation study.
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14
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Su M, Davis MH, Peterson J, Solis-Lemus C, Satola SW, Read TD. Effect of genetic background on the evolution of Vancomycin-Intermediate Staphylococcus aureus (VISA). PeerJ 2021; 9:e11764. [PMID: 34306830 PMCID: PMC8284308 DOI: 10.7717/peerj.11764] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/22/2021] [Indexed: 11/20/2022] Open
Abstract
Vancomycin-intermediate Staphylococcus aureus (VISA) typically arises through accumulation of chromosomal mutations that alter cell-wall thickness and global regulatory pathways. Genome-based prediction of VISA requires understanding whether strain background influences patterns of mutation that lead to resistance. We used an iterative method to experimentally evolve three important methicillin-resistant S. aureus (MRSA) strain backgrounds-(CC1, CC5 and CC8 (USA300)) to generate a library of 120 laboratory selected VISA isolates. At the endpoint, isolates had vancomycin MICs ranging from 4 to 10 μg/mL. We detected mutations in more than 150 genes, but only six genes (already known to be associated with VISA from prior studies) were mutated in all three background strains (walK, prs, rpoB, rpoC, vraS, yvqF). We found evidence of interactions between loci (e.g., vraS and yvqF mutants were significantly negatively correlated) and rpoB, rpoC, vraS and yvqF were more frequently mutated in one of the backgrounds. Increasing vancomycin resistance was correlated with lower maximal growth rates (a proxy for fitness) regardless of background. However, CC5 VISA isolates had higher MICs with fewer rounds of selection and had lower fitness costs than the CC8 VISA isolates. Using multivariable regression, we found that genes differed in their contribution to overall MIC depending on the background. Overall, these results demonstrated that VISA evolved through mutations in a similar set of loci in all backgrounds, but the effect of mutation in common genes differed with regard to fitness and contribution to resistance in different strains.
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Affiliation(s)
- Michelle Su
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Michelle H Davis
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Jessica Peterson
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Claudia Solis-Lemus
- Wisconsin Institute for Discovery and Department of Plant Pathology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Sarah W Satola
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Timothy D Read
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.,Department of Dermatology, School of Medicine, Emory University, Atlanta, Georgia, USA
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15
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Allen JP, Snitkin E, Pincus NB, Hauser AR. Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning. Trends Microbiol 2021; 29:621-633. [PMID: 33455849 PMCID: PMC8187264 DOI: 10.1016/j.tim.2020.12.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022]
Abstract
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.
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Affiliation(s)
- Jonathan P Allen
- Department of Microbiology and Immunology, Loyola University Chicago Stritch School of Medicine, Maywood, IL 60153, USA.
| | - Evan Snitkin
- Department of Microbiology and Immunology, Department of Internal Medicine/Division of Infectious Diseases, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nathan B Pincus
- Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Alan R Hauser
- Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Department of Medicine/Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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16
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Singh R, Kusalik A, Dillon JAR. Bioinformatics tools used for whole-genome sequencing analysis of Neisseria gonorrhoeae: a literature review. Brief Funct Genomics 2021; 21:78-89. [PMID: 34170311 DOI: 10.1093/bfgp/elab028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/02/2023] Open
Abstract
Whole-genome sequencing (WGS) data are well established for the investigation of gonococcal transmission, antimicrobial resistance prediction, population structure determination and population dynamics. A variety of bioinformatics tools, repositories, services and platforms have been applied to manage and analyze Neisseria gonorrhoeae WGS datasets. This review provides an overview of the various bioinformatics approaches and resources used in 105 published studies (as of 30 April 2021). The challenges in the analysis of N. gonorrhoeae WGS datasets, as well as future bioinformatics requirements, are also discussed.
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Affiliation(s)
- Reema Singh
- Department of Biochemistry, Microbiology and Immunology
| | - Anthony Kusalik
- Department of Computer Science at the University of Saskatchewan
| | - Jo-Anne R Dillon
- Department of Biochemistry Microbiology and Immunology, College of Medicine, c/o Vaccine and Infectious Disease Organization, University of Saskatchewan, 120 Veterinary Road, Saskatoon, Saskatchewan S7N5E3, Canada
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17
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Abstract
Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.
