1
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Adduri A, Kim S. Ornaments for efficient allele-specific expression estimation with bias correction. Am J Hum Genet 2024; 111:1770-1781. [PMID: 39047729 PMCID: PMC11339617 DOI: 10.1016/j.ajhg.2024.06.014] [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: 09/27/2023] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis-regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level.
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Affiliation(s)
- Abhinav Adduri
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Seyoung Kim
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261, USA.
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2
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Skelly DA, Graham JP, Cheng M, Furuta M, Walter A, Stoklasek TA, Yang H, Stearns TM, Poirion O, Zhang JG, Grassmann JDS, Luo D, Flynn WF, Courtois ET, Chang CH, Serreze DV, Menghi F, Reinholdt LG, Liu ET. Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.11.603136. [PMID: 39071392 PMCID: PMC11275897 DOI: 10.1101/2024.07.11.603136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.
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Affiliation(s)
- Daniel A. Skelly
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | - John P. Graham
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | | | - Mayuko Furuta
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Andrew Walter
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | | | | | | | - Olivier Poirion
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Ji-Gang Zhang
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | | | - Diane Luo
- Single Cell Biology Lab, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - William F. Flynn
- Single Cell Biology Lab, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Elise T. Courtois
- Single Cell Biology Lab, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- OB/Gyn Department, UConn Health, Farmington, CT, USA
| | - Chih-Hao Chang
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | - David V. Serreze
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | - Francesca Menghi
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Edison T. Liu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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3
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Zou LS, Cable DM, Barrera-Lopez IA, Zhao T, Murray E, Aryee MJ, Chen F, Irizarry RA. Detection of allele-specific expression in spatial transcriptomics with spASE. Genome Biol 2024; 25:180. [PMID: 38978101 PMCID: PMC11229351 DOI: 10.1186/s13059-024-03317-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.
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Affiliation(s)
- Luli S Zou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Dylan M Cable
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 02139, USA
| | | | - Tongtong Zhao
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Martin J Aryee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Rafael A Irizarry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
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4
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Gurdon B, Yates SC, Csucs G, Groeneboom NE, Hadad N, Telpoukhovskaia M, Ouellette A, Ouellette T, O'Connell KMS, Singh S, Murdy TJ, Merchant E, Bjerke I, Kleven H, Schlegel U, Leergaard TB, Puchades MA, Bjaalie JG, Kaczorowski CC. Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model. Commun Biol 2024; 7:605. [PMID: 38769398 PMCID: PMC11106287 DOI: 10.1038/s42003-024-06242-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
Alzheimer's disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.
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Affiliation(s)
- Brianna Gurdon
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
| | - Sharon C Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Gergely Csucs
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Nicolaas E Groeneboom
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Niran Hadad
- The Jackson Laboratory, Bar Harbor, ME, USA
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | | | - Andrew Ouellette
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
| | - Tionna Ouellette
- The Jackson Laboratory, Bar Harbor, ME, USA
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA
| | - Kristen M S O'Connell
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA
| | - Surjeet Singh
- The Jackson Laboratory, Bar Harbor, ME, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Ingvild Bjerke
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ulrike Schlegel
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B Leergaard
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
| | - Catherine C Kaczorowski
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA.
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA.
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA.
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5
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Racine JJ, Bachman JF, Zhang JG, Misherghi A, Khadour R, Kaisar S, Bedard O, Jenkins C, Abbott A, Forte E, Rainer P, Rosenthal N, Sattler S, Serreze DV. Murine MHC-Deficient Nonobese Diabetic Mice Carrying Human HLA-DQ8 Develop Severe Myocarditis and Myositis in Response to Anti-PD-1 Immune Checkpoint Inhibitor Cancer Therapy. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1287-1306. [PMID: 38426910 PMCID: PMC10984778 DOI: 10.4049/jimmunol.2300841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/08/2024] [Indexed: 03/02/2024]
Abstract
Myocarditis has emerged as an immune-related adverse event of immune checkpoint inhibitor (ICI) cancer therapy associated with significant mortality. To ensure patients continue to safely benefit from life-saving cancer therapy, an understanding of fundamental immunological phenomena underlying ICI myocarditis is essential. We recently developed the NOD-cMHCI/II-/-.DQ8 mouse model that spontaneously develops myocarditis with lower mortality than observed in previous HLA-DQ8 NOD mouse strains. Our strain was rendered murine MHC class I and II deficient using CRISPR/Cas9 technology, making it a genetically clean platform for dissecting CD4+ T cell-mediated myocarditis in the absence of classically selected CD8+ T cells. These mice are highly susceptible to myocarditis and acute heart failure following anti-PD-1 ICI-induced treatment. Additionally, anti-PD-1 administration accelerates skeletal muscle myositis. Using histology, flow cytometry, adoptive transfers, and RNA sequencing analyses, we performed a thorough characterization of cardiac and skeletal muscle T cells, identifying shared and unique characteristics of both populations. Taken together, this report details a mouse model with features of a rare, but highly lethal clinical presentation of overlapping myocarditis and myositis following ICI therapy. This study sheds light on underlying immunological mechanisms in ICI myocarditis and provides the basis for further detailed analyses of diagnostic and therapeutic strategies.
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Affiliation(s)
| | | | | | - Adel Misherghi
- The Jackson Laboratory, Bar Harbor, Maine, USA
- College of the Atlantic, Bar Harbor, Maine, USA
| | - Raheem Khadour
- The Jackson Laboratory, Bar Harbor, Maine, USA
- College of the Atlantic, Bar Harbor, Maine, USA
| | | | | | | | | | | | - Peter Rainer
- Medical University of Graz, Graz, 8053 Austria
- BioTechMed Graz, Graz, Austria
- BKH St. Johann in Tirol, 6380 St. Johann in Tirol, Austria
| | - Nadia Rosenthal
- The Jackson Laboratory, Bar Harbor, Maine, USA
- Imperial College London, London SW7 2AZ, UK
| | - Susanne Sattler
- Imperial College London, London SW7 2AZ, UK
- Medical University of Graz, Graz, 8053 Austria
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6
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Wang CY, Ye YS, Long WH, Li ZL, Zheng H, Lin XR, Zhou W, Tang DH. RNA sequencing and proteomic profiling reveal alterations by MPTP in chronic stomach mucosal injury in tree shrew Chinese (Tupaia belangeri chinensis). Sci Rep 2024; 14:74. [PMID: 38168759 PMCID: PMC10761816 DOI: 10.1038/s41598-023-50820-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin that can cause gastrointestinal ulcers by affecting dopamine levels. Therefore, MPTP has been considered a toxic substance that causes gastric ulcer disease in experimental animals. In this study, tree shrews were used as the animal model of gastric mucosa injury, and MPTP was intraperitoneally injected at a lower MPTP dosage 2 mg/kg/day for 13 weeks, while tree shrews were not injected as the control group. Under the light microscope, local congestion or diffuse bleeding points of gastric mucosa and multiple redness and swelling bleeding symptoms on the inner wall were observed in the treatment group, as well as immune cell infiltration was found in HE staining, but no such phenomenon was observed in the control group. In order to explore the molecular basis of changes in MPTP induced gastric mucosa injury, the transcriptome and proteome data of gastric mucosa were analyzed. We observed significant differences in mRNA and protein expression levels under the influence of MPTP. The changes in mRNA and proteins are related to increased immune infiltration, cellular processes and angiogenesis. More differentially expressed genes play a role in immune function, especially the candidate genes RPL4 and ANXA1 with significant signal and core role. There are also differentially expressed genes that play a role in mucosal injury and shedding, especially candidate genes GAST and DDC with certain signaling and corresponding functions. Understanding the factors and molecular basis that affect the expression of related genes is crucial for coping with Emotionality gastric mucosa injury disease and developing new treatment methods to establish the ability to resist disease.
