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van Lingen HJ, Suarez-Diez M, Saccenti E. Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2024; 1867:195058. [PMID: 39154857 DOI: 10.1016/j.bbagrm.2024.195058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/25/2024] [Accepted: 08/09/2024] [Indexed: 08/20/2024]
Abstract
Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.
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Affiliation(s)
- Henk J van Lingen
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, the Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, the Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, the Netherlands.
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2
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Wang C, Wang T, Kiryluk K, Wei Y, Aschard H, Ionita-Laza I. Genome-wide discovery for biomarkers using quantile regression at biobank scale. Nat Commun 2024; 15:6460. [PMID: 39085219 PMCID: PMC11291931 DOI: 10.1038/s41467-024-50726-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/18/2024] [Indexed: 08/02/2024] Open
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, NY, USA
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | | | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY, USA.
- Department of Statistics, Lund University, Lund, Sweden.
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3
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Cote AC, Young HE, Huckins LM. Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex. HGG ADVANCES 2024; 5:100311. [PMID: 38773772 PMCID: PMC11214266 DOI: 10.1016/j.xhgg.2024.100311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/24/2024] Open
Abstract
Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.
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Affiliation(s)
- Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Hannah E Young
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.
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4
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Burnham KL, Milind N, Lee W, Kwok AJ, Cano-Gamez K, Mi Y, Geoghegan CG, Zhang P, McKechnie S, Soranzo N, Hinds CJ, Knight JC, Davenport EE. eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis. CELL GENOMICS 2024; 4:100587. [PMID: 38897207 PMCID: PMC11293594 DOI: 10.1016/j.xgen.2024.100587] [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: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.
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Affiliation(s)
- Katie L Burnham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Nikhil Milind
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; University of Cambridge, Cambridge, UK
| | - Wanseon Lee
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Andrew J Kwok
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Kiki Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Yuxin Mi
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Ping Zhang
- Centre for Human Genetics, University of Oxford, Oxford, UK; Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK
| | | | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Charles J Hinds
- Centre for Translational Medicine & Therapeutics, William Harvey Research Institute, Faculty of Medicine & Dentistry, Queen Mary University of London, London, UK
| | - Julian C Knight
- Centre for Human Genetics, University of Oxford, Oxford, UK; Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.
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5
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Peng L, Renauer PA, Sferruzza G, Yang L, Zou Y, Fang Z, Park JJ, Chow RD, Zhang Y, Lin Q, Bai M, Sanchez A, Zhang Y, Lam SZ, Ye L, Chen S. In vivo AAV-SB-CRISPR screens of tumor-infiltrating primary NK cells identify genetic checkpoints of CAR-NK therapy. Nat Biotechnol 2024:10.1038/s41587-024-02282-4. [PMID: 38918616 DOI: 10.1038/s41587-024-02282-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 05/10/2024] [Indexed: 06/27/2024]
Abstract
Natural killer (NK) cells have clinical potential against cancer; however, multiple limitations hinder the success of NK cell therapy. Here, we performed unbiased functional mapping of tumor-infiltrating NK (TINK) cells using in vivo adeno-associated virus (AAV)-SB (Sleeping Beauty)-CRISPR (clustered regularly interspaced short palindromic repeats) screens in four solid tumor mouse models. In parallel, we characterized single-cell transcriptomic landscapes of TINK cells, which identified previously unexplored subpopulations of NK cells and differentially expressed TINK genes. As a convergent hit, CALHM2-knockout (KO) NK cells showed enhanced cytotoxicity and tumor infiltration in mouse primary NK cells and human chimeric antigen receptor (CAR)-NK cells. CALHM2 mRNA reversed the CALHM2-KO phenotype. CALHM2 KO in human primary NK cells enhanced their cytotoxicity, degranulation and cytokine production. Transcriptomics profiling revealed CALHM2-KO-altered genes and pathways in both baseline and stimulated conditions. In a solid tumor model resistant to unmodified CAR-NK cells, CALHM2-KO CAR-NK cells showed potent in vivo antitumor efficacy. These data identify endogenous genetic checkpoints that naturally limit NK cell function and demonstrate the use of CALHM2 KO for engineering enhanced NK cell-based immunotherapies.