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18
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Magee AF, Hilton SK, DeWitt WS. Robustness of phylogenetic inference to model misspecification caused by pairwise epistasis. Mol Biol Evol 2021; 38:4603-4615. [PMID: 34043795 PMCID: PMC8476159 DOI: 10.1093/molbev/msab163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pairwise epistasis and can be used in posterior predictive checks.
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Affiliation(s)
- Andrew F Magee
- Departments of Biology.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Sarah K Hilton
- Departments of Genome Sciences, University of Washington, Seattle, USA.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - William S DeWitt
- Departments of Genome Sciences, University of Washington, Seattle, USA.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA
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19
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Zeng HL, Aurell E. Inferring genetic fitness from genomic data. Phys Rev E 2021; 101:052409. [PMID: 32575265 DOI: 10.1103/physreve.101.052409] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 05/04/2020] [Indexed: 11/07/2022]
Abstract
The genetic composition of a naturally developing population is considered as due to mutation, selection, genetic drift, and recombination. Selection is modeled as single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness). The problem is posed to infer epistatic fitness from population-wide whole-genome data from a time series of a developing population. We generate such data in silico and show that in the quasilinkage equilibrium phase of Kimura, Neher, and Shraiman, which pertains at high enough recombination rates and low enough mutation rates, epistatic fitness can be quantitatively correctly inferred using inverse Ising-Potts methods.
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Affiliation(s)
- Hong-Li Zeng
- School of Science, and New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.,Nordita, Royal Institute of Technology, and Stockholm University, SE-10691 Stockholm, Sweden
| | - Erik Aurell
- KTH-Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden.,Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-348 Kraków, Poland
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20
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Zeng HL, Dichio V, Rodríguez Horta E, Thorell K, Aurell E. Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes. Proc Natl Acad Sci U S A 2020; 117:31519-31526. [PMID: 33203681 PMCID: PMC7733830 DOI: 10.1073/pnas.2012331117] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Genome-wide epistasis analysis is a powerful tool to infer gene interactions, which can guide drug and vaccine development and lead to deeper understanding of microbial pathogenesis. We have considered all complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes deposited in the Global Initiative on Sharing All Influenza Data (GISAID) repository until four different cutoff dates, and used direct coupling analysis together with an assumption of quasi-linkage equilibrium to infer epistatic contributions to fitness from polymorphic loci. We find eight interactions, of which three are between pairs where one locus lies in gene ORF3a, both loci holding nonsynonymous mutations. We also find interactions between two loci in gene nsp13, both holding nonsynonymous mutations, and four interactions involving one locus holding a synonymous mutation. Altogether, we infer interactions between loci in viral genes ORF3a and nsp2, nsp12, and nsp6, between ORF8 and nsp4, and between loci in genes nsp2, nsp13, and nsp14. The paper opens the prospect to use prominent epistatically linked pairs as a starting point to search for combinatorial weaknesses of recombinant viral pathogens.