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Affiliation(s)
- Chen-Yun Wang
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - You-Song Ye
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - Wei-Hu Long
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - Zhe-Li Li
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - Hong Zheng
- Kunming Medical University, 1168 West Chunrong Road, Yuhua Avenue, Chenggong District, Kunming, 650504, Yunnan, People's Republic of China
| | - Xiao-Rui Lin
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - Wei Zhou
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China
| | - Dong-Hong Tang
- Medical Primate Research Center of China, Institute of Medical Biology, Chinese Academy of Medical Sciences/Peking Union Medical College, Kunming, 650118, China.
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7
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Wu EY, Singh NP, Choi K, Zakeri M, Vincent M, Churchill GA, Ackert-Bicknell CL, Patro R, Love MI. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty. Genome Biol 2023; 24:165. [PMID: 37438847 PMCID: PMC10337143 DOI: 10.1186/s13059-023-03003-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/29/2023] [Indexed: 07/14/2023] Open
Abstract
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
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Affiliation(s)
- Euphy Y Wu
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noor P Singh
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | - Mohsen Zakeri
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | | | - Cheryl L Ackert-Bicknell
- Department of Orthopedics, School of Medicine, University of Colorado, Anschutz Campus, Aurora, CO, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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8
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Cook DE, Venkat A, Yelizarov D, Pouliot Y, Chang PC, Carroll A, De La Vega FM. A deep-learning-based RNA-seq germline variant caller. BIOINFORMATICS ADVANCES 2023; 3:vbad062. [PMID: 37416509 PMCID: PMC10320079 DOI: 10.1093/bioadv/vbad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/31/2023] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
Summary RNA sequencing (RNA-seq) can be applied to diverse tasks including quantifying gene expression, discovering quantitative trait loci and identifying gene fusion events. Although RNA-seq can detect germline variants, the complexities of variable transcript abundance, target capture and amplification introduce challenging sources of error. Here, we extend DeepVariant, a deep-learning-based variant caller, to learn and account for the unique challenges presented by RNA-seq data. Our DeepVariant RNA-seq model produces highly accurate variant calls from RNA-sequencing data, and outperforms existing approaches such as Platypus and GATK. We examine factors that influence accuracy, how our model addresses RNA editing events and how additional thresholding can be used to facilitate our models' use in a production pipeline. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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9
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Tyler AL, Spruce C, Kursawe R, Haber A, Ball RL, Pitman WA, Fine AD, Raghupathy N, Walker M, Philip VM, Baker CL, Mahoney JM, Churchill GA, Trowbridge JJ, Stitzel ML, Paigen K, Petkov PM, Carter GW. Variation in histone configurations correlates with gene expression across nine inbred strains of mice. Genome Res 2023; 33:857-871. [PMID: 37217254 PMCID: PMC10519406 DOI: 10.1101/gr.277467.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 05/19/2023] [Indexed: 05/24/2023]
Abstract
The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), as well as DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represents a distinct combination of the four histone modifications. We found that the epigenetic landscape is highly variable across the DO founders and is associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders, suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis-regulatory regions. Finally, we provide a data resource that documents strain-specific variation in the chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice.
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Affiliation(s)
- Anna L Tyler
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Catrina Spruce
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Romy Kursawe
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - Annat Haber
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - Robyn L Ball
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Wendy A Pitman
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Alexander D Fine
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | | | - Michael Walker
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Vivek M Philip
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | | | - J Matthew Mahoney
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Gary A Churchill
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | | | - Michael L Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - Kenneth Paigen
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
| | - Petko M Petkov
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA;
| | - Gregory W Carter
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine 04609, USA
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10
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Aydin S, Pham DT, Zhang T, Keele GR, Skelly DA, Paulo JA, Pankratz M, Choi T, Gygi SP, Reinholdt LG, Baker CL, Churchill GA, Munger SC. Genetic dissection of the pluripotent proteome through multi-omics data integration. CELL GENOMICS 2023; 3:100283. [PMID: 37082146 PMCID: PMC10112288 DOI: 10.1016/j.xgen.2023.100283] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/12/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.
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Affiliation(s)
- Selcan Aydin
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | | | | | | | | | - Ted Choi
- Predictive Biology, Inc., Carlsbad, CA 92010, USA
| | | | - Laura G. Reinholdt
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Christopher L. Baker
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Gary A. Churchill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
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11
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Zhang T, Keele GR, Gyuricza IG, Vincent M, Brunton C, Bell TA, Hock P, Shaw GD, Munger SC, de Villena FPM, Ferris MT, Paulo JA, Gygi SP, Churchill GA. Multi-omics analysis identifies drivers of protein phosphorylation. Genome Biol 2023; 24:52. [PMID: 36944993 PMCID: PMC10031968 DOI: 10.1186/s13059-023-02892-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated. RESULTS We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes. CONCLUSIONS Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
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Affiliation(s)
- Tian Zhang
- Harvard Medical School, Boston, MA, 02115, USA
| | | | | | | | | | - Timothy A Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ginger D Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Martin T Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
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12
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Deshpande D, Chhugani K, Chang Y, Karlsberg A, Loeffler C, Zhang J, Muszyńska A, Munteanu V, Yang H, Rotman J, Tao L, Balliu B, Tseng E, Eskin E, Zhao F, Mohammadi P, P. Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation. Front Genet 2023; 14:997383. [PMID: 36999049 PMCID: PMC10043755 DOI: 10.3389/fgene.2023.997383] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/24/2023] [Indexed: 03/14/2023] Open
Abstract
RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.
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Affiliation(s)
- Dhrithi Deshpande
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Karishma Chhugani
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Yutong Chang
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Aaron Karlsberg
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Caitlin Loeffler
- Department of Computer Science, University of California, Los Angeles, CA, United States
| | - Jinyang Zhang
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
| | - Agata Muszyńska
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Institute of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
| | - Viorel Munteanu
- Department of Computers, Informatics and Microelectronics, Technical University of Moldova, Chisinau, Moldova
| | - Harry Yang
- Department of Microbiology, Immunology and Molecular Genetics, University of California Los Angeles, Los Angeles, CA, United States
| | - Jeremy Rotman
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Laura Tao
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
| | - Brunilda Balliu
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
| | | | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, United States
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Paweł P. Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA, United States
- *Correspondence: Serghei Mangul,
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13
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Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nat Methods 2023; 20:239-247. [PMID: 36646895 DOI: 10.1038/s41592-022-01731-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/28/2022] [Indexed: 01/18/2023]
Abstract
Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
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14
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Sibbesen JA, Eizenga JM, Novak AM, Sirén J, Chang X, Garrison E, Paten B. Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nat Methods 2023; 20:239-247. [PMID: 36646895 DOI: 10.1101/2021.03.26.437240] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/28/2022] [Indexed: 05/24/2023]
Abstract
Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
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Affiliation(s)
| | | | - Adam M Novak
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Jouni Sirén
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Xian Chang
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Erik Garrison
- University of Tennessee Health Science Center, Memphis, TN, USA
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15
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Byers C, Spruce C, Fortin HJ, Hartig EI, Czechanski A, Munger SC, Reinholdt LG, Skelly DA, Baker CL. Genetic control of the pluripotency epigenome determines differentiation bias in mouse embryonic stem cells. EMBO J 2022; 41:e109445. [PMID: 34931323 PMCID: PMC8762565 DOI: 10.15252/embj.2021109445] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/01/2021] [Accepted: 11/16/2021] [Indexed: 02/03/2023] Open
Abstract
Genetically diverse pluripotent stem cells display varied, heritable responses to differentiation cues. Here, we harnessed these disparities through derivation of mouse embryonic stem cells from the BXD genetic reference panel, along with C57BL/6J (B6) and DBA/2J (D2) parental strains, to identify loci regulating cell state transitions. Upon transition to formative pluripotency, B6 stem cells quickly dissolved naïve networks adopting gene expression modules indicative of neuroectoderm lineages, whereas D2 retained aspects of naïve pluripotency. Spontaneous formation of embryoid bodies identified divergent differentiation where B6 showed a propensity toward neuroectoderm and D2 toward definitive endoderm. Genetic mapping identified major trans-acting loci co-regulating chromatin accessibility and gene expression in both naïve and formative pluripotency. These loci distally modulated occupancy of pluripotency factors at hundreds of regulatory elements. One trans-acting locus on Chr 12 primarily impacted chromatin accessibility in embryonic stem cells, while in epiblast-like cells, the same locus subsequently influenced expression of genes enriched for neurogenesis, suggesting early chromatin priming. These results demonstrate genetically determined biases in lineage commitment and identify major regulators of the pluripotency epigenome.
