1
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Lu X, Ni P, Suarez-Meade P, Ma Y, Forrest EN, Wang G, Wang Y, Quiñones-Hinojosa A, Gerstein M, Jiang YH. Transcriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes. Cell Rep 2024; 43:114376. [PMID: 38900637 DOI: 10.1016/j.celrep.2024.114376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/08/2024] [Accepted: 05/31/2024] [Indexed: 06/22/2024] Open
Abstract
Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3-mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We apply an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in humans and mice. We unexpectedly discover an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts are altered in Shank3-mutant mice and postmortem brain tissues from individuals with autism spectrum disorder. The enhanced SHANK3 transcriptome significantly improves the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest that both deterministic and stochastic transcription of the genome is associated with SHANK family genes.
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Affiliation(s)
- Xiaona Lu
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yu Ma
- Department of Neurology, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Emily Niemitz Forrest
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Guilin Wang
- Keck Microarray Shared Resource, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yi Wang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai 201102, China
| | | | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA; Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
| | - Yong-Hui Jiang
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA; Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; Pediatrics, Yale University School of Medicine, New Haven, CT 06520, USA.
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2
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024:10.1038/s12276-024-01243-w. [PMID: 38871816 DOI: 10.1038/s12276-024-01243-w] [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: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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3
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Ma Y, Xiao Y, Xiao Z, Li J. Development of DNA Insertion-specific Markers Based on the Intergenic Region of Oplegnathus punctatus Cdkn1/srsf3 for Sex Identification. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2024:10.1007/s10126-024-10336-6. [PMID: 38874827 DOI: 10.1007/s10126-024-10336-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 06/15/2024]
Abstract
Spotted knifejaw (Oplegnathus punctatus) is a marine economic fish with high food and ecological value, and its growth process has obvious male and female sexual dimorphism, with males growing significantly faster than females. However, the current sex identification technology is not yet mature, which will limit the growth rate of O. punctatus aquaculture and the efficiency of separate sex breeding, so the development of efficient sex molecular markers is imperative. This study identified a 926 bp DNA insertion fragment in the cdkn1/srsf3 intergenic region of O. punctatus males through whole-genome scanning, comparative genomics, and structural variant analysis. A pair of primers was designed based on the insertion information of the Y chromosome intergenic region in male individuals. Agarose gel electrophoresis revealed the amplification of two DNA fragments, 1118 bp and 192 bp, in male O. punctatus individuals. The 926 bp fragment was identified as the insertion in the intergenic region of cdkn1/srsf3 in males, while only a single 192 bp DNA fragment was amplified in females. The biological sex of the individuals identified in this manner was consistent with their known phenotypic sex. In this study, we developed a method to detect DNA insertion variants in the intergenic region of O. punctatus. Additionally, we introduced a new DNA marker for the rapid identification of the sex of O. punctatus, which enhances detection efficiency. The text has important reference significance and application value in sex identification, all-male breeding, and lineage selection. It provides new insights into the regulation of variation in the intergenic region of cdkn1/srsf3 genes and the study of RNA shearing.
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Affiliation(s)
- Yuting Ma
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, China
| | - Yongshuang Xiao
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, China.
| | - Zhizhong Xiao
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, China
| | - Jun Li
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, China.
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4
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Zhang J, Wang Q, Qi S, Duan Y, Liu Z, Liu J, Zhang Z, Li C. An oncogenic enhancer promotes melanoma progression via regulating ETV4 expression. J Transl Med 2024; 22:547. [PMID: 38849954 PMCID: PMC11157841 DOI: 10.1186/s12967-024-05356-8] [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: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Enhancers are important gene regulatory elements that promote the expression of critical genes in development and disease. Aberrant enhancer can modulate cancer risk and activate oncogenes that lead to the occurrence of various cancers. However, the underlying mechanism of most enhancers in cancer remains unclear. Here, we aim to explore the function and mechanism of a crucial enhancer in melanoma. METHODS Multi-omics data were applied to identify an enhancer (enh17) involved in melanoma progression. To evaluate the function of enh17, CRISPR/Cas9 technology were applied to knockout enh17 in melanoma cell line A375. RNA-seq, ChIP-seq and Hi-C data analysis integrated with luciferase reporter assay were performed to identify the potential target gene of enh17. Functional experiments were conducted to further validate the function of the target gene ETV4. Multi-omics data integrated with CUT&Tag sequencing were performed to validate the binding profile of the inferred transcription factor STAT3. RESULTS An enhancer, named enh17 here, was found to be aberrantly activated and involved in melanoma progression. CRISPR/Cas9-mediated deletion of enh17 inhibited cell proliferation, migration, and tumor growth of melanoma both in vitro and in vivo. Mechanistically, we identified ETV4 as a target gene regulated by enh17, and functional experiments further support ETV4 as a target gene that is involved in cancer-associated phenotypes. In addition, STAT3 acts as a transcription factor binding with enh17 to regulate the transcription of ETV4. CONCLUSIONS Our findings revealed that enh17 plays an oncogenic role and promotes tumor progression in melanoma, and its transcriptional regulatory mechanisms were fully elucidated, which may open a promising window for melanoma prevention and treatment.