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Affiliation(s)
- Hong-Li Zeng
- New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
- Nordic Institute for Theoretical Physics, Royal Institute of Technology and Stockholm University, 10691 Stockholm, Sweden
| | - Vito Dichio
- Nordic Institute for Theoretical Physics, Royal Institute of Technology and Stockholm University, 10691 Stockholm, Sweden
- Department of Physics, University of Trieste, 34151 Trieste, Italy
- Department of Computational Science and Technology, AlbaNova University Center, 10691 Stockholm, Sweden
| | - Edwin Rodríguez Horta
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, 10400 Havana, Cuba
| | - Kaisa Thorell
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Center for Translational Microbiome Research, Department of Microbiology, Cell and Tumor Biology, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Erik Aurell
- Department of Computational Science and Technology, AlbaNova University Center, 10691 Stockholm, Sweden;
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21
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Kafaie S, Xu L, Hu T. Statistical methods with exhaustive search in the identification of gene-gene interactions for colorectal cancer. Genet Epidemiol 2020; 45:222-234. [PMID: 33231893 DOI: 10.1002/gepi.22372] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/10/2020] [Accepted: 11/09/2020] [Indexed: 12/16/2022]
Abstract
Though additive forms of heritability are primarily studied in genetics, nonlinear, non-additive gene-gene interactions, that is, epistasis, could explain a portion of the missing heritability in complex human diseases including cancer. In recent years, powerful computational methods have been introduced to understand multivariable genetic factors of these complex human diseases in extremely high-dimensional genome-wide data. In this study, we investigated the performance of three powerful methods, BOolean Operation-based Screening and Testing (BOOST), FastEpistasis, and Tree-based Epistasis Association Mapping (TEAM) to identify interacting genetic risk factors of colorectal cancer (CRC) for genome-wide association studies (GWAS). After quality-control based data preprocessing, we applied these three algorithms to a CRC GWAS data set, and selected the top-ranked 100 single-nucleotide polymorphism (SNP) pairs identified by each method (251 SNPs in total), among which 74 pairs were common between FastEpistasis and BOOST. The identified SNPs by BOOST, FastEpistasis, and TEAM mapped to 58, 57, and 62 genes, respectively. Some genes highlighted by our study, including MACF1, USP49, SMAD2, SMAD3, TGFBR1, and RHOA, have been detected in previous CRC-related research. We also identified some new genes with potential biological relevance to CRC such as CCDC32. Furthermore, we constructed the network of these top SNP pairs for three methods, and the patterns identified in the networks show that some SNPs including rs2412531, rs349699, and rs17142011 play a crucial role in the classification of disease status in our study.
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Affiliation(s)
- Somayeh Kafaie
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada
| | - Ling Xu
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada
| | - Ting Hu
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada.,School of Computing, Queen's University, Kingston, Ontario, Canada
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22
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Li X, Lin J, Hu Y, Zhou J. PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance. Front Microbiol 2020; 11:578795. [PMID: 33193203 PMCID: PMC7642336 DOI: 10.3389/fmicb.2020.578795] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 11/17/2022] Open
Abstract
Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597 Neisseria gonorrhoeae strains, it successfully predicted their AMR phenotypes based on a pan-genome analysis. Furthermore, it identified 328 genetic alterations in 23 known AMR genes and discovered many new AMR-associated genetic alterations in ciprofloxacin-resistant N. gonorrhoeae, and it clearly indicated the genetic heterogeneity of AMR genes in different subtypes of resistant N. gonorrhoeae. Additionally, PARMAP performed well in predicting the AMR phenotypes of Mycobacterium tuberculosis and Escherichia coli, indicating the robustness of the PARMAP framework. In conclusion, PARMAP not only precisely predicts the AMR of a population of strains of a given species but also uses whole-genome sequencing data to prioritize candidate AMR-associated genetic alterations based on their likelihood of contributing to AMR. Thus, we believe that PARMAP will accelerate investigations into AMR mechanisms in other human pathogens.
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Affiliation(s)
- Xuefei Li
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Jingxia Lin
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Yongfei Hu
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Jiajian Zhou
- Dermatology Hospital, Southern Medical University, Guangzhou, China
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23
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Lees JA, Mai TT, Galardini M, Wheeler NE, Horsfield ST, Parkhill J, Corander J. Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions. mBio 2020; 11:e01344-20. [PMID: 32636251 PMCID: PMC7343994 DOI: 10.1128/mbio.01344-20] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/05/2020] [Indexed: 12/19/2022] Open
Abstract
Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially.IMPORTANCE Being able to identify the genetic variants responsible for specific bacterial phenotypes has been the goal of bacterial genetics since its inception and is fundamental to our current level of understanding of bacteria. This identification has been based primarily on painstaking experimentation, but the availability of large data sets of whole genomes with associated phenotype metadata promises to revolutionize this approach, not least for important clinical phenotypes that are not amenable to laboratory analysis. These models of phenotype-genotype association can in the future be used for rapid prediction of clinically important phenotypes such as antibiotic resistance and virulence by rapid-turnaround or point-of-care tests. However, despite much effort being put into adapting genome-wide association study (GWAS) approaches to cope with bacterium-specific problems, such as strong population structure and horizontal gene exchange, current approaches are not yet optimal. We describe a method that advances methodology for both association and generation of portable prediction models.
