1
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Olascoaga S, Tovar H, Espinal-Enríquez J. Gene co-expression networks reveal sex-biased differences in musculoskeletal ageing. FRONTIERS IN AGING 2024; 5:1469479. [PMID: 39359883 PMCID: PMC11445131 DOI: 10.3389/fragi.2024.1469479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024]
Abstract
Aging is a universal and progressive process involving the deterioration of physiological functions and the accumulation of cellular damage. Gene regulation programs influence how phenotypes respond to environmental and intrinsic changes during aging. Although several factors, including sex, are known to impact this process, the underlying mechanisms remain incompletely understood. Here, we investigate the functional organization patterns of skeletal muscle genes across different sexes and ages using gene co-expression networks (GCNs) to explore their influence on aging. We constructed GCNs for three different age groups for male and female samples, analyzed topological similarities and differences, inferred significant associated processes for each network, and constructed null models to provide statistically robust results. We found that each network is topologically and functionally distinct, with young women having the most associated processes, likely due to reproductive tasks. The functional organization and modularity of genes decline with age, starting from middle age, potentially leading to age-related deterioration. Women maintain better gene functional organization throughout life compared to men, especially in processes like macroautophagy and sarcomere organization. The study suggests that the loss of gene co-expression could be a universal aging marker. This research offers insights into how gene organization changes with age and sex, providing a complementary method to analyze aging.
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Affiliation(s)
- Samael Olascoaga
- Posgrado en Biología Experimental, División de Ciencias Biológicas y de la Salud, Universidad Autónoma Metropolitana Iztapalapa, Mexico City, Mexico
| | - Hugo Tovar
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
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2
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Chen M, Dahl A. A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data. Nat Commun 2024; 15:5229. [PMID: 38898015 PMCID: PMC11186839 DOI: 10.1038/s41467-024-49242-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.
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Affiliation(s)
- Minhui Chen
- Section of Genetic Medicine, University of Chicago, Chicago, IL, 60637, USA.
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, 60637, USA.
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3
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Gupta S, Groen SC, Zaidem ML, Sajise AGC, Calic I, Natividad MA, McNally KL, Vergara GV, Satija R, Franks SJ, Singh RK, Joly-Lopez Z, Purugganan MD. Systems genomics of salinity stress response in rice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596807. [PMID: 38895411 PMCID: PMC11185513 DOI: 10.1101/2024.05.31.596807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Populations can adapt to stressful environments through changes in gene expression. However, the role of gene regulation in mediating stress response and adaptation remains largely unexplored. Here, we use an integrative field dataset obtained from 780 plants of Oryza sativa ssp. indica (rice) grown in a field experiment under normal or moderate salt stress conditions to examine selection and evolution of gene expression variation under salinity stress conditions. We find that salinity stress induces increased selective pressure on gene expression. Further, we show that trans-eQTLs rather than cis-eQTLs are primarily associated with rice's gene expression under salinity stress, potentially via a few master-regulators. Importantly, and contrary to the expectations, we find that cis-trans reinforcement is more common than cis-trans compensation which may be reflective of rice diversification subsequent to domestication. We further identify genetic fixation as the likely mechanism underlying this compensation/reinforcement. Additionally, we show that cis- and trans-eQTLs are under different selection regimes, giving us insights into the evolutionary dynamics of gene expression variation. By examining genomic, transcriptomic, and phenotypic variation across a rice population, we gain insights into the molecular and genetic landscape underlying adaptive salinity stress responses, which is relevant for other crops and other stresses.
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Affiliation(s)
- Sonal Gupta
- Center for Genomics and Systems Biology, New York University, New York, NY USA
| | - Simon C Groen
- Center for Genomics and Systems Biology, New York University, New York, NY USA
- Department of Nematology and Department of Botany & Plant Sciences, University of California, Riverside, CA USA
- Center for Plant Cell Biology, Institute for Integrative Genome Biology, University of California, Riverside, CA USA
| | - Maricris L. Zaidem
- Center for Genomics and Systems Biology, New York University, New York, NY USA
- Department of Biology, University of Oxford, Oxford, England
| | | | - Irina Calic
- Department of Biological Sciences, Fordham University, Bronx, NY USA
- Inari Agriculture Nv, Gent, Belgium
| | | | | | - Georgina V. Vergara
- International Rice Research Institute, Los Baños, Philippines
- Institute of Crop Science, University of the Philippines, Los Baños, Philippines
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY USA
- New York Genome Center, New York, NY USA
| | - Steven J. Franks
- Department of Biological Sciences, Fordham University, Bronx, NY USA
| | - Rakesh K. Singh
- International Rice Research Institute, Los Baños, Philippines
- International Center for Biosaline Agriculture, Dubai, UAE (current affiliation)
| | - Zoé Joly-Lopez
- Center for Genomics and Systems Biology, New York University, New York, NY USA
- Département de Chimie, Université du Quebéc à Montréal, Montreal, Quebec, Canada
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4
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Boetto C, Frouin A, Henches L, Auvergne A, Suzuki Y, Patin E, Bredon M, Chiu A, Consortium MI, Sankararaman S, Zaitlen N, Kennedy SP, Quintana-Murci L, Duffy D, Sokol H, Aschard H. MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data. Brief Bioinform 2024; 25:bbae272. [PMID: 38856173 PMCID: PMC11163461 DOI: 10.1093/bib/bbae272] [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: 11/16/2023] [Revised: 04/16/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024] Open
Abstract
Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.