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Affiliation(s)
- Junyou Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Qilin Wang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Sihan Qi
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yingying Duan
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhaoshuo Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jiaxin Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ziyi Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Chunyan Li
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China.
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China.
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5
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Loupe JM, Anderson AG, Rizzardi LF, Rodriguez-Nunez I, Moyers B, Trausch-Lowther K, Jain R, Bunney WE, Bunney BG, Cartagena P, Sequeira A, Watson SJ, Akil H, Cooper GM, Myers RM. Multiomic profiling of transcription factor binding and function in human brain. Nat Neurosci 2024:10.1038/s41593-024-01658-8. [PMID: 38831039 DOI: 10.1038/s41593-024-01658-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 04/19/2024] [Indexed: 06/05/2024]
Abstract
Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.
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Affiliation(s)
- Jacob M Loupe
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | - Lindsay F Rizzardi
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Biochemistry and Molecular Biology, The University of Alabama in Birmingham, Birmingham, AL, USA
| | | | - Belle Moyers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | - Rashmi Jain
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - William E Bunney
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Blynn G Bunney
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Preston Cartagena
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Adolfo Sequeira
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Stanley J Watson
- The Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | - Huda Akil
- The Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | | | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
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6
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Li Y, Tan M, Akkari-Henić A, Zhang L, Kip M, Sun S, Sepers JJ, Xu N, Ariyurek Y, Kloet SL, Davis RP, Mikkers H, Gruber JJ, Snyder MP, Li X, Pang B. Genome-wide Cas9-mediated screening of essential non-coding regulatory elements via libraries of paired single-guide RNAs. Nat Biomed Eng 2024:10.1038/s41551-024-01204-8. [PMID: 38778183 DOI: 10.1038/s41551-024-01204-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/27/2024] [Indexed: 05/25/2024]
Abstract
The functions of non-coding regulatory elements (NCREs), which constitute a major fraction of the human genome, have not been systematically studied. Here we report a method involving libraries of paired single-guide RNAs targeting both ends of an NCRE as a screening system for the Cas9-mediated deletion of thousands of NCREs genome-wide to study their functions in distinct biological contexts. By using K562 and 293T cell lines and human embryonic stem cells, we show that NCREs can have redundant functions, and that many ultra-conserved elements have silencer activity and play essential roles in cell growth and in cellular responses to drugs (notably, the ultra-conserved element PAX6_Tarzan may be critical for heart development, as removing it from human embryonic stem cells led to defects in cardiomyocyte differentiation). The high-throughput screen, which is compatible with single-cell sequencing, may allow for the identification of druggable NCREs.
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Affiliation(s)
- Yufeng Li
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Minkang Tan
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Almira Akkari-Henić
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Limin Zhang
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Maarten Kip
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Shengnan Sun
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jorian J Sepers
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ningning Xu
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yavuz Ariyurek
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Susan L Kloet
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard P Davis
- Department of Anatomy and Embryology, The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, the Netherlands
| | - Harald Mikkers
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Joshua J Gruber
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Xiao Li
- Department of Biochemistry, The Center for RNA Science and Therapeutics, Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Baoxu Pang
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
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7
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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8
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Walter NG. Are non-protein coding RNAs junk or treasure?: An attempt to explain and reconcile opposing viewpoints of whether the human genome is mostly transcribed into non-functional or functional RNAs. Bioessays 2024; 46:e2300201. [PMID: 38351661 DOI: 10.1002/bies.202300201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 03/28/2024]
Abstract
The human genome project's lasting legacies are the emerging insights into human physiology and disease, and the ascendance of biology as the dominant science of the 21st century. Sequencing revealed that >90% of the human genome is not coding for proteins, as originally thought, but rather is overwhelmingly transcribed into non-protein coding, or non-coding, RNAs (ncRNAs). This discovery initially led to the hypothesis that most genomic DNA is "junk", a term still championed by some geneticists and evolutionary biologists. In contrast, molecular biologists and biochemists studying the vast number of transcripts produced from most of this genome "junk" often surmise that these ncRNAs have biological significance. What gives? This essay contrasts the two opposing, extant viewpoints, aiming to explain their bases, which arise from distinct reference frames of the underlying scientific disciplines. Finally, it aims to reconcile these divergent mindsets in hopes of stimulating synergy between scientific fields.