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Affiliation(s)
- Lei Peng
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Paul A Renauer
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
| | - Giacomo Sferruzza
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Luojia Yang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
| | - Yongji Zou
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Zhenghao Fang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Jonathan J Park
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
- M.D.-Ph.D. Program, Yale University, West Haven, CT, USA
| | - Ryan D Chow
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
- M.D.-Ph.D. Program, Yale University, West Haven, CT, USA
| | - Yueqi Zhang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Qianqian Lin
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Meizhu Bai
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Angelica Sanchez
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Yale College, Yale University, New Haven, CT, USA
| | - Yongzhan Zhang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Stanley Z Lam
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Lupeng Ye
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
- System Biology Institute, Yale University, West Haven, CT, USA.
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA.
- Nanjing University, Nanjing, China.
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
- System Biology Institute, Yale University, West Haven, CT, USA.
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA.
- Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA.
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA.
- M.D.-Ph.D. Program, Yale University, West Haven, CT, USA.
- Immunobiology Program, Yale University, New Haven, CT, USA.
- Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA.
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA.
- Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA.
- Yale Liver Center, Yale University School of Medicine, New Haven, CT, USA.
- Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT, USA.
- Yale Center for RNA Science and Medicine, Yale University School of Medicine, New Haven, CT, USA.
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6
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Koufaris C, Demetriadou C, Nicolaidou V, Kirmizis A. Bioinformatic Analysis Reveals the Association of Human N-Terminal Acetyltransferase Complexes with Distinct Transcriptional and Post-Transcriptional Processes. Biochem Genet 2024:10.1007/s10528-024-10860-z. [PMID: 38864963 DOI: 10.1007/s10528-024-10860-z] [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: 04/22/2024] [Accepted: 06/05/2024] [Indexed: 06/13/2024]
Abstract
N-terminal acetyltransferases (NAT) are the protein complexes that deposit the abundant N-terminal acetylation (Nt-Ac) on eukaryotic proteins, with seven human complexes currently identified. Despite the increasing recognition of their biological and clinical importance, NAT regulation remains elusive. In this study, we performed a bioinformatic investigation to identify transcriptional and post-transcriptional processes that could be involved in the regulation of human NAT complexes. First, co-expression analysis of independent transcriptomic datasets revealed divergent pathway associations for human NAT, which are potentially connected to their distinct cellular functions. One interesting connection uncovered was the coordinated regulation of the NatA and proteasomal genes in cancer and immune cells, confirmed by analysis of multiple datasets and in isolated primary T cells. Another distinctive association was of NAA40 (NatD) with DNA replication, in cancer and non-cancer settings. The link between NAA40 transcription and DNA replication is potentially mediated through E2F1, which we have experimentally shown to bind the promoter of this NAT. Second, the coupled examination of transcriptomic and proteomic datasets revealed a much greater intra-complex concordance of NAT subunits at the protein compared to the transcript level, indicating the predominance of post-transcriptional processes for achieving their coordination. In agreement with this concept, we also found that the effects of somatic copy number alterations affecting NAT genes are attenuated post-transcriptionally. In conclusion, this study provides novel insights into the regulation of human NAT complexes.
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Affiliation(s)
- C Koufaris
- Epigenetics and Gene Regulation Laboratory, Department of Biological Sciences, University of Cyprus, 2109, Nicosia, Cyprus
| | - C Demetriadou
- Epigenetics and Gene Regulation Laboratory, Department of Biological Sciences, University of Cyprus, 2109, Nicosia, Cyprus
| | - V Nicolaidou
- Department of Life Sciences, University of Nicosia, Nicosia, Cyprus
| | - A Kirmizis
- Epigenetics and Gene Regulation Laboratory, Department of Biological Sciences, University of Cyprus, 2109, Nicosia, Cyprus.
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7
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Wang X, Lu J, Han M, Wang Z, Zhang H, Liu Y, Zhou P, Fu J, Xie Y. Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize. PHYSIOLOGIA PLANTARUM 2024; 176:e14386. [PMID: 38887947 DOI: 10.1111/ppl.14386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/27/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.