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Affiliation(s)
- John A Lees
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - T Tien Mai
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
| | - Marco Galardini
- Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Nicole E Wheeler
- Centre for Genomic Pathogen Surveillance, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Samuel T Horsfield
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jukka Corander
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
- Centre for Genomic Pathogen Surveillance, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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24
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Dench J, Hinz A, Aris‐Brosou S, Kassen R. Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection. Evol Appl 2020; 13:781-793. [PMID: 32211067 PMCID: PMC7086105 DOI: 10.1111/eva.12900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 10/02/2019] [Accepted: 11/14/2019] [Indexed: 11/29/2022] Open
Abstract
The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole-genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high-throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.
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Affiliation(s)
- Jonathan Dench
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
| | - Aaron Hinz
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
| | - Stéphane Aris‐Brosou
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
- Department of Mathematics and StatisticsUniversity of OttawaOttawaOntarioCanada
| | - Rees Kassen
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
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25
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Arnold B, Sohail M, Wadsworth C, Corander J, Hanage WP, Sunyaev S, Grad YH. Fine-Scale Haplotype Structure Reveals Strong Signatures of Positive Selection in a Recombining Bacterial Pathogen. Mol Biol Evol 2020; 37:417-428. [PMID: 31589312 PMCID: PMC6993868 DOI: 10.1093/molbev/msz225] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Identifying genetic variation in bacteria that has been shaped by ecological differences remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show that derived alleles <300 bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles than synonymous alleles. This general pattern is driven by a small fraction of highly diverse genes, many of which exhibit evidence of interspecies horizontal gene transfer and an excess of intermediate frequency alleles. Extensive simulations show that two distinct forms of positive selection can create these patterns of genetic variation: directional selection on horizontally transferred alleles or balancing selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.
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Affiliation(s)
- Brian Arnold
- Division of Informatics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA
- Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Mashaal Sohail
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Crista Wadsworth
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Jukka Corander
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - William P Hanage
- Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Shamil Sunyaev
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Yonatan H Grad
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA
- Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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26
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Pensar J, Puranen S, Arnold B, MacAlasdair N, Kuronen J, Tonkin-Hill G, Pesonen M, Xu Y, Sipola A, Sánchez-Busó L, Lees JA, Chewapreecha C, Bentley SD, Harris SR, Parkhill J, Croucher NJ, Corander J. Genome-wide epistasis and co-selection study using mutual information. Nucleic Acids Res 2019; 47:e112. [PMID: 31361894 PMCID: PMC6765119 DOI: 10.1093/nar/gkz656] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 07/09/2019] [Accepted: 07/19/2019] [Indexed: 01/19/2023] Open
Abstract
Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.
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Affiliation(s)
- Johan Pensar
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland
| | - Santeri Puranen
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland.,Department of Computer Science, Aalto University, Espoo, FI-00014, Finland
| | - Brian Arnold
- Division of Informatics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Neil MacAlasdair
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Juri Kuronen
- Department of Biostatistics, University of Oslo, Oslo, 0317, Norway
| | - Gerry Tonkin-Hill
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Maiju Pesonen
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland.,Department of Computer Science, Aalto University, Espoo, FI-00014, Finland
| | - Yingying Xu
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland.,Department of Computer Science, Aalto University, Espoo, FI-00014, Finland
| | - Aleksi Sipola
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland
| | | | - John A Lees
- Department of Microbiology, New York University School of Medicine, New York, NY 10016, USA
| | - Claire Chewapreecha
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK.,Bioinformatics & Systems Biology program, King Mongkut's University of Technology Thonburi, Bangkok 10150, Thailand
| | - Stephen D Bentley
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Simon R Harris
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, CB3 0ES, UK
| | - Nicholas J Croucher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, St. Mary's Campus, Imperial College London, London, W2 1PG, UK
| | - Jukka Corander
- Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland.,Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK.,Department of Biostatistics, University of Oslo, Oslo, 0317, Norway
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27
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Modulation effect of vaginal mucosal microflora and susceptibility to Neisseria gonorrhoeae infections: a systematic review and meta-analysis. Arch Gynecol Obstet 2019; 300:261-267. [PMID: 31175401 DOI: 10.1007/s00404-019-05200-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 05/21/2019] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The vaginal microbiota may modulate susceptibility to Neisseria gonorrhea (NG) infections. The objective of this meta-analysis was to evaluate the association between these NG infections and the vaginal microbiota. METHOD A systematic review and meta-analysis was conducted to investigate the correlation of vaginal microbiota and NG risk. Primary sources of the reviewed studies were from inception through December 2018. Vaginal mucosa microflora were dichotomized into high-Lactobacillus vaginal microbiota and low-Lactobacillus vaginal microbiota (LL-VMB), using either Nugent score, Amsel's criteria, presence of clue cells or 16S rRNA gene sequencing. RESULTS A total of 8 studies qualified for inclusion in this meta-analysis. LL-VMB could be regarded as worse prognostic factor, and the pooled OR was 1.33 (95% CI 1.02, 1.73; P = 0.04, I2 = 44%). LL-VMB was associated with a significantly higher susceptibility of NG. Trend for the sensitive analysis was consistence with the primary outcome. Significant publication bias was not detected by the funnel plot. CONCLUSION In conclusion, the systematic review and meta-analysis has demonstrated that LL-VMB was significantly associated with a high NG susceptibility.