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Affiliation(s)
- Nils G Walter
- Center for RNA Biomedicine, Single Molecule Analysis Group, Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA
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9
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Kumar Halder A, Agarwal A, Jodkowska K, Plewczynski D. A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction. Brief Funct Genomics 2024:elae009. [PMID: 38555493 DOI: 10.1093/bfgp/elae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/02/2024] Open
Abstract
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.
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Affiliation(s)
- Anup Kumar Halder
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Abhishek Agarwal
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Karolina Jodkowska
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Dariusz Plewczynski
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
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10
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Lu X, Ni P, Suarez-Meade P, Ma Y, Forrest EN, Wang G, Wang Y, Quiñones-Hinojosa A, Gerstein M, Jiang YH. Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585480. [PMID: 38562714 PMCID: PMC10983920 DOI: 10.1101/2024.03.18.585480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.
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Affiliation(s)
- Xiaona Lu
- Department of Genetics, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Pengyu Ni
- Biomedical Informatics & Data Science, Yale University School of Medicine New Haven, CT, 06520 USA
| | | | - Yu Ma
- Department of Neurology, Children’s Hospital of Fudan University, Shanghai, 201102 China
| | | | - Guilin Wang
- Yale Center for Genome Analysis, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Yi Wang
- Department of Neurology, Children’s Hospital of Fudan University, Shanghai, 201102 China
| | | | - Mark Gerstein
- Biomedical Informatics & Data Science, Yale University School of Medicine New Haven, CT, 06520 USA
- Yale Center for Genome Analysis, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Yong-hui Jiang
- Department of Genetics, Yale University School of Medicine New Haven, CT, 06520 USA
- Neuroscienc, Yale University School of Medicine New Haven, CT, 06520 USA
- Pediatrics, Yale University School of Medicine New Haven, CT, 06520 USA
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11
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Willemin A, Szabó D, Pombo A. Epigenetic regulatory layers in the 3D nucleus. Mol Cell 2024; 84:415-428. [PMID: 38242127 PMCID: PMC10872226 DOI: 10.1016/j.molcel.2023.12.032] [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: 07/26/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/21/2024]
Abstract
Nearly 7 decades have elapsed since Francis Crick introduced the central dogma of molecular biology, as part of his ideas on protein synthesis, setting the fundamental rules of sequence information transfer from DNA to RNAs and proteins. We have since learned that gene expression is finely tuned in time and space, due to the activities of RNAs and proteins on regulatory DNA elements, and through cell-type-specific three-dimensional conformations of the genome. Here, we review major advances in genome biology and discuss a set of ideas on gene regulation and highlight how various biomolecular assemblies lead to the formation of structural and regulatory features within the nucleus, with roles in transcriptional control. We conclude by suggesting further developments that will help capture the complex, dynamic, and often spatially restricted events that govern gene expression in mammalian cells.
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Affiliation(s)
- Andréa Willemin
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Architecture Group, Berlin, Germany; Humboldt-Universität zu Berlin, Institute for Biology, Berlin, Germany.
| | - Dominik Szabó
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Architecture Group, Berlin, Germany; Humboldt-Universität zu Berlin, Institute for Biology, Berlin, Germany
| | - Ana Pombo
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Architecture Group, Berlin, Germany; Humboldt-Universität zu Berlin, Institute for Biology, Berlin, Germany.
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12
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Teboul L, Hérault Y, Wells S, Pavlovic G. How much do we know about the function of mammalian genes? BMC Biol 2023; 21:301. [PMID: 38155353 PMCID: PMC10755963 DOI: 10.1186/s12915-023-01794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/30/2023] Open
Affiliation(s)
- Lydia Teboul
- The Mary Lyon Centre at MRC Harwell, Harwell Campus, Didcot, OX11 0RD, Oxon, UK.