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Affiliation(s)
- Xiaoli Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiawen Lu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingfang Han
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zheyuan Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunjun Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Peng Zhou
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junjie Fu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuxin Xie
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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8
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [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: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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9
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Hu J, Weber JN, Fuess LE, Steinel NC, Bolnick DI, Wang M. A spectral framework to map QTLs affecting joint differential networks of gene co-expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587398. [PMID: 38585912 PMCID: PMC10996691 DOI: 10.1101/2024.03.29.587398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.
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Affiliation(s)
- Jiaxin Hu
- Department of Statistics, University of Wisconsin-Madison
| | - Jesse N. Weber
- Department of Integrative Biology, University of Wisconsin-Madison
| | | | | | - Daniel I. Bolnick
- Department of Ecology and Evolutionary Biology, University of Connecticut
| | - Miaoyan Wang
- Department of Statistics, University of Wisconsin-Madison
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10
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Rabaglino MB, Sánchez JM, McDonald M, O’Callaghan E, Lonergan P. Maternal blood transcriptome as a sensor of fetal organ maturation at the end of organogenesis in cattle†. Biol Reprod 2023; 109:749-758. [PMID: 37658765 PMCID: PMC10651065 DOI: 10.1093/biolre/ioad103] [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: 05/18/2023] [Revised: 07/25/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023] Open
Abstract
Harnessing information from the maternal blood to predict fetal growth is attractive yet scarcely explored in livestock. The objectives were to determine the transcriptomic modifications in maternal blood and fetal liver, gonads, and heart according to fetal weight and to model a molecular signature based on the fetal organs allowing the prediction of fetal weight from the maternal blood transcriptome in cattle. In addition to a contemporaneous maternal blood sample, organ samples were collected from 10 male fetuses at 42 days of gestation for RNA-sequencing. Fetal weight ranged from 1.25 to 1.69 g (mean = 1.44 ± 0.15 g). Clustering data analysis revealed clusters of co-expressed genes positively correlated with fetal weight and enriching ontological terms biologically relevant for the organ. For the heart, the 1346 co-expressed genes were involved in energy generation and protein synthesis. For the gonads, the 1042 co-expressed genes enriched seminiferous tubule development. The 459 co-expressed genes identified in the liver were associated with lipid synthesis and metabolism. Finally, the cluster of 571 co-expressed genes determined in maternal blood enriched oxidative phosphorylation and thermogenesis. Next, data from the fetal organs were used to train a regression model of fetal weight, which was predicted with the maternal blood data. The best prediction was achieved when the model was trained with 35 co-expressed genes overlapping between heart and maternal blood (root-mean-square error = 0.04, R2 = 0.93). In conclusion, linking transcriptomic information from maternal blood with that from the fetal heart unveiled maternal blood as a predictor of fetal development.
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Affiliation(s)
- Maria Belen Rabaglino
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - José María Sánchez
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain
| | - Michael McDonald
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Elena O’Callaghan
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Pat Lonergan
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
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11
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Torre D, Francoeur NJ, Kalma Y, Gross Carmel I, Melo BS, Deikus G, Allette K, Flohr R, Fridrikh M, Vlachos K, Madrid K, Shah H, Wang YC, Sridhar SH, Smith ML, Eliyahu E, Azem F, Amir H, Mayshar Y, Marazzi I, Guccione E, Schadt E, Ben-Yosef D, Sebra R. Isoform-resolved transcriptome of the human preimplantation embryo. Nat Commun 2023; 14:6902. [PMID: 37903791 PMCID: PMC10616205 DOI: 10.1038/s41467-023-42558-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 10/15/2023] [Indexed: 11/01/2023] Open
Abstract
Human preimplantation development involves extensive remodeling of RNA expression and splicing. However, its transcriptome has been compiled using short-read sequencing data, which fails to capture most full-length mRNAs. Here, we generate an isoform-resolved transcriptome of early human development by performing long- and short-read RNA sequencing on 73 embryos spanning the zygote to blastocyst stages. We identify 110,212 unannotated isoforms transcribed from known genes, including highly conserved protein-coding loci and key developmental regulators. We further identify 17,964 isoforms from 5,239 unannotated genes, which are largely non-coding, primate-specific, and highly associated with transposable elements. These isoforms are widely supported by the integration of published multi-omics datasets, including single-cell 8CLC and blastoid studies. Alternative splicing and gene co-expression network analyses further reveal that embryonic genome activation is associated with splicing disruption and transient upregulation of gene modules. Together, these findings show that the human embryo transcriptome is far more complex than currently known, and will act as a valuable resource to empower future studies exploring development.