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28
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Lees JA, Ferwerda B, Kremer PHC, Wheeler NE, Serón MV, Croucher NJ, Gladstone RA, Bootsma HJ, Rots NY, Wijmega-Monsuur AJ, Sanders EAM, Trzciński K, Wyllie AL, Zwinderman AH, van den Berg LH, van Rheenen W, Veldink JH, Harboe ZB, Lundbo LF, de Groot LCPGM, van Schoor NM, van der Velde N, Ängquist LH, Sørensen TIA, Nohr EA, Mentzer AJ, Mills TC, Knight JC, du Plessis M, Nzenze S, Weiser JN, Parkhill J, Madhi S, Benfield T, von Gottberg A, van der Ende A, Brouwer MC, Barrett JC, Bentley SD, van de Beek D. Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis. Nat Commun 2019; 10:2176. [PMID: 31092817 PMCID: PMC6520353 DOI: 10.1038/s41467-019-09976-3] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 04/11/2019] [Indexed: 12/21/2022] Open
Abstract
Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, identifying variants in CCDC33 associated with susceptibility. Pneumococcal genetic variation explains a large amount of invasive potential (70%), but has no effect on severity. Serotype alone is insufficient to explain invasiveness, suggesting other pneumococcal factors are involved in progression to invasive disease. We identify pneumococcal genes involved in invasiveness including pspC and zmpD, and perform a human-bacteria interaction analysis. These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines.
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Affiliation(s)
- John A Lees
- Department of Microbiology, New York University School of Medicine, New York, NY, 10016, USA
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Bart Ferwerda
- Amsterdam UMC, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Philip H C Kremer
- Amsterdam UMC, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Nicole E Wheeler
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
- The Centre for Genomic Pathogen Surveillance, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Mercedes Valls Serón
- Amsterdam UMC, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Nicholas J Croucher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | | | - Hester J Bootsma
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, 3721 MA, The Netherlands
| | - Nynke Y Rots
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, 3721 MA, The Netherlands
| | - Alienke J Wijmega-Monsuur
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, 3721 MA, The Netherlands
| | - Elisabeth A M Sanders
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, 3721 MA, The Netherlands
- Department of Pediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht, 3508 AB, The Netherlands
| | - Krzysztof Trzciński
- Department of Pediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht, 3508 AB, The Netherlands
| | - Anne L Wyllie
- Department of Pediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht, 3508 AB, The Netherlands
- Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Aeilko H Zwinderman
- Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands
| | - Wouter van Rheenen
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands
| | - Zitta B Harboe
- Department of Microbiological Surveillance and Research, Statens Serum Institut, Copenhagen, DK-2300, Denmark
| | - Lene F Lundbo
- Department of Infectious Diseases, Hvidovre Hospital, University of Copenhagen, Hvidovre, 2650, Denmark
| | - Lisette C P G M de Groot
- Department of Human Nutrition, Wageningen University, P.O. Box 17, 6700 AA, Wageningen, The Netherlands
| | - Natasja M van Schoor
- Amsterdam UMC, VU University, Department of Epidemiology and Biostatistics, Amsterdam Public Health, Van der Boechorststraat 7, Amsterdam, 1007 MB, The Netherlands
| | - Nathalie van der Velde
- Amsterdam UMC, University of Amsterdam, Department of Internal Medicine, Geriatrics, Amsterdam Public Health, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Centre Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Lars H Ängquist
- Center for Clinical Research and Disease Prevention, Bispebjerg and Frederiksberg Hospitals, The Capital Region, Copenhagen, DK-2000, Denmark
| | - Thorkild I A Sørensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Copenhagen, DK-2200, Denmark
- The Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-1014, Denmark
| | - Ellen A Nohr
- Institute of Clinical Research, University of Southern Denmark, Odense, DK-5000, Denmark
| | - Alexander J Mentzer
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Tara C Mills
