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Pan Y, Wang X, Sun J, Liu C, Peng J, Li Q. Multimodal joint deconvolution and integrative signature selection in proteomics. Commun Biol 2024; 7:493. [PMID: 38658803 PMCID: PMC11043077 DOI: 10.1038/s42003-024-06155-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/08/2024] [Indexed: 04/26/2024] Open
Abstract
Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .
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Affiliation(s)
- Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Genetics, Genomics & Informatics, University of Tennessee Health Science Center, Memphis, TN, 38105, USA
| | - Jiao Sun
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Junmin Peng
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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2
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Runemark A, Moore EC, Larson EL. Hybridization and gene expression: Beyond differentially expressed genes. Mol Ecol 2024:e17303. [PMID: 38411307 DOI: 10.1111/mec.17303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/06/2024] [Accepted: 02/15/2024] [Indexed: 02/28/2024]
Abstract
Gene expression has a key role in reproductive isolation, and studies of hybrid gene expression have identified mechanisms causing hybrid sterility. Here, we review the evidence for altered gene expression following hybridization and outline the mechanisms shown to contribute to altered gene expression in hybrids. Transgressive gene expression, transcending that of both parental species, is pervasive in early generation sterile hybrids, but also frequently observed in viable, fertile hybrids. We highlight studies showing that hybridization can result in transgressive gene expression, also in established hybrid lineages or species. Such extreme patterns of gene expression in stabilized hybrid taxa suggest that altered hybrid gene expression may result in hybridization-derived evolutionary novelty. We also conclude that while patterns of misexpression in hybrids are well documented, the understanding of the mechanisms causing misexpression is lagging. We argue that jointly assessing differences in cell composition and cell-specific changes in gene expression in hybrids, in addition to assessing changes in chromatin and methylation, will significantly advance our understanding of the basis of altered gene expression. Moreover, uncovering to what extent evolution of gene expression results in altered expression for individual genes, or entire networks of genes, will advance our understanding of how selection moulds gene expression. Finally, we argue that jointly studying the dual roles of altered hybrid gene expression, serving both as a mechanism for reproductive isolation and as a substrate for hybrid ecological adaptation, will lead to significant advances in our understanding of the evolution of gene expression.
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Affiliation(s)
- Anna Runemark
- Department of Biology, Lund University, Lund, Sweden
| | - Emily C Moore
- Department of Biological Sciences, University of Denver, Denver, Colorado, USA
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Erica L Larson
- Department of Biological Sciences, University of Denver, Denver, Colorado, USA
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3
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Panten J, Heinen T, Ernst C, Eling N, Wagner RE, Satorius M, Marioni JC, Stegle O, Odom DT. The dynamic genetic determinants of increased transcriptional divergence in spermatids. Nat Commun 2024; 15:1272. [PMID: 38341412 PMCID: PMC10858866 DOI: 10.1038/s41467-024-45133-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
Cis-genetic effects are key determinants of transcriptional divergence in discrete tissues and cell types. However, how cis- and trans-effects act across continuous trajectories of cellular differentiation in vivo is poorly understood. Here, we quantify allele-specific expression during spermatogenic differentiation at single-cell resolution in an F1 hybrid mouse system, allowing for the comprehensive characterisation of cis- and trans-genetic effects, including their dynamics across cellular differentiation. Collectively, almost half of the genes subject to genetic regulation show evidence for dynamic cis-effects that vary during differentiation. Our system also allows us to robustly identify dynamic trans-effects, which are less pervasive than cis-effects. In aggregate, genetic effects were strongest in round spermatids, which parallels their increased transcriptional divergence we identified between species. Our approach provides a comprehensive quantification of the variability of genetic effects in vivo, and demonstrates a widely applicable strategy to dissect the impact of regulatory variants on gene regulation in dynamic systems.