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Affiliation(s)
- Christophe Boetto
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Arthur Frouin
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Antoine Auvergne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Yuka Suzuki
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
| | - Marius Bredon
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
| | - Alec Chiu
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | | | - Sriram Sankararaman
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Noah Zaitlen
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Sean P Kennedy
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
- Chair of Human Genomics and Evolution, Collège de France, 11 Pl. Marcelin Berthelot, 75005 Paris, France
| | - Darragh Duffy
- Translational Immunology Unit, Institut Pasteur, Université de Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Harry Sokol
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
- Paris Center for Microbiome Medicine, Fédération Hospitalo-Universitaire, 184 rue du Faubourg Saint-Antoine, 75571 PARIS Cedex 12, France
- Gastroenterology Department, AP-HP, Saint Antoine Hospital, 184 rue du faubourg Saint-Antoine, 75012 Paris, France
- INRAE Micalis & AgroParisTech, UMR1319, Micalis & AgroParisTech, 4 avenue Jean Jaurès, 78352 Jouy en Josas, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
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5
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Med 2024; 16:62. [PMID: 38664839 PMCID: PMC11044415 DOI: 10.1186/s13073-024-01329-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).
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Affiliation(s)
- Andrew J Bass
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Aliza P Wingo
- Department of Psychiatry, Emory University, Atlanta, GA, 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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6
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George SHL, Medina-Rivera A, Idaghdour Y, Lappalainen T, Gallego Romero I. Increasing diversity of functional genetics studies to advance biological discovery and human health. Am J Hum Genet 2023; 110:1996-2002. [PMID: 37995684 PMCID: PMC10716434 DOI: 10.1016/j.ajhg.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/25/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
In this perspective we discuss the current lack of genetic and environmental diversity in functional genomics datasets. There is a well-described Eurocentric bias in genetic and functional genomic research that has a clear impact on the benefit this research can bring to underrepresented populations. Current research focused on genetic variant-to-function experiments aims to identify molecular QTLs, but the lack of data from genetically diverse individuals has limited analyses to mostly populations of European ancestry. Although some efforts have been established to increase diversity in functional genomic studies, much remains to be done to consistently generate data for underrepresented populations from now on. We discuss the major barriers for this continuity and suggest actionable insights, aiming to empower research and researchers from underserved populations.
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Affiliation(s)
- Sophia H L George
- Department of Obstetrics, Gynecology and Reproductive Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; Sylvester Comprehensive Cancer Center, Miami, FL, USA.
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación Sobre El Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Youssef Idaghdour
- Program in Biology, Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, UAE; Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, UAE; Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden; New York Genome Center, New York, NY, USA.
| | - Irene Gallego Romero
- Melbourne Integrative Genomics and School of BioSciences, University of Melbourne, Parkville, VIC, Australia; Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Tartu, Estonia
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7
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557155. [PMID: 37745553 PMCID: PMC10515795 DOI: 10.1101/2023.09.11.557155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.
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Affiliation(s)
- Andrew J. Bass
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Aliza P. Wingo
- Department of Psychiatry, Emory University, Atlanta, GA 30322, USA
| | - Thomas S. Wingo
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - David J. Cutler
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
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8
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Cai H, Des Marais DL. Revisiting regulatory coherence: accounting for temporal bias in plant gene co-expression analyses. THE NEW PHYTOLOGIST 2023; 238:16-24. [PMID: 36617750 DOI: 10.1111/nph.18720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Haoran Cai
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
| | - David L Des Marais
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
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9
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Chen M, Dahl A. A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.24.529987. [PMID: 36909553 PMCID: PMC10002707 DOI: 10.1101/2023.02.24.529987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
The development of single-cell RNA sequencing (scRNA-seq) offers opportunities to characterize cellular heterogeneity at unprecedented resolution. Although scRNA-seq has been widely used to identify and characterize gene expression variation across cell types and cell states based on their average gene expression profiles, most studies ignore variation across individual donors. Modelling this inter-individual variation could improve statistical power to detect cell type-specific biology and inform the genes and cell types that underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). CTMM operates on cell type-specific pseudobulk expression and is fit with efficient methods that scale to hundreds of samples. We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq data. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 61 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to characterize a novel dimension of cell type-specific biology in scRNA-seq.