| | - Yann Hérault
- PHENOMIN-Institut Clinique de La Souris, CELPHEDIA, CNRS, INSERM, Université de Strasbourg, Illkirch-Grafenstaden, 67404, Strasbourg, France
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique Et de Biologie Moléculaire Et Cellulaire (IGBMC), 1 Rue Laurent Fries, 67404, Illkirch Graffenstaden, France
| | - Sara Wells
- The Mary Lyon Centre at MRC Harwell, Harwell Campus, Didcot, OX11 0RD, Oxon, UK
| | - Guillaume Pavlovic
- PHENOMIN-Institut Clinique de La Souris, CELPHEDIA, CNRS, INSERM, Université de Strasbourg, Illkirch-Grafenstaden, 67404, Strasbourg, France
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13
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Khan MA. HLA-B*27 and Ankylosing Spondylitis: 50 Years of Insights and Discoveries. Curr Rheumatol Rep 2023; 25:327-340. [PMID: 37950822 DOI: 10.1007/s11926-023-01118-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2023]
Abstract
PURPOSE OF REVIEW To commemorate the 50th anniversary of the groundbreaking discovery of a remarkably strong association between HLA-B*27 and ankylosing spondylitis (AS). RECENT FINDINGS In addition to HLA-B*27, more than 116 other recognized genetic risk variants have been identified, while epigenetic factors largely remain unexplored in this context. Among patients with AS who carry the HLA-B*27 gene, clonally expanded CD8 + T cells can be found in their bloodstream and within inflamed tissues. Moreover, the α and β chain motifs of these T-cell receptors demonstrate a distinct affinity for certain self- and microbial-derived peptides, leading to an autoimmune response that ultimately results in the onset of the disease. These distinctive peptide-binding and presentation characteristics are a hallmark of the disease-associated HLA-B*27:05 subtype but are absent in HLA-B*27:09, a subtype not associated with the disease, differing by only a single amino acid. This discovery represents a significant advancement in unraveling the 50-year-old puzzle of how HLA-B*27 contributes to the development of AS. These findings will significantly accelerate the process of identifying peptides, both self- and microbial-derived, that instigate autoimmunity. This, in return, will pave the way for the development of more accurate and effective targeted treatments. Moreover, the discovery of improved biomarkers, in conjunction with the emerging technology of electric field molecular fingerprinting, has the potential to greatly bolster early diagnosis capabilities. A very recently published groundbreak paper underscores the remarkable effectiveness of targeting and eliminating disease-causing T cells in a HLA-B*27 patients with AS. This pivotal advancement not only signifies a paradigm shift but also bolsters the potential for preventing the disease in individuals carrying high-risk genetic variants.
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Affiliation(s)
- Muhammad A Khan
- Case Western Reserve School of Medicine, Cleveland, OH, USA.
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14
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Malhotra S, Mulvaney T, Cragnolini T, Sidhu H, Joseph A, Beton J, Topf M. RIBFIND2: Identifying rigid bodies in protein and nucleic acid structures. Nucleic Acids Res 2023; 51:9567-9575. [PMID: 37670532 PMCID: PMC10570027 DOI: 10.1093/nar/gkad721] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 08/10/2023] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
Molecular structures are often fitted into cryo-EM maps by flexible fitting. When this requires large conformational changes, identifying rigid bodies can help optimize the model-map fit. Tools for identifying rigid bodies in protein structures exist, however an equivalent for nucleic acid structures is lacking. With the increase in cryo-EM maps containing RNA and progress in RNA structure prediction, there is a need for such tools. We previously developed RIBFIND, a program for clustering protein secondary structures into rigid bodies. In RIBFIND2, this approach is extended to nucleic acid structures. RIBFIND2 can identify biologically relevant rigid bodies in important groups of complex RNA structures, capturing a wide range of dynamics, including large rigid-body movements. The usefulness of RIBFIND2-assigned rigid bodies in cryo-EM model refinement was demonstrated on three examples, with two conformations each: Group II Intron complexed IEP, Internal Ribosome Entry Site and the Processome, using cryo-EM maps at 2.7-5 Å resolution. A hierarchical refinement approach, performed on progressively smaller sets of RIBFIND2 rigid bodies, was clearly shown to have an advantage over classical all-atom refinement. RIBFIND2 is available via a web server with structure visualization and as a standalone tool.
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Affiliation(s)
- Sony Malhotra
- Science and Technology Facilities Council, Scientific Computing, Research Complex at Harwell, Didcot OX11 0FA, UK
| | - Thomas Mulvaney
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
- Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg 20246, Germany
| | - Tristan Cragnolini
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London WC1E 7HX, UK
| | - Haneesh Sidhu
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London WC1E 7HX, UK
| | - Agnel P Joseph
- Science and Technology Facilities Council, Scientific Computing, Research Complex at Harwell, Didcot OX11 0FA, UK
| | - Joseph G Beton
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
| | - Maya Topf
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
- Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg 20246, Germany
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15
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Theisen B, Holtz A, Rajagopalan V. Noncoding RNAs and Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes in Cardiac Arrhythmic Brugada Syndrome. Cells 2023; 12:2398. [PMID: 37830612 PMCID: PMC10571919 DOI: 10.3390/cells12192398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/14/2023] Open
Abstract
Hundreds of thousands of people die each year as a result of sudden cardiac death, and many are due to heart rhythm disorders. One of the major causes of these arrhythmic events is Brugada syndrome, a cardiac channelopathy that results in abnormal cardiac conduction, severe life-threatening arrhythmias, and, on many occasions, death. This disorder has been associated with mutations and dysfunction of about two dozen genes; however, the majority of the patients do not have a definite cause for the diagnosis of Brugada Syndrome. The protein-coding genes represent only a very small fraction of the mammalian genome, and the majority of the noncoding regions of the genome are actively transcribed. Studies have shown that most of the loci associated with electrophysiological traits are located in noncoding regulatory regions and are expected to affect gene expression dosage and cardiac ion channel function. Noncoding RNAs serve an expanding number of regulatory and other functional roles within the cells, including but not limited to transcriptional, post-transcriptional, and epigenetic regulation. The major noncoding RNAs found in Brugada Syndrome include microRNAs; however, others such as long noncoding RNAs are also identified. They contribute to pathogenesis by interacting with ion channels and/or are detectable as clinical biomarkers. Stem cells have received significant attention in the recent past, and can be differentiated into many different cell types including those in the heart. In addition to contractile and relaxational properties, BrS-relevant electrophysiological phenotypes are also demonstrated in cardiomyocytes differentiated from stem cells induced from adult human cells. In this review, we discuss the current understanding of noncoding regions of the genome and their RNA biology in Brugada Syndrome. We also delve into the role of stem cells, especially human induced pluripotent stem cell-derived cardiac differentiated cells, in the investigation of Brugada syndrome in preclinical and clinical studies.