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Affiliation(s)
- Jasper Panten
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany
| | - Christina Ernst
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Nils Eling
- University of Zurich, Department of Quantitative Biomedicine, Zurich, 8057, Switzerland
- ETH Zurich, Institute for Molecular Health Sciences, Zurich, 8093, Switzerland
| | - Rebecca E Wagner
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
- Division of Mechanisms Regulating Gene Expression, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - Maja Satorius
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany.
| | - Duncan T Odom
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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4
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van Eijnatten AL, Sterken MG, Kammenga JE, Nijveen H, Snoek BL. The effect of developmental variation on expression QTLs in a multi parental Caenorhabditis elegans population. G3 (BETHESDA, MD.) 2024; 14:jkad273. [PMID: 38015660 PMCID: PMC10849341 DOI: 10.1093/g3journal/jkad273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/21/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
Regulation of gene expression plays a crucial role in developmental processes and adaptation to changing environments. expression quantitative trait locus (eQTL) mapping is a technique used to study the genetic regulation of gene expression using the transcriptomes of recombinant inbred lines (RILs). Typically, the age of the inbred lines at the time of RNA sampling is carefully controlled. This is necessary because the developmental process causes changes in gene expression, complicating the interpretation of eQTL mapping experiments. However, due to genetics and variation in ambient micro-environments, organisms can differ in their "developmental age," even if they are of the same chronological age. As a result, eQTL patterns are affected by developmental variation in gene expression. The model organism Caenorhabditis elegans is particularly suited for studying the effect of developmental variation on eQTL mapping patterns. In a span of days, C. elegans transitions from embryo through 4 larval stages to adult while undergoing massive changes to its transcriptome. Here, we use C. elegans to investigate the effect of developmental age variation on eQTL patterns and present a normalization procedure. We used dynamical eQTL mapping, which includes the developmental age as a cofactor, to separate the variation in development from genotypic variation and explain variation in gene expression levels. We compare classical single marker eQTL mapping and dynamical eQTL mapping using RNA-seq data of ∼200 multi-parental RILs of C. elegans. The results show that (1) many eQTLs are caused by developmental variation, (2) most trans-bands are developmental QTLs, and (3) dynamical eQTL mapping detects additional eQTLs not found with classical eQTL mapping. We recommend that correction for variation in developmental age should be strongly considered in eQTL mapping studies given the large impact of processes like development on the transcriptome.
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Affiliation(s)
- Abraham L van Eijnatten
- Theoretical Biology and Bioinformatics, Utrecht University, Padualaan 8,3584 CH Utrecht, The Netherlands
| | - Mark G Sterken
- Laboratory of Nematology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Jan E Kammenga
- Laboratory of Nematology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Harm Nijveen
- Laboratory of Bioinformatics, Wageningen University, Droevendaalsesteeg 1, Radix West, Building 107, 6708 PB Wageningen, The Netherlands
| | - Basten L Snoek
- Theoretical Biology and Bioinformatics, Utrecht University, Padualaan 8,3584 CH Utrecht, The Netherlands
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5
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Vochteloo M, Deelen P, Vink B, Tsai EA, Runz H, Andreu-Sánchez S, Fu J, Zhernakova A, Westra HJ, Franke L. PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs. Genome Biol 2024; 25:29. [PMID: 38254182 PMCID: PMC10802033 DOI: 10.1186/s13059-023-03151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
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Affiliation(s)
- Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Britt Vink
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science & Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
| | - Ellen A Tsai
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Sergio Andreu-Sánchez
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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7
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Liu J, Murray JI. Mechanisms of lineage specification in Caenorhabditis elegans. Genetics 2023; 225:iyad174. [PMID: 37847877 DOI: 10.1093/genetics/iyad174] [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: 08/26/2023] [Accepted: 09/18/2023] [Indexed: 10/19/2023] Open
Abstract
The studies of cell fate and lineage specification are fundamental to our understanding of the development of multicellular organisms. Caenorhabditis elegans has been one of the premiere systems for studying cell fate specification mechanisms at single cell resolution, due to its transparent nature, the invariant cell lineage, and fixed number of somatic cells. We discuss the general themes and regulatory mechanisms that have emerged from these studies, with a focus on somatic lineages and cell fates. We next review the key factors and pathways that regulate the specification of discrete cells and lineages during embryogenesis and postembryonic development; we focus on transcription factors and include numerous lineage diagrams that depict the expression of key factors that specify embryonic founder cells and postembryonic blast cells, and the diverse somatic cell fates they generate. We end by discussing some future perspectives in cell and lineage specification.