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Affiliation(s)
- Minhui Chen
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
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10
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Wang ZY, McKenzie-Smith GC, Liu W, Cho HJ, Pereira T, Dhanerawala Z, Shaevitz JW, Kocher SD. Isolation disrupts social interactions and destabilizes brain development in bumblebees. Curr Biol 2022; 32:2754-2764.e5. [PMID: 35584698 PMCID: PMC9233014 DOI: 10.1016/j.cub.2022.04.066] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/28/2022] [Accepted: 04/22/2022] [Indexed: 12/24/2022]
Abstract
Social isolation, particularly in early life, leads to deleterious physiological and behavioral outcomes. Here, we leverage new high-throughput tools to comprehensively investigate the impact of isolation in the bumblebee, Bombus impatiens, from behavioral, molecular, and neuroanatomical perspectives. We reared newly emerged bumblebees in complete isolation, in small groups, or in their natal colony, and then analyzed their behaviors while alone or paired with another bee. We find that when alone, individuals of each rearing condition show distinct behavioral signatures. When paired with a conspecific, bees reared in small groups or in the natal colony express similar behavioral profiles. Isolated bees, however, showed increased social interactions. To identify the neurobiological correlates of these differences, we quantified brain gene expression and measured the volumes of key brain regions for a subset of individuals from each rearing condition. Overall, we find that isolation increases social interactions and disrupts gene expression and brain development. Limited social experience in small groups is sufficient to preserve typical patterns of brain development and social behavior.
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Affiliation(s)
- Z Yan Wang
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Lewis Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Grace C McKenzie-Smith
- Lewis Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Physics, Princeton University, Princeton, NJ, USA
| | - Weijie Liu
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Hyo Jin Cho
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Talmo Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zahra Dhanerawala
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua W Shaevitz
- Lewis Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Physics, Princeton University, Princeton, NJ, USA
| | - Sarah D Kocher
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Lewis Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
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11
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de Bivort B, Buchanan S, Skutt-Kakaria K, Gajda E, Ayroles J, O’Leary C, Reimers P, Akhund-Zade J, Senft R, Maloney R, Ho S, Werkhoven Z, Smith MAY. Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies. Front Behav Neurosci 2022; 16:836626. [PMID: 35692381 PMCID: PMC9178272 DOI: 10.3389/fnbeh.2022.836626] [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: 12/15/2021] [Accepted: 03/22/2022] [Indexed: 01/18/2023] Open
Abstract
Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.
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12
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Pu J, Yu H, Guo Y. A Novel Strategy to Identify Prognosis-Relevant Gene Sets in Cancers. Genes (Basel) 2022; 13:862. [PMID: 35627247 PMCID: PMC9141699 DOI: 10.3390/genes13050862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022] Open
Abstract
Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.
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Affiliation(s)
- Junyi Pu
- School of Life Sciences, Northwest University, Xi’an 710069, China;
| | - Hui Yu
- Comprehensive Cancer Center, New Mexico University, Albuquerque, NM 87131, USA;
| | - Yan Guo
- Comprehensive Cancer Center, New Mexico University, Albuquerque, NM 87131, USA;
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13
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Watowich MM, Chiou KL, Montague MJ, Simons ND, Horvath JE, Ruiz-Lambides AV, Martínez MI, Higham JP, Brent LJN, Platt ML, Snyder-Mackler N. Natural disaster and immunological aging in a nonhuman primate. Proc Natl Acad Sci U S A 2022; 119:e2121663119. [PMID: 35131902 PMCID: PMC8872742 DOI: 10.1073/pnas.2121663119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/20/2021] [Indexed: 12/23/2022] Open
Abstract
Weather-related disasters are increasing in frequency and severity, leaving survivors to cope with ensuing mental, financial, and physical hardships. This adversity can exacerbate existing morbidities, trigger new ones, and increase the risk of mortality-features that are also characteristic of advanced age-inviting the hypothesis that extreme weather events may accelerate aging. To test this idea, we examined the impact of Hurricane Maria and its aftermath on immune cell gene expression in large, age-matched, cross-sectional samples from free-ranging rhesus macaques (Macaca mulatta) living on an isolated island. A cross section of macaques was sampled 1 to 4 y before (n = 435) and 1 y after (n = 108) the hurricane. Hurricane Maria was significantly associated with differential expression of 4% of immune-cell-expressed genes, and these effects were correlated with age-associated alterations in gene expression. We further found that individuals exposed to the hurricane had a gene expression profile that was, on average, 1.96 y older than individuals that were not-roughly equivalent to an increase in 7 to 8 y of a human life. Living through an intense hurricane and its aftermath was associated with expression of key immune genes, dysregulated proteostasis networks, and greater expression of inflammatory immune cell-specific marker genes. Together, our findings illuminate potential mechanisms through which the adversity unleashed by extreme weather and potentially other natural disasters might become biologically embedded, accelerate age-related molecular immune phenotypes, and ultimately contribute to earlier onset of disease and death.
