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Maddamsetti R, Grant NA. Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations. PLoS Genet 2022; 18:e1010324. [PMID: 35981004 PMCID: PMC9426924 DOI: 10.1371/journal.pgen.1010324] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/30/2022] [Accepted: 07/04/2022] [Indexed: 11/18/2022] Open
Abstract
A general method to infer both positive and purifying selection during the real-time evolution of hypermutator pathogens would be broadly useful. To this end, we introduce a Simple Test to Infer Mode of Selection (STIMS) from metagenomic time series of evolving microbial populations. We test STIMS on metagenomic data generated by simulations of bacterial evolution, and on metagenomic data spanning 62,750 generations of Lenski's long-term evolution experiment with Escherichia coli (LTEE). This benchmarking shows that STIMS detects positive selection in both nonmutator and hypermutator populations, and purifying selection in hypermutator populations. Using STIMS, we find strong evidence of ongoing positive selection on key regulators of the E. coli gene regulatory network, even in some hypermutator populations. STIMS also detects positive selection on regulatory genes in hypermutator populations of Pseudomonas aeruginosa that adapted to subinhibitory concentrations of colistin-an antibiotic of last resort-for just twenty-six days of laboratory evolution. Our results show that the fine-tuning of gene regulatory networks is a general mechanism for rapid and ongoing adaptation. The simplicity of STIMS, together with its intuitive visual interpretation, make it a useful test for positive and purifying selection in metagenomic data sets that track microbial evolution in real-time.
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Affiliation(s)
- Rohan Maddamsetti
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Nkrumah A. Grant
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
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2
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Chandran R, Singh A, Singh RK, Mandal S, Ganesan K, Sah P, Paul P, Pathak A, Dutta N, Sah R, Lal KK, Mohindra V. Phenotypic variation of Chitala chitala (Hamilton, 1822) from Indian rivers using truss network and geometric morphometrics. PeerJ 2022; 10:e13290. [PMID: 35462771 PMCID: PMC9022642 DOI: 10.7717/peerj.13290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/28/2022] [Indexed: 01/13/2023] Open
Abstract
Chitala chitala (Hamilton, 1822) is an economically important food fish species occurring throughout Indian rivers, which also has ornamental value. This study focuses on morphological variations in C. chitala from seven river basins across India namely; Son, Tons, Ken, Brahmaputra, Ganga, Gomti and Gandak. A truss network was constructed by interconnecting nine landmarks to generate 36 morphometric variables extracted from digital images of specimens sampled from the study locations. Transformed truss measurements were subjected to principal component analysis (PCA), canonical discriminant function analysis (CDFA) and discriminant analyses of principal components (DAPC). DAPC function coefficients performed much better in capturing the variation pattern and discrimination between the rivers which was not achieved using CDFA. Eight truss variables were identified with significant and highest loading for truss variables on principal components and coefficients on discriminant function from DAPC contributing to maximum variation between the rivers. Performance graph and functional distribution of identified truss variables clearly indicated distinction between the rivers. Thin plate spline analysis and procrustes shape analysis further showed the variation in morphology between specimens across the rivers. The significant parameters differentiating specimens from different rivers were linked to dorsal fin origin, the base of the pectoral fin and the perpendicular point on the anal fin from the dorsal fin origin. Variation in the hydrodynamics of the rivers studied might be possibly affecting the fin kinematics and consequently leading to adaption seen as phenotypic variation in C. chitala. The results showcased in the present study shall help in better understanding of intra-specific diversity which is significant for management and conservation of a species.