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Affiliation(s)
- Jun Liu
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - John Isaac Murray
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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9
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Britton C, Laing R, McNeilly TN, Perez MG, Otto TD, Hildersley KA, Maizels RM, Devaney E, Gillan V. New technologies to study helminth development and host-parasite interactions. Int J Parasitol 2023; 53:393-403. [PMID: 36931423 DOI: 10.1016/j.ijpara.2022.11.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/24/2022] [Accepted: 11/26/2022] [Indexed: 03/17/2023]
Abstract
How parasites develop and survive, and how they stimulate or modulate host immune responses are important in understanding disease pathology and for the design of new control strategies. Microarray analysis and bulk RNA sequencing have provided a wealth of data on gene expression as parasites develop through different life-cycle stages and on host cell responses to infection. These techniques have enabled gene expression in the whole organism or host tissue to be detailed, but do not take account of the heterogeneity between cells of different types or developmental stages, nor the spatial organisation of these cells. Single-cell RNA-seq (scRNA-seq) adds a new dimension to studying parasite biology and host immunity by enabling gene profiling at the individual cell level. Here we review the application of scRNA-seq to establish gene expression cell atlases for multicellular helminths and to explore the expansion and molecular profile of individual host cell types involved in parasite immunity and tissue repair. Studying host-parasite interactions in vivo is challenging and we conclude this review by briefly discussing the applications of organoids (stem-cell derived mini-tissues) to examine host-parasite interactions at the local level, and as a potential system to study parasite development in vitro. Organoid technology and its applications have developed rapidly, and the elegant studies performed to date support the use of organoids as an alternative in vitro system for research on helminth parasites.
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Affiliation(s)
- Collette Britton
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom.
| | - Roz Laing
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Tom N McNeilly
- Disease Control Department, Moredun Research Institute, Penicuik, United Kingdom
| | - Matias G Perez
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Thomas D Otto
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, United Kingdom
| | - Katie A Hildersley
- Disease Control Department, Moredun Research Institute, Penicuik, United Kingdom
| | - Rick M Maizels
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, United Kingdom
| | - Eileen Devaney
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Victoria Gillan
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
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10
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Gunawardhana KL, Hong L, Rugira T, Uebbing S, Kucharczak J, Mehta S, Karunamuni DR, Cabera-Mendoza B, Gandotra N, Scharfe C, Polimanti R, Noonan JP, Mani A. A systems biology approach identifies the role of dysregulated PRDM6 in the development of hypertension. J Clin Invest 2023; 133:e160036. [PMID: 36602864 PMCID: PMC9927944 DOI: 10.1172/jci160036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 12/07/2022] [Indexed: 01/06/2023] Open
Abstract
Genetic variants in the third intron of the PRDM6 gene have been associated with BP traits in multiple GWAS. By combining fine mapping, massively parallel reporter assays, and gene editing, we identified super enhancers that drive the expression of PRDM6 and are partly regulated by STAT1 as the causal variants for hypertension. The heterozygous disruption of Prdm6 in mice expressing Cre recombinase under the control of mouse smooth muscle cell protein 22-α promoter (Prdm6fl/+ SM22-Cre) exhibited a markedly higher number of renin-producing cells in the kidneys at E18.5 compared with WT littermates and developed salt-induced systemic hypertension that was completely responsive to the renin inhibitor aliskiren. Strikingly, RNA-Seq analysis of the mouse aortas identified a network of PRDM6-regulated genes that are located in GWAS-associated loci for blood pressure, most notably Sox6, which modulates renin expression in the kidney. Accordingly, the smooth muscle cell-specific disruption of Sox6 in Prdm6fl/+ SM22-Cre mice resulted in a dramatic reduction of renin. Fate mapping and histological studies also showed increased numbers of neural crest-derived cells accompanied by increased collagen deposition in the kidneys of Prdm6fl/+ Wnt1Cre-ZsGreen1Cre mice compared with WT mice. These findings establish the role of PRDM6 as a regulator of renin-producing cell differentiation into smooth muscle cells and as an attractive target for the development of antihypertensive drugs.