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Affiliation(s)
- Marina M Watowich
- Department of Biology, University of Washington, Seattle, WA 98195
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281
- School of Life Sciences, Arizona State University, Tempe, AZ 85281
| | - Kenneth L Chiou
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281
- School of Life Sciences, Arizona State University, Tempe, AZ 85281
| | - Michael J Montague
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Noah D Simons
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708
| | - Julie E Horvath
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC 27707
- Research and Collections Section, North Carolina Museum of Natural Sciences, Raleigh, NC 27601
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695
| | - Angelina V Ruiz-Lambides
- Caribbean Primate Research Center, Unit of Comparative Medicine, University of Puerto Rico, San Juan, PR 00936
| | - Melween I Martínez
- Caribbean Primate Research Center, Unit of Comparative Medicine, University of Puerto Rico, San Juan, PR 00936
| | - James P Higham
- Department of Anthropology, New York University, New York, NY 10003
- New York Consortium in Evolutionary Primatology, New York, NY 10016
| | - Lauren J N Brent
- Centre for Research in Animal Behaviour, University of Exeter, Exeter EX4 4QG, United Kingdom
| | - Michael L Platt
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Marketing Department, Wharton School of Business, University of Pennsylvania, Philadelphia, PA 19104
| | - Noah Snyder-Mackler
- Department of Biology, University of Washington, Seattle, WA 98195;
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281
- School of Life Sciences, Arizona State University, Tempe, AZ 85281
- School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85281
- Department of Psychology, University of Washington, Seattle, WA 98195
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14
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Abstract
The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more protein-coding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals.
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15
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Yu H, Wang L, Chen D, Li J, Guo Y. Conditional transcriptional relationships may serve as cancer prognostic markers. BMC Med Genomics 2021; 14:101. [PMID: 34856998 PMCID: PMC8638091 DOI: 10.1186/s12920-021-00958-3] [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] [Received: 03/28/2021] [Accepted: 04/08/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods. METHODS Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach. RESULTS By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types. CONCLUSIONS Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Limei Wang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Kaikou, Hainan, 571199, China.,College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang, China
| | - Danqian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Jin Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Kaikou, Hainan, 571199, China
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
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16
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Boughner JC, Marchiori DF, Packota GV. Unexpected variation of human molar size patterns. J Hum Evol 2021; 161:103072. [PMID: 34628299 DOI: 10.1016/j.jhevol.2021.103072] [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: 02/03/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 11/17/2022]
Abstract
A tenet of mammalian, including primate dental evolution, is the Inhibitory Cascade Model, where first molar (M1) size predicts in a linear cline the size and onset time of the second (M2) and third (M3) molars: a larger M1 portends a progressively smaller and later-developing M2 and M3. In contemporary modern Homo sapiens, later-developing M3s are less likely to erupt properly. The Inhibitory Cascade Model is also used to predict molar sizes of extinct taxa, including fossil Homo. The extent to which Inhibitory Cascade Model predictions hold in contemporary H. sapiens molars is unclear, including whether this tenet informs about molar initiation, development, and eruption. We tested these questions here. In our radiographic sample of 323 oral quadrants and molar rows from contemporary humans based on mesiodistal crown lengths, we observed the distribution of molar proportions with a central tendency around parity (M1 = M2 = M3) that parsed into 13 distinct molar size ratio patterns. These patterns presented at different frequencies (e.g., M1 > M2 > M3 in about one-third of cases) that reflected whether the molar row was located in the maxilla or mandible and included both linear (e.g., M1 < M2 < M3) and nonlinear molar size ratio progressions (e.g., M1 > M2 < M3). Up to four patterns were found in the same subject's mouth. Lastly, M1 size alone does not predict M3 size, developmental timing, or eruption; rather, M2 size is integral to predicting M3 size. Our study indicates that human molar size is genetically 'softwired' and sensitive to factors local to the human upper jaw vs. lower jaw. The lack of a single stereotypical molar size ratio for contemporary H. sapiens suggests that predictions of fossil H. sapiens molar sizes using the Inhibitory Cascade Model must be made with caution.
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Affiliation(s)
- Julia C Boughner
- Department of Anatomy, Physiology & Pharmacology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan S7N 5E5, Canada.
| | - Denver F Marchiori
- Department of Anatomy, Physiology & Pharmacology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan S7N 5E5, Canada.
| | - Garnet V Packota
- College of Dentistry, University of Saskatchewan, 105 Wiggins Road, Health Sciences Building, Saskatoon, SK, S7N 5E5, Canada
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17
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Zwir I, Del-Val C, Arnedo J, Pulkki-Råback L, Konte B, Yang SS, Romero-Zaliz R, Hintsanen M, Cloninger KM, Garcia D, Svrakic DM, Lester N, Rozsa S, Mesa A, Lyytikäinen LP, Giegling I, Kähönen M, Martinez M, Seppälä I, Raitoharju E, de Erausquin GA, Mamah D, Raitakari O, Rujescu D, Postolache TT, Gu CC, Sung J, Lehtimäki T, Keltikangas-Järvinen L, Cloninger CR. Three genetic-environmental networks for human personality. Mol Psychiatry 2021; 26:3858-3875. [PMID: 31748689 PMCID: PMC8550959 DOI: 10.1038/s41380-019-0579-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/26/2019] [Accepted: 10/24/2019] [Indexed: 02/07/2023]
Abstract
Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.