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Affiliation(s)
- Benjamin Theisen
- Department of Biomedical and Anatomical Sciences, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, Jonesboro, AR 72401, USA
| | - Austin Holtz
- Department of Biomedical and Anatomical Sciences, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, Jonesboro, AR 72401, USA
| | - Viswanathan Rajagopalan
- Department of Biomedical and Anatomical Sciences, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, Jonesboro, AR 72401, USA
- Arkansas Biosciences Institute, Jonesboro, AR 72401, USA
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16
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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17
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Laub V, Devraj K, Elias L, Schulte D. Bioinformatics for wet-lab scientists: practical application in sequencing analysis. BMC Genomics 2023; 24:382. [PMID: 37420172 DOI: 10.1186/s12864-023-09454-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. CONCLUSION Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.
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Affiliation(s)
- Vera Laub
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany.
| | - Kavi Devraj
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, Telangana, India
| | - Lena Elias
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Dorothea Schulte
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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18
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Michael AK, Stoos L, Crosby P, Eggers N, Nie XY, Makasheva K, Minnich M, Healy KL, Weiss J, Kempf G, Cavadini S, Kater L, Seebacher J, Vecchia L, Chakraborty D, Isbel L, Grand RS, Andersch F, Fribourgh JL, Schübeler D, Zuber J, Liu AC, Becker PB, Fierz B, Partch CL, Menet JS, Thomä NH. Cooperation between bHLH transcription factors and histones for DNA access. Nature 2023; 619:385-393. [PMID: 37407816 PMCID: PMC10338342 DOI: 10.1038/s41586-023-06282-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 06/02/2023] [Indexed: 07/07/2023]
Abstract
The basic helix-loop-helix (bHLH) family of transcription factors recognizes DNA motifs known as E-boxes (CANNTG) and includes 108 members1. Here we investigate how chromatinized E-boxes are engaged by two structurally diverse bHLH proteins: the proto-oncogene MYC-MAX and the circadian transcription factor CLOCK-BMAL1 (refs. 2,3). Both transcription factors bind to E-boxes preferentially near the nucleosomal entry-exit sites. Structural studies with engineered or native nucleosome sequences show that MYC-MAX or CLOCK-BMAL1 triggers the release of DNA from histones to gain access. Atop the H2A-H2B acidic patch4, the CLOCK-BMAL1 Per-Arnt-Sim (PAS) dimerization domains engage the histone octamer disc. Binding of tandem E-boxes5-7 at endogenous DNA sequences occurs through direct interactions between two CLOCK-BMAL1 protomers and histones and is important for circadian cycling. At internal E-boxes, the MYC-MAX leucine zipper can also interact with histones H2B and H3, and its binding is indirectly enhanced by OCT4 elsewhere on the nucleosome. The nucleosomal E-box position and the type of bHLH dimerization domain jointly determine the histone contact, the affinity and the degree of competition and cooperativity with other nucleosome-bound factors.