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Affiliation(s)
- Candice Byers
- The Jackson LaboratoryBar HarborMEUSA
- Graduate School of Biomedical SciencesTufts UniversityBostonMAUSA
| | | | - Haley J Fortin
- The Jackson LaboratoryBar HarborMEUSA
- Graduate School of Biomedical SciencesTufts UniversityBostonMAUSA
| | - Ellen I Hartig
- The Jackson LaboratoryBar HarborMEUSA
- Graduate School of Biomedical SciencesTufts UniversityBostonMAUSA
| | | | - Steven C Munger
- The Jackson LaboratoryBar HarborMEUSA
- Graduate School of Biomedical SciencesTufts UniversityBostonMAUSA
| | | | | | - Christopher L Baker
- The Jackson LaboratoryBar HarborMEUSA
- Graduate School of Biomedical SciencesTufts UniversityBostonMAUSA
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16
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Tomihara K, Kawamoto M, Suzuki Y, Katsuma S, Kiuchi T. Masculinizer-induced dosage compensation is achieved by transcriptional downregulation of both copies of Z-linked genes in the silkworm, Bombyx mori. Biol Lett 2022; 18:20220116. [PMID: 36069069 PMCID: PMC9449812 DOI: 10.1098/rsbl.2022.0116] [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: 03/09/2022] [Accepted: 08/15/2022] [Indexed: 11/12/2022] Open
Abstract
The evolution of dosage compensation produces similar expression of sex-linked and autosomal genes in the heterogametic sex. The silkworm (Bombyx mori), a lepidopteran insect, has a female heterogametic WZ sex determination system. A Z-linked gene, Masculinizer (Masc), is the primary determinant of maleness and dosage compensation in B. mori. However, it remains unknown whether one of the two Z chromosomes is inactivated or both Z chromosomes are suppressed in B. mori males. Hence, we performed transcriptome analysis using hybrids between two B. mori strains and analysed allele-specific expression to distinguish these alternatives. Our analysis revealed that genes on both the maternal and paternal Z chromosomes are transcriptionally upregulated in Masc knocked down males. We therefore conclude that both Z chromosomes are transcriptionally downregulated in B. mori males, similar to the system in Caenorhabditis elegans.
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Affiliation(s)
- Kenta Tomihara
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Munetaka Kawamoto
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Susumu Katsuma
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Takashi Kiuchi
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
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17
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Dwyer JR, Racine JJ, Chapman HD, Quinlan A, Presa M, Stafford GA, Schmitz I, Serreze DV. Nfkbid Overexpression in Nonobese Diabetic Mice Elicits Complete Type 1 Diabetes Resistance in Part Associated with Enhanced Thymic Deletion of Pathogenic CD8 T Cells and Increased Numbers and Activity of Regulatory T Cells. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 209:227-237. [PMID: 35760520 PMCID: PMC9365269 DOI: 10.4049/jimmunol.2100558] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
Type 1 diabetes (T1D) in both humans and NOD mice is caused by T cell-mediated autoimmune destruction of pancreatic β cells. Increased frequency or activity of autoreactive T cells and failures of regulatory T cells (Tregs) to control these pathogenic effectors have both been implicated in T1D etiology. Due to the expression of MHC class I molecules on β cells, CD8 T cells represent the ultimate effector population mediating T1D. Developing autoreactive CD8 T cells normally undergo extensive thymic negative selection, but this process is impaired in NOD mice and also likely T1D patients. Previous studies identified an allelic variant of Nfkbid, a NF-κB signal modulator, as a gene strongly contributing to defective thymic deletion of autoreactive CD8 T cells in NOD mice. These previous studies found ablation of Nfkbid in NOD mice using the clustered regularly interspaced short palindromic repeats system resulted in greater thymic deletion of pathogenic CD8 AI4 and NY8.3 TCR transgenic T cells but an unexpected acceleration of T1D onset. This acceleration was associated with reductions in the frequency of peripheral Tregs. In this article, we report transgenic overexpression of Nfkbid in NOD mice also paradoxically results in enhanced thymic deletion of autoreactive CD8 AI4 T cells. However, transgenic elevation of Nfkbid expression also increased the frequency and functional capacity of peripheral Tregs, in part contributing to the induction of complete T1D resistance. Thus, future identification of a pharmaceutical means to enhance Nfkbid expression might ultimately provide an effective T1D intervention approach.
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Affiliation(s)
| | | | | | | | | | | | - Ingo Schmitz
- Department of Molecular Immunology, Ruhr-University, Bochum, Germany
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18
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Deng W, Mou T, Pawitan Y, Vu TN. Quantification of mutant–allele expression at isoform level in cancer from RNA-seq data. NAR Genom Bioinform 2022; 4:lqac052. [PMID: 35855322 PMCID: PMC9278039 DOI: 10.1093/nargab/lqac052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/26/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant–allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant–allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant–allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.
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Affiliation(s)
- Wenjiang Deng
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden
| | - Tian Mou
- School of Biomedical Engineering, Shenzhen University , Shenzhen, China
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden
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19
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Systematic Identification and Expression Analysis of the Auxin Response Factor (ARF) Gene Family in Ginkgo biloba L. Int J Mol Sci 2022; 23:ijms23126754. [PMID: 35743196 PMCID: PMC9223646 DOI: 10.3390/ijms23126754] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/04/2022] [Accepted: 06/14/2022] [Indexed: 12/10/2022] Open
Abstract
Auxin participates in various physiological and molecular response-related developmental processes and is a pivotal hormone that regulates phenotypic formation in plants. Auxin response factors (ARFs) are vital transcription factors that mediate downstream auxin signaling by explicitly binding to auxin-responsive genes' promoters. Here, to investigate the possible developmental regulatory functions of ARFs in Ginkgo biloba, through employing comprehensive bioinformatics, we recognized 15 putative GbARF members. Conserved domains and motifs, gene and protein structure, gene duplication, GO enrichment, transcriptome expression profiles, and qRT-PCR all showed that Group I and III members were highly conserved. Among them, GbARF10b and GbARF10a were revealed as transcriptional activators in the auxin response for the development of Ginkgo male flowers through sequences alignment, cis-elements analysis and GO annotation; the results were corroborated for the treatment of exogenous SA. Moreover, the GbARFs expansion occurred predominantly by segmental duplication, and most GbARFs have undergone purifying selection. The Ka/Ks ratio test identified the functional consistence of GbARF2a and GbARF2c, GbARF10b, and GbARF10a in tissue expression profiles and male flower development. In summary, our study established a new research basis for exploring Ginkgo GbARF members' roles in floral organ development and hormone response.
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20
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Gerdes Gyuricza I, Chick JM, Keele GR, Deighan AG, Munger SC, Korstanje R, Gygi SP, Churchill GA. Genome-wide transcript and protein analysis highlights the role of protein homeostasis in the aging mouse heart. Genome Res 2022; 32:838-852. [PMID: 35277432 PMCID: PMC9104701 DOI: 10.1101/gr.275672.121] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 03/09/2022] [Indexed: 11/25/2022]
Abstract
Investigation of the molecular mechanisms of aging in the human heart is challenging because of confounding factors, such as diet and medications, as well as limited access to tissues from healthy aging individuals. The laboratory mouse provides an ideal model to study aging in healthy individuals in a controlled environment. However, previous mouse studies have examined only a narrow range of the genetic variation that shapes individual differences during aging. Here, we analyze transcriptome and proteome data from 185 genetically diverse male and female mice at ages 6, 12, and 18 mo to characterize molecular changes that occur in the aging heart. Transcripts and proteins reveal activation of pathways related to exocytosis and cellular transport with age, whereas processes involved in protein folding decrease with age. Additional changes are apparent only in the protein data including reduced fatty acid oxidation and increased autophagy. For proteins that form complexes, we see a decline in correlation between their component subunits with age, suggesting age-related loss of stoichiometry. The most affected complexes are themselves involved in protein homeostasis, which potentially contributes to a cycle of progressive breakdown in protein quality control with age. Our findings highlight the important role of post-transcriptional regulation in aging. In addition, we identify genetic loci that modulate age-related changes in protein homeostasis, suggesting that genetic variation can alter the molecular aging process.