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Affiliation(s)
- Denis Torre
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Yael Kalma
- Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, Tel Aviv, 64239, Israel
| | - Ilana Gross Carmel
- Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, Tel Aviv, 64239, Israel
| | - Betsaida S Melo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kimaada Allette
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ron Flohr
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, 69978, Israel
- CORAL - Center Of Regeneration and Longevity, Tel-Aviv Sourasky Medical Center, Tel Aviv, 64239, Israel
| | - Maya Fridrikh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Kent Madrid
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hardik Shah
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ying-Chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shwetha H Sridhar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Melissa L Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, 40202, USA
| | - Efrat Eliyahu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Foad Azem
- Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, Tel Aviv, 64239, Israel
| | - Hadar Amir
- Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, Tel Aviv, 64239, Israel
| | - Yoav Mayshar
- Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Ivan Marazzi
- Department of Biological Chemistry, Center for Epigenetics and Metabolism, University of California, Irvine, CA, 92697, USA
| | - Ernesto Guccione
- Center for OncoGenomics and Innovative Therapeutics (COGIT); Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Dalit Ben-Yosef
- Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, Tel Aviv, 64239, Israel.
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, 69978, Israel.
- CORAL - Center Of Regeneration and Longevity, Tel-Aviv Sourasky Medical Center, Tel Aviv, 64239, Israel.
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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12
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Brown AA, Fernandez-Tajes JJ, Hong MG, Brorsson CA, Koivula RW, Davtian D, Dupuis T, Sartori A, Michalettou TD, Forgie IM, Adam J, Allin KH, Caiazzo R, Cederberg H, De Masi F, Elders PJM, Giordano GN, Haid M, Hansen T, Hansen TH, Hattersley AT, Heggie AJ, Howald C, Jones AG, Kokkola T, Laakso M, Mahajan A, Mari A, McDonald TJ, McEvoy D, Mourby M, Musholt PB, Nilsson B, Pattou F, Penet D, Raverdy V, Ridderstråle M, Romano L, Rutters F, Sharma S, Teare H, 't Hart L, Tsirigos KD, Vangipurapu J, Vestergaard H, Brunak S, Franks PW, Frost G, Grallert H, Jablonka B, McCarthy MI, Pavo I, Pedersen O, Ruetten H, Walker M, Adamski J, Schwenk JM, Pearson ER, Dermitzakis ET, Viñuela A. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nat Commun 2023; 14:5062. [PMID: 37604891 PMCID: PMC10442420 DOI: 10.1038/s41467-023-40569-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Affiliation(s)
- Andrew A Brown
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Juan J Fernandez-Tajes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Caroline A Brorsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, United Kingdom
| | - David Davtian
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Théo Dupuis
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Ambra Sartori
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Theodora-Dafni Michalettou
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Jonathan Adam
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Kristine H Allin
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert Caiazzo
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Petra J M Elders
- Department of General Practice, Amsterdam UMC- location Vumc, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Giuseppe N Giordano
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mark Haid
- Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Torben Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Tue H Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Alison J Heggie
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cédric Howald
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, 35127, Italy
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, United Kingdom
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Miranda Mourby
- Nuffield Department of Population Health, Centre for Health, Law and Emerging Technologies (HeLEX), University of Oxford, Oxford, OX2 7DD, United Kingdom
| | - Petra B Musholt
- Global Development, Sanofi-Aventis Deutschland GmbH, Hoechst Industrial Park, Frankfurt am Main, 65926, Germany
| | - Birgitte Nilsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Francois Pattou
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Deborah Penet
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Violeta Raverdy
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | | | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Femke Rutters
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, München, Germany
| | - Harriet Teare
- Centre for Health Law and Emerging Technologies, Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom
| | - Leen 't Hart
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology section, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
- Steno Diabetes Center Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Gary Frost
- Nutrition and Dietetics Research Group, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Bernd Jablonka
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- GENENTECH, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H, Vienna, 1030, Austria
| | - Oluf Pedersen
- Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Hartmut Ruetten
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United Kingdom
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Experimental Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland.