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Affiliation(s)
- Alicia K Michael
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Lisa Stoos
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Priya Crosby
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Nikolas Eggers
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-Universität, Munich, Germany
| | - Xinyu Y Nie
- Department of Biology, Center for Biological Clock Research, Texas A&M University, College Station, TX, USA
| | - Kristina Makasheva
- Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Martina Minnich
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Kelly L Healy
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Joscha Weiss
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Georg Kempf
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Simone Cavadini
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Lukas Kater
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Jan Seebacher
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Luca Vecchia
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Deyasini Chakraborty
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Luke Isbel
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Ralph S Grand
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Florian Andersch
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Jennifer L Fribourgh
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Dirk Schübeler
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Johannes Zuber
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
- Medical University of Vienna, Vienna, Austria
| | - Andrew C Liu
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Peter B Becker
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-Universität, Munich, Germany
| | - Beat Fierz
- Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Carrie L Partch
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jerome S Menet
- Department of Biology, Center for Biological Clock Research, Texas A&M University, College Station, TX, USA
| | - Nicolas H Thomä
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
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19
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Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP, Dong MX, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas MJ, Marinescu VD, Wang C, Wallerman O, Xue J, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins LM, Lawler A, Keough KC, Zheng Z, Zeng J, Wray NR, Li Y, Johnson J, Chen J, Paten B, Reilly SK, Hughes GM, Weng Z, Pollard KS, Pfenning AR, Forsberg-Nilsson K, Karlsson EK, Lindblad-Toh K. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 2023; 380:eabn2937. [PMID: 37104612 PMCID: PMC10259825 DOI: 10.1126/science.abn2937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/09/2023] [Indexed: 04/29/2023]
Abstract
Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
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Affiliation(s)
- Patrick F. Sullivan
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - BaDoi N. Phan
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Xue Li
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Diane P. Genereux
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Michael X. Dong
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Matteo Bianchi
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Gregory Andrews
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Sharadha Sakthikumar
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Jessika Nordin
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ananya Roy
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Matthew J. Christmas
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Voichita D. Marinescu
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Chao Wang
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - Ola Wallerman
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
| | - James Xue
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Center for System Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Quan Sun
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jin Szatkiewicz
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Laura M. Huckins
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alyssa Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kathleen C. Keough
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Yun Li
- Department of Genetics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | - Jessica Johnson
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina Medical School, Chapel Hill, NC 27599, USA
| | | | - Benedict Paten
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95064, USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Graham M. Hughes
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Katherine S. Pollard
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Karin Forsberg-Nilsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | - Elinor K. Karlsson
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, 75132 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
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20
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Fisher WW, Hammonds AS, Weiszmann R, Booth BW, Gevirtzman L, Patton JEJ, Kubo CA, Waterston RH, Celniker SE. A modERN resource: identification of Drosophila transcription factor candidate target genes using RNAi. Genetics 2023; 223:iyad004. [PMID: 36652461 PMCID: PMC10078917 DOI: 10.1093/genetics/iyad004] [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: 11/18/2022] [Revised: 11/18/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023] Open
Abstract
Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change -3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies (ERR, sens, and zfh2) and two previously uncharacterized TFs (sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC (http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.
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Affiliation(s)
- William W Fisher
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ann S Hammonds
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Richard Weiszmann
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Benjamin W Booth
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Louis Gevirtzman
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Jaeda E J Patton
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Connor A Kubo
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Robert H Waterston
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Susan E Celniker
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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21
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Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP, Dong MX, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas MJ, Marinescu VD, Wallerman O, Xue JR, Li Y, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins LM, Lawler AJ, Keough KC, Zheng Z, Zeng J, Wray NR, Johnson J, Chen J, Paten B, Reilly SK, Hughes GM, Weng Z, Pollard KS, Pfenning AR, Forsberg-Nilsson K, Karlsson EK, Lindblad-Toh K. Leveraging Base Pair Mammalian Constraint to Understand Genetic Variation and Human Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.531987. [PMID: 36945512 PMCID: PMC10028973 DOI: 10.1101/2023.03.10.531987] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