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Affiliation(s)
| | - Lingjuan Hong
- Cardiovascular Research Center, Department of Internal Medicine, and
| | - Trojan Rugira
- Cardiovascular Research Center, Department of Internal Medicine, and
| | - Severin Uebbing
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Joanna Kucharczak
- Trinity Hall College, University of Cambridge, Cambridge, United Kingdom
| | - Sameet Mehta
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Dineth R. Karunamuni
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Brenda Cabera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Neeru Gandotra
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Curt Scharfe
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - James P. Noonan
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Arya Mani
- Cardiovascular Research Center, Department of Internal Medicine, and
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
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11
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Li X, Kumar S, Brenneman KV, Anderson TJC. Bulk segregant linkage mapping for rodent and human malaria parasites. Parasitol Int 2022; 91:102653. [PMID: 36007706 DOI: 10.1016/j.parint.2022.102653] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/25/2022]
Abstract
In 2005 Richard Carter's group surprised the malaria genetics community with an elegant approach to rapidly mapping the genetic basis of phenotypic traits in rodent malaria parasites. This approach, which he termed "linkage group selection", utilized bulk pools of progeny, rather than individual clones, and exploited simple selection schemes to identify genome regions underlying resistance to drug treatment (or other phenotypes). This work was the first application of "bulk segregant" methodologies for genetic mapping in microbes: this approach is now widely used in yeast, and across multiple recombining pathogens ranging from Aspergillus fungi to Schistosome parasites. Genetic crosses of human malaria parasites (for which Richard Carter was also a pioneer) can now be conducted in humanized mice, providing new opportunities for exploiting bulk segregant approaches for a wide variety of malaria parasite traits. We review the application of bulk segregant approaches to mapping malaria parasite traits and suggest additional developments that may further expand the utility of this powerful approach.
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Affiliation(s)
- Xue Li
- Program in Disease Intervention and Prevention, Texas Biomedical Research Institute, San Antonio, TX, USA.
| | - Sudhir Kumar
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Katelyn Vendrely Brenneman
- Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Tim J C Anderson
- Program in Disease Intervention and Prevention, Texas Biomedical Research Institute, San Antonio, TX, USA.
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Wang AJ, Wibisono P, Geppert BM, Liu Y. Using single-worm RNA sequencing to study C. elegans responses to pathogen infection. BMC Genomics 2022; 23:653. [PMID: 36104659 PMCID: PMC9472404 DOI: 10.1186/s12864-022-08878-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
Background Caenorhabditis elegans is an excellent research model whose populations have been used in many studies to address various biological questions. Although worm-to-worm phenotypic variations in isogenic populations have been persistently observed, they are not well understood and are often ignored or averaged out in studies, masking the impacts of such variations on data collection and interpretation. Single-worm RNA sequencing that profiles the transcriptomes of individual animals has the power to examine differences between individuals in a worm population, but this approach has been understudied. The integrity of the starting RNA, the quality of the library and sequence data, as well as the transcriptome-profiling effectiveness of single-worm RNA-seq remain unclear. Therefore, more studies are needed to improve this technique and its application in research. Results In this study, we aimed to develop a single-worm RNA-seq method that includes five steps: worm lysis and RNA extraction, cDNA synthesis, library preparation, sequencing, and sequence data analysis. We found that the mechanical lysis of worms using a Qiagen TissueLyser maintained RNA integrity and determined that the quality of our single-worm libraries was comparable to that of standard RNA-seq libraries based on assessments of a variety of parameters. Furthermore, analysis of pathogen infection-induced gene expression using single-worm RNA-seq identified a core set of genes and biological processes relating to the immune response and metabolism affected by infection. These results demonstrate the effectiveness of our single-worm RNA-seq method in transcriptome profiling and its usefulness in addressing biological questions. Conclusions We have developed a single-worm RNA-seq method to effectively profile gene expression in individual C. elegans and have applied this method to study C. elegans responses to pathogen infection. Key aspects of our single-worm RNA-seq libraries were comparable to those of standard RNA-seq libraries. The single-worm method captured the core set of, but not all, infection-affected genes and biological processes revealed by the standard method, indicating that there was gene regulation that is not shared by all individuals in a population. Our study suggests that combining single-worm and standard RNA-seq approaches will allow for detecting and distinguishing shared and individual-specific gene activities in isogenic populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08878-x.