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland.
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland.
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
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13
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Wang C, Wang T, Wei Y, Aschard H, Ionita-Laza I. Quantile Regression for biomarkers in the UK Biobank. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543699. [PMID: 37333162 PMCID: PMC10274625 DOI: 10.1101/2023.06.05.543699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. An alternative and easy to apply approach is quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest by modeling conditional quantiles within a regression framework. Quantile regression can be applied efficiently at biobank scale using standard statistical packages in much the same way as linear regression, while having some unique advantages such as identifying variants with heterogeneous effects across different quantiles, including non-additive effects and variants involved in gene-environment interactions; accommodating a wide range of phenotype distributions with invariance to trait transformation; and overall providing more detailed information about the underlying genotype-phenotype associations. Here, we demonstrate the value of quantile regression in the context of GWAS by applying it to 39 quantitative traits in the UK Biobank (n > 300 , 000 individuals). Across these 39 traits we identify 7,297 significant loci, including 259 loci only detected by quantile regression. We show that quantile regression can help uncover replicable but unmodelled gene-environment interactions, and can provide additional key insights into poorly understood genotype-phenotype correlations for clinically relevant biomarkers at minimal additional cost.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, USA
| | - Tianying Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, France
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14
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Kerimov N, Tambets R, Hayhurst JD, Rahu I, Kolberg P, Raudvere U, Kuzmin I, Chowdhary A, Vija A, Teras HJ, Kanai M, Ulirsch J, Ryten M, Hardy J, Guelfi S, Trabzuni D, Kim-Hellmuth S, Rayner W, Finucane H, Peterson H, Mosaku A, Parkinson H, Alasoo K. Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535816. [PMID: 37066341 PMCID: PMC10104061 DOI: 10.1101/2023.04.06.535816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - James D Hayhurst
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ida Rahu
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Uku Raudvere
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Anshika Chowdhary
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Andreas Vija
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Hans J Teras
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacob Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - John Hardy
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Sebastian Guelfi
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Daniah Trabzuni
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London
| | - Sarah Kim-Hellmuth
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany
| | - Will Rayner
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Hilary Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
| | - Abayomi Mosaku
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Helen Parkinson
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, 51009, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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15
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Peng L, Renauer PA, Ye L, Yang L, Park JJ, Chow RD, Zhang Y, Lin Q, Bai M, Sanchez A, Zhang Y, Lam SZ, Chen S. Perturbomics of tumor-infiltrating NK cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532653. [PMID: 36993337 PMCID: PMC10055047 DOI: 10.1101/2023.03.14.532653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Natural killer (NK) cells are an innate immune cell type that serves at the first level of defense against pathogens and cancer. NK cells have clinical potential, however, multiple current limitations exist that naturally hinder the successful implementation of NK cell therapy against cancer, including their effector function, persistence, and tumor infiltration. To unbiasedly reveal the functional genetic landscape underlying critical NK cell characteristics against cancer, we perform perturbomics mapping of tumor infiltrating NK cells by joint in vivo AAV-CRISPR screens and single cell sequencing. We establish a strategy with AAV-SleepingBeauty(SB)- CRISPR screening leveraging a custom high-density sgRNA library targeting cell surface genes, and perform four independent in vivo tumor infiltration screens in mouse models of melanoma, breast cancer, pancreatic cancer, and glioblastoma. In parallel, we characterize single-cell transcriptomic landscapes of tumor-infiltrating NK cells, which identifies previously unexplored sub-populations of NK cells with distinct expression profiles, a shift from immature to mature NK (mNK) cells in the tumor microenvironment (TME), and decreased expression of mature marker genes in mNK cells. CALHM2, a calcium homeostasis modulator that emerges from both screen and single cell analyses, shows both in vitro and in vivo efficacy enhancement when perturbed in chimeric antigen receptor (CAR)-NK cells. Differential gene expression analysis reveals that CALHM2 knockout reshapes cytokine production, cell adhesion, and signaling pathways in CAR- NKs. These data directly and systematically map out endogenous factors that naturally limit NK cell function in the TME to offer a broad range of cellular genetic checkpoints as candidates for future engineering to enhance NK cell-based immunotherapies.