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Grants
- Spanish Ministry of Science and Technology TIN2012-38805 and DPI2015-69585-R
- The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), 41071 (Skidi), and 308676; the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research ; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association: and EU Horizon 2020 (grant 755320 for TAXINOMISIS).
- American Federation for Suicide Prevention
- Healthy Twin Family Register of Korea
- Anthropedia Foundation
- The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 322098, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), 41071 (Skidi), and 308676; the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research ; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association: and EU Horizon 2020 (grant 755320 for TAXINOMISIS); and Tampere University Hospital Supporting Foundation.
- American Society for Suicide Prevention
- American Foundation for Suicide Prevention
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Affiliation(s)
- Igor Zwir
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Department of Computer Science, University of Granada, Granada, Spain
| | - Coral Del-Val
- Department of Computer Science, University of Granada, Granada, Spain
| | - Javier Arnedo
- Department of Computer Science, University of Granada, Granada, Spain
| | - Laura Pulkki-Råback
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Bettina Konte
- Department of Psychiatry, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Sarah S Yang
- Department of Epidemiology, and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea
| | | | - Mirka Hintsanen
- Unit of Psychology, Faculty of Education, University of Oulu, Oulu, Finland
| | | | - Danilo Garcia
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
- Blekinge Centre of Competence, Blekinge County Council, Karlskrona, Sweden
| | - Dragan M Svrakic
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nigel Lester
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Sandor Rozsa
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Alberto Mesa
- Department of Computer Science, University of Granada, Granada, Spain
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ina Giegling
- Department of Psychiatry, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- University Clinic, Ludwig-Maximilian University, Munich, Germany
| | - Mika Kähönen
- Department of Clinical Physiology Tampere University Hospital, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Maribel Martinez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Gabriel A de Erausquin
- The Glenn Biggs Institute of Alzheimer's and Neurodegenerative Disorders, Long School of Medicine, University of Texas Heath San Antonio, San Antonio, TX, USA
| | - Daniel Mamah
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Olli Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Dan Rujescu
- Department of Psychiatry, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Teodor T Postolache
- Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
- Rocky Mountain Mental Illness, Research, Education, and Clinical Center for Veteran Suicide Prevention, Denver, CO, USA
| | - C Charles Gu
- Division of Biostatistics, School of Medicine, Washington University, St. Louis, MO, USA
| | - Joohon Sung
- Department of Epidemiology, and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - C Robert Cloninger
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Psychological and Brain Sciences, and School of Medicine, Department of Genetics, School of Arts and Sciences, Washington University, St. Louis, MO, USA.
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18
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Wang L, Xie W, Li K, Wang Z, Li X, Feng W, Li J. DysPIA: A Novel Dysregulated Pathway Identification Analysis Method. Front Genet 2021; 12:647653. [PMID: 34290733 PMCID: PMC8287415 DOI: 10.3389/fgene.2021.647653] [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: 01/13/2021] [Accepted: 04/20/2021] [Indexed: 11/13/2022] Open
Abstract
Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700-8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages "DysPIA" and "DysPIAData" are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).
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Affiliation(s)
- Limei Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.,Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weixin Xie
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Kongning Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Zhenzhen Wang
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weixing Feng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Jin Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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19
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Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC, Kalita CA, Alazizi A, Pazokitoroudi A, Sankararaman S, Wen X, Lanfear DE, Pique-Regi R, Gilad Y, Luca F. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 2021; 10:e67077. [PMID: 33988505 PMCID: PMC8248987 DOI: 10.7554/elife.67077] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022] Open
Abstract
Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.
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Affiliation(s)
- Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Alan Monziani
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Allison L Richards
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Katherine Rhodes
- Department of Human Genetics, University of ChicagoChicagoUnited States
| | - Michelle C Ward
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | | | - Sriram Sankararaman
- Department of Computer Science, UCLALos AngelesUnited States
- Department of Human Genetics, UCLALos AngelesUnited States
- Department of Computational Medicine, UCLALos AngelesUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Henry Ford HospitalDetroitUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Yoav Gilad
- Department of Human Genetics, University of ChicagoChicagoUnited States
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
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20
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Yu H, Guo Y, Chen J, Chen X, Jia P, Zhao Z. Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods. Genes (Basel) 2021; 12:665. [PMID: 33946654 PMCID: PMC8146818 DOI: 10.3390/genes12050665] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 02/03/2023] Open
Abstract
Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Xiangning Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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21
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Haltigan JD, Del Giudice M, Khorsand S. Growing points in attachment disorganization: looking back to advance forward. Attach Hum Dev 2021; 23:438-454. [PMID: 33890555 DOI: 10.1080/14616734.2021.1918454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this special issue paper we reflect on the next generation of attachment research with a focus on disorganization, a central but still poorly understood topic in this area. We suggest that progress will be facilitated by a return to attachment theory's evolutionary roots, and to the emphasis on biological function that inspired Bowlby's original thinking. Increased interdisciplinary cross-fertilization and collaborations would enable novel and generative research on some of the long-standing questions surrounding attachment disorganization. Accordingly, we present an agenda for future research that encompasses contributions of modern ethology and neurobiology, novel hypotheses based on the concept of adaptive decanalization, connections with neurodevelopmental vulnerability and risk for mental disorders such as schizophrenia, and the possibility of sex differences in the behavioral manifestations of attachment disorganization. We believe that these avenues of theory and research offer exciting potential for innovative work in attachment disorganization in the years ahead.