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Affiliation(s)
- Rejani Chandran
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Achal Singh
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Rajeev K. Singh
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Sangeeta Mandal
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Kantharajan Ganesan
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Priyanka Sah
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Pradipta Paul
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India,Department of Fisheries, Bankura, West Bengal, India
| | - Abhinav Pathak
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India,Molecular Biological Sciences, Farelabs Private Limited, Gurugram, India
| | - Nimisha Dutta
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India,Molecular Biological Sciences, Farelabs Private Limited, Gurugram, India
| | - Ramashankar Sah
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Kuldeep K. Lal
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
| | - Vindhya Mohindra
- Fish Conservation Division, National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India
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Huang JH, Liao YR, Lin TC, Tsai CH, Lai WY, Chou YK, Leu JY, Tsai HK, Kao CF. iTARGEX analysis of yeast deletome reveals novel regulators of transcriptional buffering in S phase and protein turnover. Nucleic Acids Res 2021; 49:7318-7329. [PMID: 34197604 PMCID: PMC8287957 DOI: 10.1093/nar/gkab555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 05/12/2021] [Accepted: 06/29/2021] [Indexed: 11/24/2022] Open
Abstract
Integrating omics data with quantification of biological traits provides unparalleled opportunities for discovery of genetic regulators by in silico inference. However, current approaches to analyze genetic-perturbation screens are limited by their reliance on annotation libraries for prioritization of hits and subsequent targeted experimentation. Here, we present iTARGEX (identification of Trait-Associated Regulatory Genes via mixture regression using EXpectation maximization), an association framework with no requirement of a priori knowledge of gene function. After creating this tool, we used it to test associations between gene expression profiles and two biological traits in single-gene deletion budding yeast mutants, including transcription homeostasis during S phase and global protein turnover. For each trait, we discovered novel regulators without prior functional annotations. The functional effects of the novel candidates were then validated experimentally, providing solid evidence for their roles in the respective traits. Hence, we conclude that iTARGEX can reliably identify novel factors involved in given biological traits. As such, it is capable of converting genome-wide observations into causal gene function predictions. Further application of iTARGEX in other contexts is expected to facilitate the discovery of new regulators and provide observations for novel mechanistic hypotheses regarding different biological traits and phenotypes.
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Affiliation(s)
- Jia-Hsin Huang
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - You-Rou Liao
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 115, Taiwan
| | - Tzu-Chieh Lin
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Cheng-Hung Tsai
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Wei-Yun Lai
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Yang-Kai Chou
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Huai-Kuang Tsai
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Cheng-Fu Kao
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 115, Taiwan
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Liu L, Wang Y, Zhang D, Chen Z, Chen X, Su Z, He X. The Origin of Additive Genetic Variance Driven by Positive Selection. Mol Biol Evol 2021; 37:2300-2308. [PMID: 32243529 PMCID: PMC7403624 DOI: 10.1093/molbev/msaa085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Fisher's fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability (h2) for traits important to fitness. However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity (e.g., humans), the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher's fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.
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Affiliation(s)
- Li Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Yayu Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Di Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhuoxin Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoshu Chen
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Zhijian Su
- Department of Cell Biology, Jinan University, Guangzhou, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
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Chen H, Liang H. A High-Resolution Map of Human Enhancer RNA Loci Characterizes Super-enhancer Activities in Cancer. Cancer Cell 2020; 38:701-715.e5. [PMID: 33007258 PMCID: PMC7658066 DOI: 10.1016/j.ccell.2020.08.020] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 07/21/2020] [Accepted: 08/28/2020] [Indexed: 12/20/2022]
Abstract
Although enhancers play critical roles in cancer, quantifying enhancer activities in clinical samples remains challenging, especially for super-enhancers. Enhancer activities can be inferred from enhancer RNA (eRNA) signals, which requires enhancer transcription loci definition. Only a small proportion of human eRNA loci has been precisely identified, limiting investigations of enhancer-mediated oncogenic mechanisms. Here, we characterize super-enhancer regions using aggregated RNA sequencing (RNA-seq) data from large cohorts. Super-enhancers usually contain discrete loci featuring sharp eRNA expression peaks. We identify >300,000 eRNA loci in ∼377 Mb super-enhancer regions that are regulated by evolutionarily conserved, well-positioned nucleosomes and are frequently dysregulated in cancer. The eRNAs provide explanatory power for cancer phenotypes beyond that provided by mRNA expression through resolving intratumoral heterogeneity with enhancer cell-type specificity. Our study provides a high-resolution map of eRNA loci through which super-enhancer activities can be quantified by RNA-seq and a user-friendly data portal, enabling a broad range of biomedical investigations.