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16
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Han H, Best AJ, Braunschweig U, Mikolajewicz N, Li JD, Roth J, Chowdhury F, Mantica F, Nabeel-Shah S, Parada G, Brown KR, O'Hanlon D, Wei J, Yao Y, Zid AA, Comsa LC, Jen M, Wang J, Datti A, Gonatopoulos-Pournatzis T, Weatheritt RJ, Greenblatt JF, Wrana JL, Irimia M, Gingras AC, Moffat J, Blencowe BJ. Systematic exploration of dynamic splicing networks reveals conserved multistage regulators of neurogenesis. Mol Cell 2022; 82:2982-2999.e14. [PMID: 35914530 PMCID: PMC10686216 DOI: 10.1016/j.molcel.2022.06.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 04/16/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022]
Abstract
Alternative splicing (AS) is a critical regulatory layer; yet, factors controlling functionally coordinated splicing programs during developmental transitions are poorly understood. Here, we employ a screening strategy to identify factors controlling dynamic splicing events important for mammalian neurogenesis. Among previously unknown regulators, Rbm38 acts widely to negatively control neural AS, in part through interactions mediated by the established repressor of splicing, Ptbp1. Puf60, a ubiquitous factor, is surprisingly found to promote neural splicing patterns. This activity requires a conserved, neural-differential exon that remodels Puf60 co-factor interactions. Ablation of this exon rewires distinct AS networks in embryonic stem cells and at different stages of mouse neurogenesis. Single-cell transcriptome analyses further reveal distinct roles for Rbm38 and Puf60 isoforms in establishing neuronal identity. Our results describe important roles for previously unknown regulators of neurogenesis and establish how an alternative exon in a widely expressed splicing factor orchestrates temporal control over cell differentiation.
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Affiliation(s)
- Hong Han
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
| | - Andrew J Best
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | | | | | - Jack Daiyang Li
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jonathan Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Fuad Chowdhury
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Federica Mantica
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Dr. Aiguader, 88, Barcelona 08003, Spain
| | - Syed Nabeel-Shah
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Guillermo Parada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Dave O'Hanlon
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Jiarun Wei
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Yuxi Yao
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Abdelrahman Abou Zid
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | - Lim Caden Comsa
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Mark Jen
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Jenny Wang
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Alessandro Datti
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Thomas Gonatopoulos-Pournatzis
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Center for Cancer Research National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Robert J Weatheritt
- EMBL Australia, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia; St. Vincent Clinical School, University of New South Wales, Darlinghurst, NSW 2010, Australia
| | - Jack F Greenblatt
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jeffrey L Wrana
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Manuel Irimia
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Dr. Aiguader, 88, Barcelona 08003, Spain; Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
| | - Benjamin J Blencowe
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
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17
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Peng L, Fang Z, Renauer PA, McNamara A, Park JJ, Lin Q, Zhou X, Dong MB, Zhu B, Zhao H, Wilen CB, Chen S. Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species. Cell Rep 2022; 40:111160. [PMID: 35921835 PMCID: PMC9294034 DOI: 10.1016/j.celrep.2022.111160] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/07/2022] [Accepted: 07/13/2022] [Indexed: 12/02/2022] Open
Abstract
Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.