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Affiliation(s)
| | - Joel M Chick
- Vividion Therapeutics, San Diego, California 92121, USA
| | | | | | | | - Ron Korstanje
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Steven P Gygi
- Harvard Medical School, Boston, Massachusetts 02115, USA
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21
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Thomas SM, Ackert-Bicknell CL, Zuscik MJ, Payne KA. Understanding the Transcriptomic Landscape to Drive New Innovations in Musculoskeletal Regenerative Medicine. Curr Osteoporos Rep 2022; 20:141-152. [PMID: 35156183 DOI: 10.1007/s11914-022-00726-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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/18/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW RNA-sequencing (RNA-seq) is a novel and highly sought-after tool in the field of musculoskeletal regenerative medicine. The technology is being used to better understand pathological processes, as well as elucidate mechanisms governing development and regeneration. It has allowed in-depth characterization of stem cell populations and discovery of molecular mechanisms that regulate stem cell development, maintenance, and differentiation in a way that was not possible with previous technology. This review introduces RNA-seq technology and how it has paved the way for advances in musculoskeletal regenerative medicine. RECENT FINDINGS Recent studies in regenerative medicine have utilized RNA-seq to decipher mechanisms of pathophysiology and identify novel targets for regenerative medicine. The technology has also advanced stem cell biology through in-depth characterization of stem cells, identifying differentiation trajectories and optimizing cell culture conditions. It has also provided new knowledge that has led to improved growth factor use and scaffold design for musculoskeletal regenerative medicine. This article reviews recent studies utilizing RNA-seq in the field of musculoskeletal regenerative medicine. It demonstrates how transcriptomic analysis can be used to provide insights that can aid in formulating a regenerative strategy.
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Affiliation(s)
- Stacey M Thomas
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Mail Stop 8343, 12800 East 19th Avenue, Aurora, CO, 80045, USA
| | - Cheryl L Ackert-Bicknell
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Mail Stop 8343, 12800 East 19th Avenue, Aurora, CO, 80045, USA
| | - Michael J Zuscik
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Mail Stop 8343, 12800 East 19th Avenue, Aurora, CO, 80045, USA
| | - Karin A Payne
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Mail Stop 8343, 12800 East 19th Avenue, Aurora, CO, 80045, USA.
- Gates Center for Regenerative Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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22
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Zhabotynsky V, Huang L, Little P, Hu YJ, Pardo-Manuel de Villena F, Zou F, Sun W. eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects. PLoS Genet 2022; 18:e1010076. [PMID: 35286297 PMCID: PMC8947591 DOI: 10.1371/journal.pgen.1010076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 03/24/2022] [Accepted: 02/03/2022] [Indexed: 12/15/2022] Open
Abstract
Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.
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Affiliation(s)
- Vasyl Zhabotynsky
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- * E-mail: (VZ); (WS)
| | - Licai Huang
- Quantitative Sciences Program, The University of Texas MD Anderson Cancer Center and UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Paul Little
- Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Yi-Juan Hu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Wei Sun
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
- * E-mail: (VZ); (WS)
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23
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Lee JY, Davis I, Youth EHH, Kim J, Churchill G, Godwin J, Korstanje R, Beck S. Misexpression of genes lacking CpG islands drives degenerative changes during aging. SCIENCE ADVANCES 2021; 7:eabj9111. [PMID: 34910517 PMCID: PMC8673774 DOI: 10.1126/sciadv.abj9111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/26/2021] [Indexed: 05/14/2023]
Abstract
Cellular aging is characterized by disruption of the nuclear lamina and its associated heterochromatin. How these structural changes within the nucleus contribute to age-related degeneration of the organism is unclear. Genes lacking CpG islands (CGI− genes) generally associate with heterochromatin when they are inactive. Here, we show that the expression of these genes is globally activated in aged cells and tissues. This CGI− gene misexpression is a common feature of normal and pathological aging in mice and humans. We report evidence that CGI− gene up-regulation is directly responsible for age-related physiological deterioration, notably for increased secretion of inflammatory mediators.
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Affiliation(s)
- Jun-Yeong Lee
- Davis Center for Regenerative Biology and Medicine, MDI Biological Laboratory, Bar Harbor, ME 04609, USA
| | - Ian Davis
- Davis Center for Regenerative Biology and Medicine, MDI Biological Laboratory, Bar Harbor, ME 04609, USA
| | - Elliot H. H. Youth
- Davis Center for Regenerative Biology and Medicine, MDI Biological Laboratory, Bar Harbor, ME 04609, USA
- Brown University, Providence, RI 02912, USA
| | - Jonghwan Kim
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | | | - James Godwin
- Davis Center for Regenerative Biology and Medicine, MDI Biological Laboratory, Bar Harbor, ME 04609, USA
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Samuel Beck
- Davis Center for Regenerative Biology and Medicine, MDI Biological Laboratory, Bar Harbor, ME 04609, USA
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24
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Sands B, Yun S, Mendenhall AR. Introns control stochastic allele expression bias. Nat Commun 2021; 12:6527. [PMID: 34764277 PMCID: PMC8585970 DOI: 10.1038/s41467-021-26798-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 10/19/2021] [Indexed: 01/26/2023] Open
Abstract
Monoallelic expression (MAE) or extreme allele bias can account for incomplete penetrance, missing heritability and non-Mendelian diseases. In cancer, MAE is associated with shorter patient survival times and higher tumor grade. Prior studies showed that stochastic MAE is caused by stochastic epigenetic silencing, in a gene and tissue-specific manner. Here, we used C. elegans to study stochastic MAE in vivo. We found allele bias/MAE to be widespread within C. elegans tissues, presenting as a continuum from fully biallelic to MAE. We discovered that the presence of introns within alleles robustly decreases MAE. We determined that introns control MAE at distinct loci, in distinct cell types, with distinct promoters, and within distinct coding sequences, using a 5'-intron position-dependent mechanism. Bioinformatic analysis showed human intronless genes are significantly enriched for MAE. Our experimental evidence demonstrates a role for introns in regulating MAE, possibly explaining why some mutations within introns result in disease.
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Affiliation(s)
- Bryan Sands
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
| | - Soo Yun
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
| | - Alexander R. Mendenhall
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
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25
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Wu W, Lovett JL, Shedden K, Strassmann BI, Vincenz C. Targeted RNA-seq improves efficiency, resolution, and accuracy of allele specific expression for human term placentas. G3 (BETHESDA, MD.) 2021; 11:jkab176. [PMID: 34009305 PMCID: PMC8496276 DOI: 10.1093/g3journal/jkab176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/12/2021] [Indexed: 12/30/2022]
Abstract
Genomic imprinting is an epigenetic mechanism that results in allele-specific expression (ASE) based on the parent of origin. It is known to play a role in the prenatal and postnatal allocation of maternal resources in mammals. ASE detected by whole transcriptome RNA-seq (wht-RNAseq) has been widely used to analyze imprinted genes using reciprocal crosses in mice to generate large numbers of informative SNPs. Studies in humans are more challenging due to the paucity of SNPs and the poor preservation of RNA in term placentas and other tissues. Targeted RNA-seq (tar-RNAseq) can potentially mitigate these challenges by focusing sequencing resources on the regions of interest in the transcriptome. Here, we compared tar-RNAseq and wht-RNAseq in a study of ASE in known imprinted genes in placental tissue collected from a healthy human cohort in Mali, West Africa. As expected, tar-RNAseq substantially improved the coverage of SNPs. Compared to wht-RNAseq, tar-RNAseq produced on average four times more SNPs in twice as many genes per sample and read depth at the SNPs increased fourfold. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that tar-RNAseq reduces this limitation as it unexpectedly increased the concordance of ASE between SNPs of the same gene, even in cases of degraded RNA. Studies aimed at discovering associations between individual variation in ASE and phenotypes in mammals and flowering plants will benefit from the improved power and accuracy of tar-RNAseq.