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Affiliation(s)
- Han Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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Liu L, Liu M, Zhang D, Deng S, Chen P, Yang J, Xie Y, He X. Decoupling gene functions from knockout effects by evolutionary analyses. Natl Sci Rev 2020; 7:1169-1180. [PMID: 34692141 PMCID: PMC8288921 DOI: 10.1093/nsr/nwaa079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 03/19/2020] [Accepted: 04/22/2020] [Indexed: 11/14/2022] Open
Abstract
Genic functions have long been confounded by pleiotropic mutational effects. To understand such genetic effects, we examine HAP4, a well-studied transcription factor in Saccharomyces cerevisiae that functions by forming a tetramer with HAP2, HAP3 and HAP5. Deletion of HAP4 results in highly pleiotropic gene expression responses, some of which are clustered in related cellular processes (clustered effects) while most are distributed randomly across diverse cellular processes (distributed effects). Strikingly, the distributed effects that account for much of HAP4 pleiotropy tend to be non-heritable in a population, suggesting they have few evolutionary consequences. Indeed, these effects are poorly conserved in closely related yeasts. We further show substantial overlaps of clustered effects, but not distributed effects, among the four genes encoding the HAP2/3/4/5 tetramer. This pattern holds for other biochemically characterized yeast protein complexes or metabolic pathways. Examination of a set of cell morphological traits of the deletion lines yields consistent results. Hence, only some deletion effects of a gene support related biochemical understandings with the rest being often pleiotropic and evolutionarily decoupled from the gene's normal functions. This study suggests a new framework for reverse genetic analysis.
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Affiliation(s)
- Li Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Mengdi Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Di Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Shanjun Deng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Piaopiao Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Jing Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Yunhan Xie
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Xionglei He
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
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7
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Fast-Evolving Human-Specific Neural Enhancers Are Associated with Aging-Related Diseases. Cell Syst 2019; 6:604-611.e4. [PMID: 29792826 DOI: 10.1016/j.cels.2018.04.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 01/25/2018] [Accepted: 04/05/2018] [Indexed: 01/22/2023]
Abstract
The antagonistic pleiotropy theory hypothesizes that evolutionary adaptations maximizing the fitness in early age increase disease burden after reproduction. This theory remains largely untested at the molecular level. Here, we analyzed enhancer evolution in primates to investigate the relationships between aging-related diseases and enhancers acquired after the human-chimpanzee divergence. We report a 5-fold increased evolutionary rate of enhancers that are activated in neural tissues, leading to fixation of ∼100 human-specific enhancers potentially under adaptation. These enhancers show prognostic expression levels and correlations with driver genes in cancer, and their nearby genes are enriched in known loci associated with aging-related diseases. Using CRISPR/Cas9, we further functionally validated an enhancer on chr8p23.1 as activator counteracting REST, a master regulator known to be a transcriptional suppressor of Alzheimer disease. Our results suggest an evolutionary origin of aging-related diseases: the side effects of human-specific, neural-tissue expressed enhancers. Thus, adaptive molecular changes in human macroevolution may introduce vulnerabilities to disease development in modern populations.
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Liu X, Li YI, Pritchard JK. Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell 2019; 177:1022-1034.e6. [PMID: 31051098 PMCID: PMC6553491 DOI: 10.1016/j.cell.2019.04.014] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/18/2018] [Accepted: 04/07/2019] [Indexed: 01/02/2023]
Abstract
Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits.
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Affiliation(s)
- Xuanyao Liu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
| | - Jonathan K Pritchard
- Departments of Biology and Genetics and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
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Fragata I, Blanckaert A, Dias Louro MA, Liberles DA, Bank C. Evolution in the light of fitness landscape theory. Trends Ecol Evol 2019; 34:69-82. [DOI: 10.1016/j.tree.2018.10.009] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/16/2018] [Accepted: 10/17/2018] [Indexed: 01/28/2023]
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