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Affiliation(s)
- John D Haltigan
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Marco Del Giudice
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Soha Khorsand
- Faculty of Science, Western University, London, Canada
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22
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Mishra BH, Mishra PP, Raitoharju E, Marttila S, Mononen N, Sievänen H, Viikari J, Juonala M, Laaksonen M, Hutri-Kähönen N, Kähönen M, Raitakari OT, Lehtimäki T. Modular genome-wide gene expression architecture shared by early traits of osteoporosis and atherosclerosis in the Young Finns Study. Sci Rep 2021; 11:7111. [PMID: 33782480 PMCID: PMC8007808 DOI: 10.1038/s41598-021-86536-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 03/12/2021] [Indexed: 02/07/2023] Open
Abstract
We analysed whole blood genome-wide expression data to identify gene co-expression modules shared by early traits of osteoporosis and atherosclerosis. Gene expression was profiled for the Young Finns Study participants. Bone mineral density and content were measured as early traits of osteoporosis. Carotid and bulbus intima media thickness were measured as early traits of atherosclerosis. Joint association of the modules, identified with weighted co-expression analysis, with early traits of the diseases was tested with multivariate analysis. Among the six modules significantly correlated with early traits of both the diseases, two had significant (adjusted p-values (p.adj) < 0.05) and another two had suggestively significant (p.adj < 0.25) joint association with the two diseases after adjusting for age, sex, body mass index, smoking habit, alcohol consumption, and physical activity. The three most significant member genes from the significant modules were NOSIP, GXYLT2, and TRIM63 (p.adj ≤ 0.18). Genes in the modules were enriched with biological processes that have separately been found to be involved in either bone metabolism or atherosclerosis. The gene modules and their most significant member genes identified in this study support the osteoporosis-atherosclerosis comorbidity hypothesis and can provide new joint biomarkers for both diseases and their dual prevention.
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Affiliation(s)
- Binisha H Mishra
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Saara Marttila
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Gerontology Research Center (GEREC), Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Harri Sievänen
- The UKK Institute for Health Promotion Research, Tampere, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku, Turku, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
| | - Markus Juonala
- Department of Medicine, University of Turku, Turku, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | | | - Nina Hutri-Kähönen
- Department of Paediatrics, Tampere University Hospital, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
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23
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Johnson CSC, Shively C, Michalson KT, Lea AJ, DeBo RJ, Howard TD, Hawkins GA, Appt SE, Liu Y, McCall CE, Herrington DM, Ip EH, Register TC, Snyder-Mackler N. Contrasting effects of Western vs Mediterranean diets on monocyte inflammatory gene expression and social behavior in a primate model. eLife 2021; 10:68293. [PMID: 34338633 PMCID: PMC8423447 DOI: 10.7554/elife.68293] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/28/2021] [Indexed: 01/20/2023] Open
Abstract
Dietary changes associated with industrialization increase the prevalence of chronic diseases, such as obesity, type II diabetes, and cardiovascular disease. This relationship is often attributed to an 'evolutionary mismatch' between human physiology and modern nutritional environments. Western diets enriched with foods that were scarce throughout human evolutionary history (e.g. simple sugars and saturated fats) promote inflammation and disease relative to diets more akin to ancestral human hunter-gatherer diets, such as a Mediterranean diet. Peripheral blood monocytes, precursors to macrophages and important mediators of innate immunity and inflammation, are sensitive to the environment and may represent a critical intermediate in the pathway linking diet to disease. We evaluated the effects of 15 months of whole diet manipulations mimicking Western or Mediterranean diet patterns on monocyte polarization in a well-established model of human health, the cynomolgus macaque (Macaca fascicularis). Monocyte transcriptional profiles differed markedly between diets, with 40% of transcripts showing differential expression (FDR < 0.05). Monocytes from Western diet consumers were polarized toward a more proinflammatory phenotype. The Western diet shifted the co-expression of 445 gene pairs, including small RNAs and transcription factors associated with metabolism and adiposity in humans, and dramatically altered behavior. For example, Western-fed individuals were more anxious and less socially integrated. These behavioral changes were also associated with some of the effects of diet on gene expression, suggesting an interaction between diet, central nervous system activity, and monocyte gene expression. This study provides new molecular insights into an evolutionary mismatch and uncovers new pathways through which Western diets alter monocyte polarization toward a proinflammatory phenotype.