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Affiliation(s)
- Weisheng Wu
- BRCF Bioinformatics Core, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jennie L Lovett
- Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Beverly I Strassmann
- Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI 48106, USA
| | - Claudius Vincenz
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI 48106, USA
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Abstract
Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.
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Affiliation(s)
- Siobhan Cleary
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
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27
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Statistical Modeling of High Dimensional Counts. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2284:97-134. [PMID: 33835440 DOI: 10.1007/978-1-0716-1307-8_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Statistical modeling of count data from RNA sequencing (RNA-seq) experiments is important for proper interpretation of results. Here I will describe how count data can be modeled using count distributions, or alternatively analyzed using nonparametric methods. I will focus on basic routines for performing data input, scaling/normalization, visualization, and statistical testing to determine sets of features where the counts reflect differences in gene expression across samples. Finally, I discuss limitations and possible extensions to the models presented here.
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28
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Takemon Y, Wright V, Davenport B, Gatti DM, Sheehan SM, Letson K, Savage HS, Lennon R, Korstanje R. Uncovering Modifier Genes of X-Linked Alport Syndrome Using a Novel Multiparent Mouse Model. J Am Soc Nephrol 2021; 32:1961-1973. [PMID: 34045313 PMCID: PMC8455275 DOI: 10.1681/asn.2020060777] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 03/27/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Mutations in COL4A5 are responsible for 80% of cases of X-linked Alport Syndrome (XLAS). Although genes that cause AS are well characterized, people with AS who have similar genetic mutations present with a wide variation in the extent of kidney impairment and age of onset, suggesting the activities of modifier genes. METHODS We created a cohort of genetically diverse XLAS male and female mice using the Diversity Outbred mouse resource and measured albuminuria, GFR, and gene expression. Using a quantitative trait locus approach, we mapped modifier genes that can best explain the underlying phenotypic variation measured in our diverse population. RESULTS Genetic analysis identified several loci associated with the variation in albuminuria and GFR, including a locus on the X chromosome associated with X inactivation and a locus on chromosome 2 containing Fmn1. Subsequent analysis of genetically reduced Fmn1 expression in Col4a5 knockout mice showed a decrease in albuminuria, podocyte effacement, and podocyte protrusions in the glomerular basement membrane, which support the candidacy of Fmn1 as a modifier gene for AS. CONCLUSION With this novel approach, we emulated the variability in the severity of kidney phenotypes found in human patients with Alport Syndrome through albuminuria and GFR measurements. This approach can identify modifier genes in kidney disease that can be used as novel therapeutic targets.
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Affiliation(s)
| | | | - Bernard Davenport
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | | | | | | | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
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29
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Zhang Z, van Dijk F, de Klein N, van Gijn ME, Franke LH, Sinke RJ, Swertz MA, van der Velde KJ. Feasibility of predicting allele specific expression from DNA sequencing using machine learning. Sci Rep 2021; 11:10606. [PMID: 34012022 PMCID: PMC8134421 DOI: 10.1038/s41598-021-89904-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/04/2021] [Indexed: 11/09/2022] Open
Abstract
Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.
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Affiliation(s)
- Zhenhua Zhang
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Freerk van Dijk
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- Prinses Maxima Center for Child Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Niek de Klein
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Mariëlle E van Gijn
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Lude H Franke
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Richard J Sinke
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - K Joeri van der Velde
- Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
- Department of Genetics, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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30
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Sabik OL, Calabrese GM, Taleghani E, Ackert-Bicknell CL, Farber CR. Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data. Cell Rep 2021; 32:108145. [PMID: 32937138 DOI: 10.1016/j.celrep.2020.108145] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/31/2020] [Accepted: 08/21/2020] [Indexed: 12/12/2022] Open
Abstract
The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.
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Affiliation(s)
- Olivia L Sabik
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Gina M Calabrese
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Eric Taleghani
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Cheryl L Ackert-Bicknell
- Center for Musculoskeletal Research, University of Rochester Medical Center, University of Rochester, Rochester, NY 14624, USA
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
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31
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Takemon Y, Chick JM, Gerdes Gyuricza I, Skelly DA, Devuyst O, Gygi SP, Churchill GA, Korstanje R. Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney. eLife 2021; 10:e62585. [PMID: 33687326 PMCID: PMC8096428 DOI: 10.7554/elife.62585] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/06/2021] [Indexed: 01/10/2023] Open
Abstract
Little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand these changes, we measured mRNA and protein levels in genetically diverse mice at different ages. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.
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Affiliation(s)
| | - Joel M Chick
- Harvard Medical SchoolBostonUnited States
- VividionTherapeuticsSan DiegoUnited States
| | | | | | - Olivier Devuyst
- Institute of Physiology, University of ZurichZurichSwitzerland
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32
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Sarkar H, Srivastava A, Bravo HC, Love MI, Patro R. Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data. Bioinformatics 2021; 36:i102-i110. [PMID: 32657377 PMCID: PMC7355257 DOI: 10.1093/bioinformatics/btaa448] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Advances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects. RESULTS We introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. AVAILABILITY AND IMPLEMENTATION Terminus is implemented in Rust, and is freely available and open source. It can be obtained from https://github.com/COMBINE-lab/Terminus. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hirak Sarkar
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Avi Srivastava
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Héctor Corrada Bravo
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA.,Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA.,Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
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33
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Yang HS, Onos KD, Choi K, Keezer KJ, Skelly DA, Carter GW, Howell GR. Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer's disease. Cell Rep 2021; 34:108739. [PMID: 33567283 PMCID: PMC7937391 DOI: 10.1016/j.celrep.2021.108739] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 11/09/2020] [Accepted: 01/15/2021] [Indexed: 02/07/2023] Open
Abstract
Genetic and genome-wide association studies suggest a central role for microglia in Alzheimer’s disease (AD). However, single-cell RNA sequencing (scRNA-seq) of microglia in mice, a key preclinical model, has shown mixed results regarding translatability to human studies. To address this, scRNA-seq of microglia from C57BL/6J (B6) and wild-derived strains (WSB/EiJ, CAST/EiJ, and PWK/PhJ) with and without APP/PS1 demonstrates that genetic diversity significantly alters features and dynamics of microglia in baseline neuroimmune functions and in response to amyloidosis. Results show significant variation in the abundance of microglial subtypes or states, including numbers of previously identified disease-associated and interferon-responding microglia, across the strains. For each subtype, significant differences in the expression of many genes are observed in wild-derived strains relative to B6, including 19 genes previously associated with human AD including Apoe, Trem2, and Sorl1. This resource is critical in the development of appropriately targeted therapeutics for AD and other neurological diseases. Neuroinflammation is a key component of Alzheimer’s disease. Yang et al. perform single-cell sequencing of microglia in wild-derived mouse strains that carry amyloid and show that these strains differ from the commonly used strains, exhibiting significant variation in abundance of microglial subtypes, including numbers of disease-associated and interferon-responding microglia.
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Affiliation(s)
| | | | | | | | | | - Gregory W Carter
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA
| | - Gareth R Howell
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA.
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34
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aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals. J Mol Biol 2021; 433:166829. [PMID: 33508309 DOI: 10.1016/j.jmb.2021.166829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 01/08/2021] [Accepted: 01/09/2021] [Indexed: 02/06/2023]
Abstract
In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.