Although thousands of genomic regions have been associated with heritable human diseases, attempts to elucidate biological mechanisms are impeded by a general inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function that is agnostic to cell type or disease mechanism. Here, single base phyloP scores from the whole genome alignment of 240 placental mammals identified 3.5% of the human genome as significantly constrained, and likely functional. We compared these scores to large-scale genome annotation, genome-wide association studies (GWAS), copy number variation, clinical genetics findings, and cancer data sets. Evolutionarily constrained positions are enriched for variants explaining common disease heritability (more than any other functional annotation). Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
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Affiliation(s)
- Patrick F. Sullivan
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Steven Gazal
- Keck School of Medicine, University of Southern California; Los Angeles, CA 90033, USA
| | - BaDoi N. Phan
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Medical Scientist Training Program, University of Pittsburgh School of Medicine; Pittsburgh, PA 15261, USA
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Xue Li
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Morningside Graduate School of Biomedical Sciences, UMass Chan Medical School; Worcester, MA 01605, USA
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | | | - Michael X. Dong
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Matteo Bianchi
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Gregory Andrews
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Sharadha Sakthikumar
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
| | - Jessika Nordin
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
| | - Ananya Roy
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
| | - Matthew J. Christmas
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Voichita D. Marinescu
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - Ola Wallerman
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
| | - James R. Xue
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Department of Organismic and Evolutionary Biology, Harvard University; Cambridge, MA 02138, USA
| | - Yun Li
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Stockholm, Sweden
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, USA
| | - Jin Szatkiewicz
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina Medical School; Chapel Hill, NC 27599, USA
| | - Laura M. Huckins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Alyssa J. Lawler
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Kathleen C. Keough
- Department of Epidemiology & Biostatistics, University of California San Francisco; San Francisco, CA 94158, USA
- Fauna Bio Incorporated; Emeryville, CA 94608, USA
- Gladstone Institutes; San Francisco, CA 94158, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland; Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland; Brisbane, Queensland, Australia
| | - Jessica Johnson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, USA
| | | | - Benedict Paten
- Genomics Institute, University of California Santa Cruz; Santa Cruz, CA 95064, USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine; New Haven, CT 06510, USA
| | - Graham M. Hughes
- School of Biology and Environmental Science, University College Dublin; Belfield, Dublin 4, Ireland
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Katherine S. Pollard
- Department of Epidemiology & Biostatistics, University of California San Francisco; San Francisco, CA 94158, USA
- Gladstone Institutes; San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub; San Francisco, CA 94158, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University; Pittsburgh, PA 15213, USA
- Neuroscience Institute, Carnegie Mellon University; Pittsburgh, PA 15213, USA
| | - Karin Forsberg-Nilsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University; Uppsala, 751 85, Sweden
- Biodiscovery Institute, University of Nottingham; Nottingham, UK
| | - Elinor K. Karlsson
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School; Worcester, MA 01605, USA
- Program in Molecular Medicine, UMass Chan Medical School; Worcester, MA 01605, USA
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University; Uppsala, 751 32, Sweden
- Broad Institute of MIT and Harvard; Cambridge, MA 02139, USA
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22
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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23
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Jordan B. [The birth of a gene]. Med Sci (Paris) 2023; 39:297-300. [PMID: 36943130 DOI: 10.1051/medsci/2023021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023] Open
Abstract
The availability of an extensive set of vertebrate genome sequences, together with large-scale transcriptome studies, has allowed the discovery of numerous non-canonical ORFs (usually quite short) with evidence of transcription, translation and functional involvement. Orthologs for these ORFs can be detected in many vertebrates, and the time of appearance of a functional mini-gene can be ascertained. Some of these have appeared quite recently in evolution and have already very specific expression patterns in humans.
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Affiliation(s)
- Bertrand Jordan
- Biologiste, généticien et immunologiste, président d'Aprogène (Association pour la promotion de la Génomique), 13007 Marseille, France
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24
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How rare mutations contribute to complex traits. Nature 2023; 614:418-419. [PMID: 36755145 DOI: 10.1038/d41586-023-00272-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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25
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Lack of GPR180 ameliorates hepatic lipid depot via downregulation of mTORC1 signaling. Sci Rep 2023; 13:1843. [PMID: 36726016 PMCID: PMC9892563 DOI: 10.1038/s41598-023-29135-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/31/2023] [Indexed: 02/03/2023] Open
Abstract
Our previous genome-wide association study to explore genetic loci associated with lean nonalcoholic fatty liver disease (NAFLD) in Japan suggested four candidate loci, which were mapped to chr6, chr7, chr12 and chr13. The present study aimed to identify the locus involved functionally in NAFLD around the association signal observed in chr13. Chromosome conformation capture assay and a database survey suggested the intermolecular interaction among DNA fragments in association signals with the adjacent four coding gene promoters. The four genes were further screened by knockdown (KD) in mice using shRNA delivered by an adeno-associated virus vector (AAV8), and KD of G protein-coupled receptor 180 (Gpr180) showed amelioration of hepatic lipid storage. Gpr180 knockout (KO) mice also showed ameliorated hepatic and plasma lipid levels without influencing glucose metabolism after high-fat diet intake. Transcriptome analyses showed downregulation of mTORC1 signaling and cholesterol homeostasis, which was confirmed by weakened phosphorylation of mTOR and decreased activated SREBP1 in Gpr180KO mice and a human hepatoma cell line (Huh7). AAV8-mediated hepatic rescue of GPR180 expression in KO mice showed recovery of plasma and hepatic lipid levels. In conclusion, ablation of GPR180 ameliorated plasma and hepatic lipid levels, which was mediated by downregulation of mTORC1 signaling.