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Affiliation(s)
- Lei Peng
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Zhenhao Fang
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Paul A Renauer
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Molecular Cell Biology, Genetics and Development Program, Yale University, New Haven, CT 06516, USA
| | - Andrew McNamara
- Department of Immunobiology, Yale University, New Haven, CT 06510, USA; Department of Laboratory Medicine, Yale University, New Haven, CT 06510, USA
| | - Jonathan J Park
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA
| | - Qianqian Lin
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Xiaoyu Zhou
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Matthew B Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Department of Immunobiology, Yale University, New Haven, CT 06510, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA; Immunobiology Program, Yale University, New Haven, CT 06510, USA
| | - Biqing Zhu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
| | - Hongyu Zhao
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA; Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06510, USA; Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Craig B Wilen
- Department of Immunobiology, Yale University, New Haven, CT 06510, USA; Department of Laboratory Medicine, Yale University, New Haven, CT 06510, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Molecular Cell Biology, Genetics and Development Program, Yale University, New Haven, CT 06516, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA; Immunobiology Program, Yale University, New Haven, CT 06510, USA; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA; Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT 06510, USA; Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT 06510, USA.
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18
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Peng L, Renauer PA, Ökten A, Fang Z, Park JJ, Zhou X, Lin Q, Dong MB, Filler R, Xiong Q, Clark P, Lin C, Wilen CB, Chen S. Variant-specific vaccination induces systems immune responses and potent in vivo protection against SARS-CoV-2. Cell Rep Med 2022; 3:100634. [PMID: 35561673 PMCID: PMC9040489 DOI: 10.1016/j.xcrm.2022.100634] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 03/06/2022] [Accepted: 04/21/2022] [Indexed: 12/27/2022]
Abstract
Lipid nanoparticle (LNP)-mRNA vaccines offer protection against COVID-19; however, multiple variant lineages caused widespread breakthrough infections. Here, we generate LNP-mRNAs specifically encoding wild-type (WT), B.1.351, and B.1.617 SARS-CoV-2 spikes, and systematically study their immune responses. All three LNP-mRNAs induced potent antibody and T cell responses in animal models; however, differences in neutralization activity have been observed between variants. All three vaccines offer potent protection against in vivo challenges of authentic viruses of WA-1, Beta, and Delta variants. Single-cell transcriptomics of WT- and variant-specific LNP-mRNA-vaccinated animals reveal a systematic landscape of immune cell populations and global gene expression. Variant-specific vaccination induces a systemic increase of reactive CD8 T cells and altered gene expression programs in B and T lymphocytes. BCR-seq and TCR-seq unveil repertoire diversity and clonal expansions in vaccinated animals. These data provide assessment of efficacy and direct systems immune profiling of variant-specific LNP-mRNA vaccination in vivo.
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Affiliation(s)
- Lei Peng
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Paul A Renauer
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA; Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
| | - Arya Ökten
- Department of Immunobiology, Yale University, New Haven, CT, USA; Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Zhenhao Fang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Jonathan J Park
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT, USA
| | - Xiaoyu Zhou
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Qianqian Lin
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Matthew B Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA; Department of Immunobiology, Yale University, New Haven, CT, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT, USA; Immunobiology Program, Yale University, New Haven, CT, USA
| | - Renata Filler
- Department of Immunobiology, Yale University, New Haven, CT, USA; Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Qiancheng Xiong
- Department of Cell Biology, Yale University, New Haven, CT, USA; Nanobiology Institute, Yale University, New Haven, CT, USA
| | - Paul Clark
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Chenxiang Lin
- Department of Cell Biology, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Nanobiology Institute, Yale University, New Haven, CT, USA
| | - Craig B Wilen
- Department of Immunobiology, Yale University, New Haven, CT, USA; Department of Laboratory Medicine, Yale University, New Haven, CT, USA.
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; System Biology Institute, Yale University, West Haven, CT, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA; Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT, USA; Immunobiology Program, Yale University, New Haven, CT, USA; Department of Cell Biology, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Nanobiology Institute, Yale University, New Haven, CT, USA; Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA; Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA; Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT, USA.