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35
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Cechova M. Probably Correct: Rescuing Repeats with Short and Long Reads. Genes (Basel) 2020; 12:48. [PMID: 33396198 PMCID: PMC7823596 DOI: 10.3390/genes12010048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 02/07/2023] Open
Abstract
Ever since the introduction of high-throughput sequencing following the human genome project, assembling short reads into a reference of sufficient quality posed a significant problem as a large portion of the human genome-estimated 50-69%-is repetitive. As a result, a sizable proportion of sequencing reads is multi-mapping, i.e., without a unique placement in the genome. The two key parameters for whether or not a read is multi-mapping are the read length and genome complexity. Long reads are now able to span difficult, heterochromatic regions, including full centromeres, and characterize chromosomes from "telomere to telomere". Moreover, identical reads or repeat arrays can be differentiated based on their epigenetic marks, such as methylation patterns, aiding in the assembly process. This is despite the fact that long reads still contain a modest percentage of sequencing errors, disorienting the aligners and assemblers both in accuracy and speed. Here, I review the proposed and implemented solutions to the repeat resolution and the multi-mapping read problem, as well as the downstream consequences of reference choice, repeat masking, and proper representation of sex chromosomes. I also consider the forthcoming challenges and solutions with regards to long reads, where we expect the shift from the problem of repeat localization within a single individual to the problem of repeat positioning within pangenomes.
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Affiliation(s)
- Monika Cechova
- Genetics and Reproductive Biotechnologies, Veterinary Research Institute, Central European Institute of Technology (CEITEC), 621 00 Brno, Czech Republic
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36
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Vangala P, Murphy R, Quinodoz SA, Gellatly K, McDonel P, Guttman M, Garber M. High-Resolution Mapping of Multiway Enhancer-Promoter Interactions Regulating Pathogen Detection. Mol Cell 2020; 80:359-373.e8. [PMID: 32991830 PMCID: PMC7572724 DOI: 10.1016/j.molcel.2020.09.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 06/04/2020] [Accepted: 09/04/2020] [Indexed: 11/19/2022]
Abstract
Eukaryotic gene expression regulation involves thousands of distal regulatory elements. Understanding the quantitative contribution of individual enhancers to gene expression is critical for assessing the role of disease-associated genetic risk variants. Yet, we lack the ability to accurately link genes with their distal regulatory elements. To address this, we used 3D enhancer-promoter (E-P) associations identified using split-pool recognition of interactions by tag extension (SPRITE) to build a predictive model of gene expression. Our model dramatically outperforms models using genomic proximity and can be used to determine the quantitative impact of enhancer loss on gene expression in different genetic backgrounds. We show that genes that form stable E-P hubs have less cell-to-cell variability in gene expression. Finally, we identified transcription factors that regulate stimulation-dependent E-P interactions. Together, our results provide a framework for understanding quantitative contributions of E-P interactions and associated genetic variants to gene expression.
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Affiliation(s)
- Pranitha Vangala
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Rachel Murphy
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Sofia A Quinodoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Kyle Gellatly
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Patrick McDonel
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Mitchell Guttman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Manuel Garber
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA; Department of Dermatology, Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA.
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37
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Dunn AR, Hadad N, Neuner SM, Zhang JG, Philip VM, Dumitrescu L, Hohman TJ, Herskowitz JH, O’Connell KMS, Kaczorowski CC. Identifying Mechanisms of Normal Cognitive Aging Using a Novel Mouse Genetic Reference Panel. Front Cell Dev Biol 2020; 8:562662. [PMID: 33042997 PMCID: PMC7517308 DOI: 10.3389/fcell.2020.562662] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 08/17/2020] [Indexed: 12/18/2022] Open
Abstract
Developing strategies to maintain cognitive health is critical to quality of life during aging. The basis of healthy cognitive aging is poorly understood; thus, it is difficult to predict who will have normal cognition later in life. Individuals may have higher baseline functioning (cognitive reserve) and others may maintain or even improve with age (cognitive resilience). Understanding the mechanisms underlying cognitive reserve and resilience may hold the key to new therapeutic strategies for maintaining cognitive health. However, reserve and resilience have been inconsistently defined in human studies. Additionally, our understanding of the molecular and cellular bases of these phenomena is poor, compounded by a lack of longitudinal molecular and cognitive data that fully capture the dynamic trajectories of cognitive aging. Here, we used a genetically diverse mouse population (B6-BXDs) to characterize individual differences in cognitive abilities in adulthood and investigate evidence of cognitive reserve and/or resilience in middle-aged mice. We tested cognitive function at two ages (6 months and 14 months) using y-maze and contextual fear conditioning. We observed heritable variation in performance on these traits (h 2 RIx̄ = 0.51-0.74), suggesting moderate to strong genetic control depending on the cognitive domain. Due to the polygenetic nature of cognitive function, we did not find QTLs significantly associated with y-maze, contextual fear acquisition (CFA) or memory, or decline in cognitive function at the genome-wide level. To more precisely interrogate the molecular regulation of variation in these traits, we employed RNA-seq and identified gene networks related to transcription/translation, cellular metabolism, and neuronal function that were associated with working memory, contextual fear memory, and cognitive decline. Using this method, we nominate the Trio gene as a modulator of working memory ability. Finally, we propose a conceptual framework for identifying strains exhibiting cognitive reserve and/or resilience to assess whether these traits can be observed in middle-aged B6-BXDs. Though we found that earlier cognitive reserve evident early in life protects against cognitive impairment later in life, cognitive performance and age-related decline fell along a continuum, with no clear genotypes emerging as exemplars of exceptional reserve or resilience - leading to recommendations for future use of aging mouse populations to understand the nature of cognitive reserve and resilience.
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Affiliation(s)
- Amy R. Dunn
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Niran Hadad
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Sarah M. Neuner
- The Jackson Laboratory, Bar Harbor, ME, United States
- Department of Anatomy and Neurobiology, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ji-Gang Zhang
- The Jackson Laboratory, Bar Harbor, ME, United States
| | | | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jeremy H. Herskowitz
- Center for Neurodegeneration and Experimental Therapeutics and Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, United States
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38
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Ortmann D, Brown S, Czechanski A, Aydin S, Muraro D, Huang Y, Tomaz RA, Osnato A, Canu G, Wesley BT, Skelly DA, Stegle O, Choi T, Churchill GA, Baker CL, Rugg-Gunn PJ, Munger SC, Reinholdt LG, Vallier L. Naive Pluripotent Stem Cells Exhibit Phenotypic Variability that Is Driven by Genetic Variation. Cell Stem Cell 2020; 27:470-481.e6. [PMID: 32795399 PMCID: PMC7487768 DOI: 10.1016/j.stem.2020.07.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 04/10/2020] [Accepted: 07/24/2020] [Indexed: 12/11/2022]
Abstract
Variability among pluripotent stem cell (PSC) lines is a prevailing issue that hampers not only experimental reproducibility but also large-scale applications and personalized cell-based therapy. This variability could result from epigenetic and genetic factors that influence stem cell behavior. Naive culture conditions minimize epigenetic fluctuation, potentially overcoming differences in PSC line differentiation potential. Here we derived PSCs from distinct mouse strains under naive conditions and show that lines from distinct genetic backgrounds have divergent differentiation capacity, confirming a major role for genetics in PSC phenotypic variability. This is explained in part through inconsistent activity of extra-cellular signaling, including the Wnt pathway, which is modulated by specific genetic variants. Overall, this study shows that genetic background plays a dominant role in driving phenotypic variability of PSCs.
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Affiliation(s)
- Daniel Ortmann
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK.
| | - Stephanie Brown
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | | | | | - Daniele Muraro
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Yuanhua Huang
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Rute A Tomaz
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Anna Osnato
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Giovanni Canu
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Brandon T Wesley
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | | | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK; European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research, Center (DKFZ), Heidelberg, Germany
| | - Ted Choi
- Jackson Laboratory, Bar Harbor, ME, USA
| | | | | | - Peter J Rugg-Gunn
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Epigenetics Programme, Babraham Institute, Cambridge, UK
| | | | | | - Ludovic Vallier
- Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK.