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Mignon du Plessis
- School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, 2000, South Africa
| | - Susan Nzenze
- School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, 2000, South Africa
| | - Jeffrey N Weiser
- Department of Microbiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Julian Parkhill
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Shabir Madhi
- National Institute for Communicable Diseases, Johannesburg, 2192, South Africa
| | - Thomas Benfield
- Department of Infectious Diseases, Hvidovre Hospital, University of Copenhagen, Hvidovre, 2650, Denmark
| | - Anne von Gottberg
- School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, 2000, South Africa
- National Institute for Communicable Diseases, Johannesburg, 2192, South Africa
| | - Arie van der Ende
- Amsterdam UMC, University of Amsterdam, Department of Medical Microbiology, Amsterdam Infection and Immunity, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
- Netherlands Reference Laboratory for Bacterial Meningitis, Amsterdam UMC/RIVM, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Matthijs C Brouwer
- Amsterdam UMC, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Jeffrey C Barrett
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
- Genomics Plc, East Road, Cambridge, CB1 1BH, UK
| | - Stephen D Bentley
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK.
| | - Diederik van de Beek
- Amsterdam UMC, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands.
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29
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Savel D, Koyutürk M. Characterizing human genomic coevolution in locus-gene regulatory interactions. BioData Min 2019; 12:8. [PMID: 30923571 PMCID: PMC6419833 DOI: 10.1186/s13040-019-0195-y] [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: 09/12/2018] [Accepted: 02/19/2019] [Indexed: 11/10/2022] Open
Abstract
Background Coevolution has been used to identify and predict interactions and functional relationships between proteins of many different organisms including humans. Current efforts in annotating the human genome increasingly show that non-coding DNA sequence has important functional and regulatory interactions. Furthermore, regulatory elements do not necessarily reside in close proximity of the coding region for their target genes. Results We characterize coevolution as it appears in locus-gene interactions in the human genome, focusing on expression Quantitative Trait - Locus (eQTL) interactions. Our results show that in these interactions the conservation status of the loci is predictive of the conservation status of their target genes. Furthermore, comparing the phylogenetic histories of intra-chromosomal pairs of loci and transcription start sites, we find that pairs that appear coevolved are enriched for cis-eQTL interactions. Exploring this property we found that coevolution might be useful in prioritizing association tests in cis-eQTL detection. Conclusions The relationship between the conservation status of pairs of loci and protein coding transcription start sites reveal correlations with regulatory interactions. Pairs that appear coevolved are enriched for intra-chromosomal regulatory interactions, thus our results suggest that measures of coevolution can be useful for prediction and detection of new interactions. Measures of coevolution are genome-wide and could potentially be used to prioritize the detection of distant or inter-chromosomal interactions such as trans-eQTL interactions in the human genome.
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Affiliation(s)
- Daniel Savel
- 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA
| | - Mehmet Koyutürk
- 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA.,2Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA
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30
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Gao CY, Cecconi F, Vulpiani A, Zhou HJ, Aurell E. DCA for genome-wide epistasis analysis: the statistical genetics perspective. Phys Biol 2019; 16:026002. [PMID: 30605896 DOI: 10.1088/1478-3975/aafbe0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.
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Affiliation(s)
- Chen-Yi Gao
- Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China. School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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