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19
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Zhang J, Li Y. High-Dimensional Gaussian Graphical Regression Models with Covariates. J Am Stat Assoc 2022; 118:2088-2100. [PMID: 38143787 PMCID: PMC10746132 DOI: 10.1080/01621459.2022.2034632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/20/2022] [Indexed: 10/19/2022]
Abstract
Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the ℓ2 convergence rates and the selection consistency of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.
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Affiliation(s)
- Jingfei Zhang
- Department of Management Science, University of Miami, Coral Gables, FL 33146
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109
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20
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Cote AC, Young HE, Huckins LM. Comparison of confound adjustment methods in the construction of gene co-expression networks. Genome Biol 2022; 23:44. [PMID: 35115012 PMCID: PMC8812044 DOI: 10.1186/s13059-022-02606-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 01/03/2022] [Indexed: 11/23/2022] Open
Abstract
Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.
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Affiliation(s)
- Alanna C. Cote
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Hannah E. Young
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 10468 USA
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21
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Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study. Front Cell Dev Biol 2022; 9:720321. [PMID: 35155440 PMCID: PMC8826544 DOI: 10.3389/fcell.2021.720321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.
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Affiliation(s)
- Kai Yuan
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Guangzhou Laboratory, Guangzhou, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
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22
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Ruffieux H, Fairfax BP, Nassiri I, Vigorito E, Wallace C, Richardson S, Bottolo L. EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies. Am J Hum Genet 2021; 108:983-1000. [PMID: 33909991 PMCID: PMC8206410 DOI: 10.1016/j.ajhg.2021.04.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 04/08/2021] [Indexed: 12/27/2022] Open
Abstract
We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.
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Affiliation(s)
- Hélène Ruffieux
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
| | - Benjamin P Fairfax
- Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Isar Nassiri
- Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Elena Vigorito
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0AW, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Leonardo Bottolo
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK; Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK
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23
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Miller HE, Bishop AJR. Correlation AnalyzeR: functional predictions from gene co-expression correlations. BMC Bioinformatics 2021; 22:206. [PMID: 33879054 PMCID: PMC8056587 DOI: 10.1186/s12859-021-04130-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 04/13/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. RESULTS We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. CONCLUSIONS Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .
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Affiliation(s)
- Henry E Miller
- Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. .,Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.
| | - Alexander J R Bishop
- Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.,Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.,Mays Cancer Center, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA
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24
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Ben-David E, Boocock J, Guo L, Zdraljevic S, Bloom JS, Kruglyak L. Whole-organism eQTL mapping at cellular resolution with single-cell sequencing. eLife 2021; 10:e65857. [PMID: 33734084 PMCID: PMC8062134 DOI: 10.7554/elife.65857] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 03/17/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematode Caenorhabditis elegans that uses single-cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinct C. elegans individuals. We found cell-type-specific trans eQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.
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Affiliation(s)
- Eyal Ben-David
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Department of Biochemistry and Molecular Biology, Institute for Medical Research Israel-Canada, The Hebrew University School of MedicineJerusalemIsrael
| | - James Boocock
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
| | - Longhua Guo
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
| | - Stefan Zdraljevic
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
| | - Joshua S Bloom
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
| | - Leonid Kruglyak
- Department of Human Genetics, Department of Biological Chemistry, and Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
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25
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Umans BD, Battle A, Gilad Y. Where Are the Disease-Associated eQTLs? Trends Genet 2021; 37:109-124. [PMID: 32912663 PMCID: PMC8162831 DOI: 10.1016/j.tig.2020.08.009] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Most disease-associated variants, although located in putatively regulatory regions, do not have detectable effects on gene expression. One explanation could be that we have not examined gene expression in the cell types or conditions that are most relevant for disease. Even large-scale efforts to study gene expression across tissues are limited to human samples obtained opportunistically or postmortem, mostly from adults. In this review we evaluate recent findings and suggest an alternative strategy, drawing on the dynamic and highly context-specific nature of gene regulation. We discuss new technologies that can extend the standard regulatory mapping framework to more diverse, disease-relevant cell types and states.
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Affiliation(s)
- Benjamin D Umans
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Yoav Gilad
- Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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