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39
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Liang ZS, Cimino I, Yalcin B, Raghupathy N, Vancollie VE, Ibarra-Soria X, Firth HV, Rimmington D, Farooqi IS, Lelliott CJ, Munger SC, O’Rahilly S, Ferguson-Smith AC, Coll AP, Logan DW. Trappc9 deficiency causes parent-of-origin dependent microcephaly and obesity. PLoS Genet 2020; 16:e1008916. [PMID: 32877400 PMCID: PMC7467316 DOI: 10.1371/journal.pgen.1008916] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 06/08/2020] [Indexed: 11/30/2022] Open
Abstract
Some imprinted genes exhibit parental origin specific expression bias rather than being transcribed exclusively from one copy. The physiological relevance of this remains poorly understood. In an analysis of brain-specific allele-biased expression, we identified that Trappc9, a cellular trafficking factor, was expressed predominantly (~70%) from the maternally inherited allele. Loss-of-function mutations in human TRAPPC9 cause a rare neurodevelopmental syndrome characterized by microcephaly and obesity. By studying Trappc9 null mice we discovered that homozygous mutant mice showed a reduction in brain size, exploratory activity and social memory, as well as a marked increase in body weight. A role for Trappc9 in energy balance was further supported by increased ad libitum food intake in a child with TRAPPC9 deficiency. Strikingly, heterozygous mice lacking the maternal allele (70% reduced expression) had pathology similar to homozygous mutants, whereas mice lacking the paternal allele (30% reduction) were phenotypically normal. Taken together, we conclude that Trappc9 deficient mice recapitulate key pathological features of TRAPPC9 mutations in humans and identify a role for Trappc9 and its imprinting in controlling brain development and metabolism.
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Affiliation(s)
- Zhengzheng S. Liang
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Irene Cimino
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Binnaz Yalcin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université de Strasbourg, France
| | | | | | - Ximena Ibarra-Soria
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Helen V. Firth
- Department of Clinical Genetics, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Debra Rimmington
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - I. Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, United Kingdom
| | | | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Stephen O’Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | | | - Anthony P. Coll
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Darren W. Logan
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom
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40
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Heuer SE, Neuner SM, Hadad N, O'Connell KMS, Williams RW, Philip VM, Gaiteri C, Kaczorowski CC. Identifying the molecular systems that influence cognitive resilience to Alzheimer's disease in genetically diverse mice. ACTA ACUST UNITED AC 2020; 27:355-371. [PMID: 32817302 PMCID: PMC7433658 DOI: 10.1101/lm.051839.120] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/10/2020] [Indexed: 12/23/2022]
Abstract
Individual differences in cognitive decline during normal aging and Alzheimer's disease (AD) are common, but the molecular mechanisms underlying these distinct outcomes are not fully understood. We utilized a combination of genetic, molecular, and behavioral data from a mouse population designed to model human variation in cognitive outcomes to search for the molecular mechanisms behind this population-wide variation. Specifically, we used a systems genetics approach to relate gene expression to cognitive outcomes during AD and normal aging. Statistical causal-inference Bayesian modeling was used to model systematic genetic perturbations matched with cognitive data that identified astrocyte and microglia molecular networks as drivers of cognitive resilience to AD. Using genetic mapping, we identified Fgf2 as a potential regulator of the astrocyte network associated with individual differences in short-term memory. We also identified several immune genes as regulators of a microglia network associated with individual differences in long-term memory, which was partly mediated by amyloid burden. Finally, significant overlap between mouse and two different human coexpression networks provided strong evidence of translational relevance for the genetically diverse AD-BXD panel as a model of late-onset AD. Together, this work identified two candidate molecular pathways enriched for microglia and astrocyte genes that serve as causal AD cognitive biomarkers, and provided a greater understanding of processes that modulate individual and population-wide differences in cognitive outcomes during AD.
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Affiliation(s)
- Sarah E Heuer
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,Tufts University School of Graduate Biomedical Sciences, Boston, Massachusetts 02111, USA
| | - Sarah M Neuner
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - Niran Hadad
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Robert W Williams
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | | | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois 60612, USA
| | - Catherine C Kaczorowski
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,Tufts University School of Graduate Biomedical Sciences, Boston, Massachusetts 02111, USA
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41
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Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency. Cell Stem Cell 2020; 27:459-469.e8. [PMID: 32795400 DOI: 10.1016/j.stem.2020.07.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 02/07/2020] [Accepted: 07/02/2020] [Indexed: 12/23/2022]
Abstract
Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states in vitro.
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42
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Deschamps-Francoeur G, Simoneau J, Scott MS. Handling multi-mapped reads in RNA-seq. Comput Struct Biotechnol J 2020; 18:1569-1576. [PMID: 32637053 PMCID: PMC7330433 DOI: 10.1016/j.csbj.2020.06.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/06/2020] [Accepted: 06/07/2020] [Indexed: 11/07/2022] Open
Abstract
Many eukaryotic genomes harbour large numbers of duplicated sequences, of diverse biotypes, resulting from several mechanisms including recombination, whole genome duplication and retro-transposition. Such repeated sequences complicate gene/transcript quantification during RNA-seq analysis due to reads mapping to more than one locus, sometimes involving genes embedded in other genes. Genes of different biotypes have dissimilar levels of sequence duplication, with long-noncoding RNAs and messenger RNAs sharing less sequence similarity to other genes than biotypes encoding shorter RNAs. Many strategies have been elaborated to handle these multi-mapped reads, resulting in increased accuracy in gene/transcript quantification, although separate tools are typically used to estimate the abundance of short and long genes due to their dissimilar characteristics. This review discusses the mechanisms leading to sequence duplication, the biotypes affected, the computational strategies employed to deal with multi-mapped reads and the challenges that still remain to be overcome.
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Affiliation(s)
- Gabrielle Deschamps-Francoeur
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
| | - Joël Simoneau
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
| | - Michelle S. Scott
- Département de Biochimie et Génomique Fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
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43
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Eizenga JM, Novak AM, Sibbesen JA, Heumos S, Ghaffaari A, Hickey G, Chang X, Seaman JD, Rounthwaite R, Ebler J, Rautiainen M, Garg S, Paten B, Marschall T, Sirén J, Garrison E. Pangenome Graphs. Annu Rev Genomics Hum Genet 2020; 21:139-162. [PMID: 32453966 DOI: 10.1146/annurev-genom-120219-080406] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Low-cost whole-genome assembly has enabled the collection of haplotype-resolved pangenomes for numerous organisms. In turn, this technological change is encouraging the development of methods that can precisely address the sequence and variation described in large collections of related genomes. These approaches often use graphical models of the pangenome to support algorithms for sequence alignment, visualization, functional genomics, and association studies. The additional information provided to these methods by the pangenome allows them to achieve superior performance on a variety of bioinformatic tasks, including read alignment, variant calling, and genotyping. Pangenome graphs stand to become a ubiquitous tool in genomics. Although it is unclear whether they will replace linearreference genomes, their ability to harmoniously relate multiple sequence and coordinate systems will make them useful irrespective of which pangenomic models become most common in the future.
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Affiliation(s)
- Jordan M Eizenga
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Adam M Novak
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Jonas A Sibbesen
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Simon Heumos
- Quantitative Biology Center, University of Tübingen, 72076 Tübingen, Germany
| | - Ali Ghaffaari
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Xian Chang
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Josiah D Seaman
- Royal Botanic Gardens, Kew, Richmond TW9 3AB, United Kingdom.,School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Robin Rounthwaite
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Jana Ebler
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Mikko Rautiainen
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Saarbrücken Graduate School for Computer Science, Saarland University, 66123 Saarbrücken, Germany
| | - Shilpa Garg
- Departments of Genetics and Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215, USA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Jouni Sirén
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
| | - Erik Garrison
- Genomics Institute, University of California, Santa Cruz, California 95064, USA;
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44
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Genome-wide analysis of spatiotemporal allele-specific expression in F1 hybrids of meat- and egg-type chickens. Gene 2020; 747:144671. [PMID: 32304782 DOI: 10.1016/j.gene.2020.144671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/04/2020] [Accepted: 04/12/2020] [Indexed: 12/20/2022]
Abstract
In diploid organisms, each gene locus is composed of two parental alleles, which would interact with each other for determining the phenotypic variation. Better understanding of the allele-specific expression (ASE) in farm animals is much important to explore the genetic basis underlying economically important traits, which have been poorly understood yet. In this study, genome-wide analysis was applied to explore the spatiotemporal pattern of ASE in the F1 hybrids of chicken. First, meat- and egg-type chickens were selected for producing a full-sib F1 hybrid population (n = 57). Then, genome resequencing of two parents and 38 offspring were performed and liver and breast muscle samples (n = 38) were subjected to strand-specific RNA sequencing (ssRNA-seq) for ASE detection at 1, 28, and 56 days of age, respectively. The results accurately identified a total of 465 informative genes that could be distinguished with respect to their parental origins. There were 0.4% - 4.1% of informative genes showing ASE, and 57 of them were found across different tissues and time points. Besides, most ASE genes in chickens were tissue-specific, and no matter what the time-point pattern of one ASE gene, the same parental allele of this gene almost showed consistently higher or lower expression across all time points in the same type tissue. In conclusion, this study indicated that most of ASE genes were tissue-specific and time-dependent.