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Affiliation(s)
- Corbin SC Johnson
- Department of Psychology, University of WashingtonSeattleUnited States
| | - Carol Shively
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Kristofer T Michalson
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Amanda J Lea
- Lewis-Sigler Institute for Integrative Genomics, Princeton UniversityPrincetonUnited States,Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - Ryne J DeBo
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Timothy D Howard
- Department of Biochemistry, Wake Forest School of MedicineWinston-SalemUnited States
| | - Gregory A Hawkins
- Department of Biochemistry, Wake Forest School of MedicineWinston-SalemUnited States
| | - Susan E Appt
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Yongmei Liu
- Division of Cardiology, Duke University School of MedicineDurhamUnited States
| | - Charles E McCall
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - David M Herrington
- Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Edward H Ip
- Department of Biostatistics and Data Science, Wake Forest School of MedicineWinston-SalemUnited States
| | - Thomas C Register
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Noah Snyder-Mackler
- Department of Psychology, University of WashingtonSeattleUnited States,Center for Studies in Demography and Ecology, University of WashingtonSeattleUnited States,Department of Biology, University of WashingtonSeattleUnited States,School of Life Sciences, Arizona State UniversityTempeUnited States,Center for Evolution & Medicine, Arizona State UniversityTempeUnited States
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24
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Huang W, Carbone MA, Lyman RF, Anholt RRH, Mackay TFC. Genotype by environment interaction for gene expression in Drosophila melanogaster. Nat Commun 2020; 11:5451. [PMID: 33116142 PMCID: PMC7595129 DOI: 10.1038/s41467-020-19131-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 09/22/2020] [Indexed: 01/17/2023] Open
Abstract
The genetics of phenotypic responses to changing environments remains elusive. Using whole-genome quantitative gene expression as a model, here we study how the genetic architecture of regulatory variation in gene expression changed in a population of fully sequenced inbred Drosophila melanogaster strains when flies developed in different environments (25 °C and 18 °C). We find a substantial fraction of the transcriptome exhibited genotype by environment interaction, implicating environmentally plastic genetic architecture of gene expression. Genetic variance in expression increases at 18 °C relative to 25 °C for most genes that have a change in genetic variance. Although the majority of expression quantitative trait loci (eQTLs) for the gene expression traits in the two environments are shared and have similar effects, analysis of the environment-specific eQTLs reveals enrichment of binding sites for two transcription factors. Finally, although genotype by environment interaction in gene expression could potentially disrupt genetic networks, the co-expression networks are highly conserved across environments. Genes with higher network connectivity are under stronger stabilizing selection, suggesting that stabilizing selection on expression plays an important role in promoting network robustness.
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Affiliation(s)
- Wen Huang
- Program in Genetics, Department of Biological Sciences, W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA.
- Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA.
| | - Mary Anna Carbone
- Program in Genetics, Department of Biological Sciences, W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA
- Center for Integrated Fungal Research and Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27695-7244, USA
| | - Richard F Lyman
- Program in Genetics, Department of Biological Sciences, W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA
- Clemson Center for Human Genetics, Clemson University, Greenwood, SC, 29646, USA
| | - Robert R H Anholt
- Program in Genetics, Department of Biological Sciences, W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA
- Clemson Center for Human Genetics, Clemson University, Greenwood, SC, 29646, USA
| | - Trudy F C Mackay
- Program in Genetics, Department of Biological Sciences, W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA.
- Clemson Center for Human Genetics, Clemson University, Greenwood, SC, 29646, USA.
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25
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Yu H, Chen D, Oyebamiji O, Zhao YY, Guo Y. Expression correlation attenuates within and between key signaling pathways in chronic kidney disease. BMC Med Genomics 2020; 13:134. [PMID: 32957963 PMCID: PMC7504859 DOI: 10.1186/s12920-020-00772-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet. METHODS RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase. RESULTS We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways. CONCLUSIONS Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131 USA
| | - Danqian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi’an, 710069 Shaanxi China
| | | | - Ying-Yong Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xi’an, 710069 Shaanxi China
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131 USA
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26
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Zwir I, Mishra P, Del-Val C, Gu CC, de Erausquin GA, Lehtimäki T, Cloninger CR. Uncovering the complex genetics of human personality: response from authors on the PGMRA Model. Mol Psychiatry 2020; 25:2210-2213. [PMID: 30886336 PMCID: PMC7515846 DOI: 10.1038/s41380-019-0399-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 02/14/2019] [Indexed: 12/01/2022]
Affiliation(s)
- Igor Zwir
- grid.4367.60000 0001 2355 7002Washington University School of Medicine, Department of Psychiatry, St. Louis, MO USA ,grid.4489.10000000121678994University of Granada, Department of Computer Science, Granada, Spain
| | - Pashupati Mishra
- grid.502801.e0000 0001 2314 6254Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Coral Del-Val
- grid.4489.10000000121678994University of Granada, Department of Computer Science, Granada, Spain
| | - C. Charles Gu
- grid.4367.60000 0001 2355 7002Washington University, School of Medicine, Division of Biostatistics, St. Louis, MO USA
| | - Gabriel A. de Erausquin
- grid.449717.80000 0004 5374 269XUniversity of Texas Rio-Grande Valley, School of Medicine, Department of Psychiatry and Neurology, and Institute of Neurosciences, Harlingen, TX USA