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The impact of species-wide gene expression variation on Caenorhabditis elegans complex traits. Nat Commun 2022; 13:3462. [PMID: 35710766 PMCID: PMC9203580 DOI: 10.1038/s41467-022-31208-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/08/2022] [Indexed: 12/15/2022] Open
Abstract
Phenotypic variation in organism-level traits has been studied in Caenorhabditis elegans wild strains, but the impacts of differences in gene expression and the underlying regulatory mechanisms are largely unknown. Here, we use natural variation in gene expression to connect genetic variants to differences in organismal-level traits, including drug and toxicant responses. We perform transcriptomic analyses on 207 genetically distinct C. elegans wild strains to study natural regulatory variation of gene expression. Using this massive dataset, we perform genome-wide association mappings to investigate the genetic basis underlying gene expression variation and reveal complex genetic architectures. We find a large collection of hotspots enriched for expression quantitative trait loci across the genome. We further use mediation analysis to understand how gene expression variation could underlie organism-level phenotypic variation for a variety of complex traits. These results reveal the natural diversity in gene expression and possible regulatory mechanisms in this keystone model organism, highlighting the promise of using gene expression variation to understand how phenotypic diversity is generated.
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14
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Nathan A, Asgari S, Ishigaki K, Valencia C, Amariuta T, Luo Y, Beynor JI, Baglaenko Y, Suliman S, Price AL, Lecca L, Murray MB, Moody DB, Raychaudhuri S. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 2022; 606:120-128. [PMID: 35545678 PMCID: PMC9842455 DOI: 10.1038/s41586-022-04713-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 03/31/2022] [Indexed: 02/02/2023]
Abstract
Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.
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Affiliation(s)
- Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Samira Asgari
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jessica I Beynor
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Suliman
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Leonid Lecca
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Socios En Salud Sucursal Peru, Lima, Peru
| | - Megan B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Division of Global Health Equity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - D Branch Moody
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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15
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Maria M, Pouyanfar N, Örd T, Kaikkonen MU. The Power of Single-Cell RNA Sequencing in eQTL Discovery. Genes (Basel) 2022; 13:502. [PMID: 35328055 PMCID: PMC8949403 DOI: 10.3390/genes13030502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 02/05/2023] Open
Abstract
Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.
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Affiliation(s)
| | | | | | - Minna U. Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland; (M.M.); (N.P.); (T.Ö.)
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Dia A, Cheeseman IH. Single-cell genome sequencing of protozoan parasites. Trends Parasitol 2021; 37:803-814. [PMID: 34172399 PMCID: PMC8364489 DOI: 10.1016/j.pt.2021.05.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 12/27/2022]
Abstract
Despite considerable genetic variation within hosts, most parasite genome sequencing studies focus on bulk samples composed of millions of cells. Analysis of bulk samples is biased toward the dominant genotype, concealing cell-to-cell variation and rare variants. To tackle this, single-cell sequencing approaches have been developed and tailored to specific host-parasite systems. These are allowing the genetic diversity and kinship in complex parasite populations to be deciphered and for de novo genetic variation to be captured. Here, we outline the methodologies being used for single-cell sequencing of parasitic protozoans, such as Plasmodium and Leishmania spp., and how these tools are being applied to understand parasite biology.
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Affiliation(s)
- Aliou Dia
- Host-Pathogen Interaction Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Ian H Cheeseman
- Host-Pathogen Interaction Program, Texas Biomedical Research Institute, San Antonio, TX, USA.
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17
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Evans KS, van Wijk MH, McGrath PT, Andersen EC, Sterken MG. From QTL to gene: C. elegans facilitates discoveries of the genetic mechanisms underlying natural variation. Trends Genet 2021; 37:933-947. [PMID: 34229867 DOI: 10.1016/j.tig.2021.06.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 11/15/2022]
Abstract
Although many studies have examined quantitative trait variation across many species, only a small number of genes and thereby molecular mechanisms have been discovered. Without these data, we can only speculate about evolutionary processes that underlie trait variation. Here, we review how quantitative and molecular genetics in the nematode Caenorhabditis elegans led to the discovery and validation of 37 quantitative trait genes over the past 15 years. Using these data, we can start to make inferences about evolution from these quantitative trait genes, including the roles that coding versus noncoding variation, gene family expansion, common versus rare variants, pleiotropy, and epistasis play in trait variation across this species.
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Affiliation(s)
- Kathryn S Evans
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA; Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
| | - Marijke H van Wijk
- Laboratory of Nematology, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands
| | - Patrick T McGrath
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Erik C Andersen
- Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA.
| | - Mark G Sterken
- Laboratory of Nematology, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands.
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