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45
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Spruce C, Dlamini S, Ananda G, Bronkema N, Tian H, Paigen K, Carter GW, Baker CL. HELLS and PRDM9 form a pioneer complex to open chromatin at meiotic recombination hot spots. Genes Dev 2020; 34:398-412. [PMID: 32001511 PMCID: PMC7050486 DOI: 10.1101/gad.333542.119] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 12/27/2019] [Indexed: 12/16/2022]
Abstract
In this study, Spruce et al. investigated access to recombination hot spots during meiosis and show that the chromatin remodeler HELLS and DNA-binding protein PRDM9 function together to open chromatin at hot spots and provide access for the DNA double-strand break (DSB) machinery. Their data provide a model for hot spot activation in which HELLS and PRDM9 form a pioneer complex to create a unique epigenomic environment of open chromatin, permitting correct placement and repair of DSBs. Chromatin barriers prevent spurious interactions between regulatory elements and DNA-binding proteins. One such barrier, whose mechanism for overcoming is poorly understood, is access to recombination hot spots during meiosis. Here we show that the chromatin remodeler HELLS and DNA-binding protein PRDM9 function together to open chromatin at hot spots and provide access for the DNA double-strand break (DSB) machinery. Recombination hot spots are decorated by a unique combination of histone modifications not found at other regulatory elements. HELLS is recruited to hot spots by PRDM9 and is necessary for both histone modifications and DNA accessibility at hot spots. In male mice lacking HELLS, DSBs are retargeted to other sites of open chromatin, leading to germ cell death and sterility. Together, these data provide a model for hot spot activation in which HELLS and PRDM9 form a pioneer complex to create a unique epigenomic environment of open chromatin, permitting correct placement and repair of DSBs.
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Affiliation(s)
| | | | | | | | - Hui Tian
- The Jackson Laboratory, Bar Harbor, Maine 04660, USA
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46
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Abstract
Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism, and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimates for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of three different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates, and integrated it into the apeglm package. The three methods were evaluated on both simulated and real data. Apeglm consistently performed better than ML according to a variety of criteria, including mean absolute error and concordance at the top. While ash had lower error and greater concordance than ML on the simulations, it also had a tendency to over-shrink large effects, and performed worse on the real data according to error and concordance. Furthermore, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.
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Affiliation(s)
- Joshua P Zitovsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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47
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Abstract
Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimators for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use pseudocounts or Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of four different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, adding a pseudocount to each allele, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates and integrated it into the apeglm package. The four methods were evaluated on two simulations and one real data set. Apeglm consistently performed better than ML according to a variety of criteria, and generally outperformed use of pseudocounts as well. Ash also performed better than ML in one of the simulations, but in the other performance was more mixed. Finally, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster and more numerically reliable, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.
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Affiliation(s)
- Joshua P. Zitovsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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48
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da Silva Francisco Junior R, Dos Santos Ferreira C, Santos E Silva JC, Terra Machado D, Côrtes Martins Y, Ramos V, Simões Carnivali G, Garcia AB, Medina-Acosta E. Pervasive Inter-Individual Variation in Allele-Specific Expression in Monozygotic Twins. Front Genet 2019; 10:1178. [PMID: 31850058 PMCID: PMC6887657 DOI: 10.3389/fgene.2019.01178] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/24/2019] [Indexed: 01/19/2023] Open
Abstract
Despite being developed from one zygote, heterokaryotypic monozygotic (MZ) co-twins exhibit discordant karyotypes. Epigenomic studies in biological samples from heterokaryotypic MZ co-twins are of the most significant value for assessing the effects on gene- and allele-specific expression of an extranumerary chromosomal copy or structural chromosomal disparities in otherwise nearly identical germline genetic contributions. Here, we use RNA-Seq data from existing repositories to establish within-pair correlations for the breadth and magnitude of allele-specific expression (ASE) in heterokaryotypic MZ co-twins discordant for trisomy 21 and maternal 21q inheritance, as well as homokaryotypic co-twins. We show that there is a genome-wide disparity at ASE sites between the heterokaryotypic MZ co-twins. Although most of the disparity corresponds to changes in the magnitude of biallelic imbalance, ASE sites switching from either strictly monoallelic to biallelic imbalance or the reverse occur in few genes that are known or predicted to be imprinted, subject to X-chromosome inactivation or A-to-I(G) RNA edited. We also uncovered comparable ASE differences between homokaryotypic MZ twins. The extent of ASE discordance in MZ twins (2.7%) was about 10-fold lower than the expected between pairs of unrelated, non-twin males or females. The results indicate that the observed within-pair dissimilarities in breadth and magnitude of ASE sites in the heterokaryotypic MZ co-twins could not solely be attributable to the aneuploidy and the missing allelic heritability at 21q.
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Affiliation(s)
| | - Cristina Dos Santos Ferreira
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Juan Carlo Santos E Silva
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Douglas Terra Machado
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Yasmmin Côrtes Martins
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Victor Ramos
- Department of Genetics, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Gustavo Simões Carnivali
- Department of Computational Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Beatriz Garcia
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Enrique Medina-Acosta
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
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Choi K, Raghupathy N, Churchill GA. A Bayesian mixture model for the analysis of allelic expression in single cells. Nat Commun 2019; 10:5188. [PMID: 31729374 PMCID: PMC6858378 DOI: 10.1038/s41467-019-13099-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 10/09/2019] [Indexed: 11/09/2022] Open
Abstract
Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression. Here, we report a method for ASE analysis from single-cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells. We further demonstrate that combining information across cells using a hierarchical mixture model reduces sampling variability without sacrificing cell-to-cell heterogeneity. We applied our approach to re-evaluate the statistical independence of allelic bursting and track changes in the allele-specific expression patterns of cells sampled over a developmental time course.
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Affiliation(s)
- Kwangbom Choi
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | | | - Gary A Churchill
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA.
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Salavati M, Bush SJ, Palma-Vera S, McCulloch MEB, Hume DA, Clark EL. Elimination of Reference Mapping Bias Reveals Robust Immune Related Allele-Specific Expression in Crossbred Sheep. Front Genet 2019; 10:863. [PMID: 31608110 PMCID: PMC6761296 DOI: 10.3389/fgene.2019.00863] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/19/2019] [Indexed: 12/13/2022] Open
Abstract
Pervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript, we describe an unbiased standardized computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open license. The analysis pipeline we present is designed to minimize reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel×Scottish Blackface sheep, using the sheep gene expression atlas data set. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited, and instead, they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programs for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq data sets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterization of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling to provide both a novel analysis of the multi-dimensional sheep gene expression atlas data set and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.
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Affiliation(s)
- Mazdak Salavati
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh, United Kingdom
| | - Stephen J. Bush
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh, United Kingdom
| | - Sergio Palma-Vera
- Leibniz Institute for Farm Animal Biology (FBN), Institute for Reproductive Biology, Dummerstorf, Germany
| | - Mary E. B. McCulloch
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh, United Kingdom
| | - David A. Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Emily L. Clark
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh, United Kingdom
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