| | - Terho Lehtimäki
- grid.502801.e0000 0001 2314 6254Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - C. Robert Cloninger
- grid.4367.60000 0001 2355 7002Washington University School of Medicine, Department of Psychiatry, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Washington University, School of Arts and Sciences, Department of Psychological and Brain Sciences, and School of Medicine, Department of Genetics, St. Louis, MO USA
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27
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Pique-Regi R, Romero R, Tarca AL, Sendler ED, Xu Y, Garcia-Flores V, Leng Y, Luca F, Hassan SS, Gomez-Lopez N. Single cell transcriptional signatures of the human placenta in term and preterm parturition. eLife 2019; 8:52004. [PMID: 31829938 PMCID: PMC6949028 DOI: 10.7554/elife.52004] [Citation(s) in RCA: 172] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 12/12/2019] [Indexed: 01/02/2023] Open
Abstract
More than 135 million births occur each year; yet, the molecular underpinnings of human parturition in gestational tissues, and in particular the placenta, are still poorly understood. The placenta is a complex heterogeneous organ including cells of both maternal and fetal origin, and insults that disrupt the maternal-fetal dialogue could result in adverse pregnancy outcomes such as preterm birth. There is limited knowledge of the cell type composition and transcriptional activity of the placenta and its compartments during physiologic and pathologic parturition. To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal plate, and chorioamniotic membranes of women with or without labor at term and those with preterm labor. Significant differences in cell type composition and transcriptional profiles were found among placental compartments and across study groups. For the first time, two cell types were identified: 1) lymphatic endothelial decidual cells in the chorioamniotic membranes, and 2) non-proliferative interstitial cytotrophoblasts in the placental villi. Maternal macrophages from the chorioamniotic membranes displayed the largest differences in gene expression (e.g. NFKB1) in both processes of labor; yet, specific gene expression changes were also detected in preterm labor. Importantly, several placental scRNA-seq transcriptional signatures were modulated with advancing gestation in the maternal circulation, and specific immune cell type signatures were increased with labor at term (NK-cell and activated T-cell signatures) and with preterm labor (macrophage, monocyte, and activated T-cell signatures). Herein, we provide a catalogue of cell types and transcriptional profiles in the human placenta, shedding light on the molecular underpinnings and non-invasive prediction of the physiologic and pathologic parturition.
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Affiliation(s)
- Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States.,Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States
| | - Roberto Romero
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States.,Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, United States.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, United States.,Detroit Medical Center, Detroit, United States
| | - Adi L Tarca
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States.,Department of Computer Science, College of Engineering, Wayne State University, Detroit, United States
| | - Edward D Sendler
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States
| | - Yi Xu
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States
| | - Valeria Garcia-Flores
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States
| | - Yaozhu Leng
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, United States.,Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States
| | - Sonia S Hassan
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Department of Physiology, Wayne State University School of Medicine, Detroit, United States
| | - Nardhy Gomez-Lopez
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States.,Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Detroit, United States.,Department of Immunology, Microbiology, and Biochemistry, Wayne State University School of Medicine, Detroit, United States
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28
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Dalgıç E, Konu Ö, Öz ZS, Chan C. Lower connectivity of tumor coexpression networks is not specific to cancer. In Silico Biol 2019; 13:41-53. [PMID: 31156157 PMCID: PMC6597990 DOI: 10.3233/isb-190472] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Global level network analysis of molecular links is necessary for systems level view of complex diseases like cancer. Using genome-wide expression datasets, we constructed and compared gene co-expression based specific networks of pre-cancerous tumors (adenoma) and cancerous tumors (carcinoma) with paired normal networks to assess for any possible changes in network connectivity. Previously, loss of connectivity was reported as a characteristic of cancer samples. Here, we observed that pre-cancerous conditions also had significantly less connections than paired normal samples. We observed a loss of connectivity trend for colorectal adenoma, aldosterone producing adenoma and uterine leiomyoma. We also showed that the loss of connectivity trend is not specific to positive or negative correlation based networks. Differential hub genes, which were the most highly differentially less connected genes in tumor, were mostly different between different datasets. No common gene list could be defined which underlies the lower connectivity of tumor specific networks. Connectivity of colorectal cancer methylation targets was different from other genes. Extracellular space related terms were enriched in negative correlation based differential hubs and common methylation targets of colorectal carcinoma. Our results indicate a systems level change of lower connectivity as cells transform to not only cancer but also pre-cancerous conditions. This systems level behavior could not be attributed to a group of genes.
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Affiliation(s)
- Ertuğrul Dalgıç
- Department of Medical Biology, Zonguldak Bülent Ecevit University School of Medicine, Zonguldak, Turkey
| | - Özlen Konu
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
| | - Zehra Safi Öz
- Department of Medical Biology, Zonguldak Bülent Ecevit University School of Medicine, Zonguldak, Turkey
| | - Christina Chan
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
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