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26
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ATM suppresses c-Myc overexpression in the mammary epithelium in response to estrogen. Cell Rep 2023; 42:111909. [PMID: 36640339 PMCID: PMC10023214 DOI: 10.1016/j.celrep.2022.111909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/27/2022] [Accepted: 12/12/2022] [Indexed: 12/31/2022] Open
Abstract
ATM gene mutation carriers are predisposed to estrogen-receptor-positive breast cancer (BC). ATM prevents BC oncogenesis by activating p53 in every cell; however, much remains unknown about tissue-specific oncogenesis after ATM loss. Here, we report that ATM controls the early transcriptional response to estrogens. This response depends on topoisomerase II (TOP2), which generates TOP2-DNA double-strand break (DSB) complexes and rejoins the breaks. When TOP2-mediated ligation fails, ATM facilitates DSB repair. After estrogen exposure, TOP2-dependent DSBs arise at the c-MYC enhancer in human BC cells, and their defective repair changes the activation profile of enhancers and induces the overexpression of many genes, including the c-MYC oncogene. CRISPR/Cas9 cleavage at the enhancer also causes c-MYC overexpression, indicating that this DSB causes c-MYC overexpression. Estrogen treatment induced c-Myc protein overexpression in mammary epithelial cells of ATM-deficient mice. In conclusion, ATM suppresses the c-Myc-driven proliferative effects of estrogens, possibly explaining such tissue-specific oncogenesis.
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27
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Zhu XN, Wang YZ, Li C, Wu HY, Zhang R, Hu XX, Zhang R, Hu XX. Chicken chromatin accessibility atlas accelerates epigenetic annotation of birds and gene fine-mapping associated with growth traits. Zool Res 2023; 44:53-62. [PMID: 36317479 PMCID: PMC9841184 DOI: 10.24272/j.issn.2095-8137.2022.228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The development of epigenetic maps, such as the ENCODE project in humans, provides resources for gene regulation studies and a reference for research of disease-related regulatory elements. However, epigenetic information, such as a bird-specific chromatin accessibility atlas, is currently lacking for the thousands of bird species currently described. The major genomic difference between birds and mammals is their shorter introns and intergenic distances, which seriously hinders the use of humans and mice as a reference for studying the function of important regulatory regions in birds. In this study, using chicken as a model bird species, we systematically compiled a chicken chromatin accessibility atlas using 53 Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) samples across 11 tissues. An average of 50 796 open chromatin regions were identified per sample, cumulatively accounting for 20.36% of the chicken genome. Tissue specificity was largely reflected by differences in intergenic and intronic peaks, with specific functional regulation achieved by two mechanisms: recruitment of several sequence-specific transcription factors and direct regulation of adjacent functional genes. By integrating data from genome-wide association studies, our results suggest that chicken body weight is driven by different regulatory variants active in growth-relevant tissues. We propose CAB39L (active in the duodenum), RCBTB1 (muscle and liver), and novel long non-coding RNA ENSGALG00000053256 (bone) as candidate genes regulating chicken body weight. Overall, this study demonstrates the value of epigenetic data in fine-mapping functional variants and provides a compendium of resources for further research on the epigenetics and evolution of birds and mammals.
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Affiliation(s)
- Xiao-Ning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yu-Zhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,E-mail:
| | - Chong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Han-Yu Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China
| | - Ran Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xiao-Xiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,
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28
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Huang Z, Efthymiadou A, Liang N, Fan R, Treuter E. Antagonistic action of GPS2 and KDM1A at enhancers governs alternative macrophage activation by interleukin 4. Nucleic Acids Res 2023; 51:1067-1086. [PMID: 36610795 PMCID: PMC9943668 DOI: 10.1093/nar/gkac1230] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 11/24/2022] [Accepted: 01/02/2023] [Indexed: 01/09/2023] Open
Abstract
The Th2 cytokine interleukin 4 (IL4) promotes macrophage differentiation into alternative subtypes and plays important roles in physiology, in metabolic and inflammatory diseases, in cancer and in tissue regeneration. While the regulatory transcription factor networks governing IL4 signaling are already well-characterized, it is currently less understood which transcriptional coregulators are involved and how they operate mechanistically. In this study, we discover that G protein pathway suppressor 2 (GPS2), a core subunit of the HDAC3 corepressor complex assembled by SMRT and NCOR, represses IL4-dependent enhancer activation in mouse macrophages. Our genome-wide and gene-specific characterization revealed that, instead of directly repressing STAT6, chromatin-bound GPS2 cooperates with SMRT and NCOR to antagonize enhancer activation by lysine demethylase 1A (KDM1A, LSD1). Mechanistically, corepressor depletion increased KDM1A recruitment to enhancers linked to IL4-induced genes, accompanied by demethylation of the repressive histone marks H3K9me2/3 without affecting H3K4me1/2, the classic KDM1A substrates for demethylation in other cellular contexts. This in turn caused enhancer and gene activation already in the absence of IL4/STAT6 and sensitized the STAT6-dependent IL4 responsiveness of macrophages. Thus, our work identified with the antagonistic action of a GPS2-containing corepressor complex and the lysine demethylase KDM1A a hitherto unknown epigenetic corepressor-coactivator switching mechanism that governs alternative macrophage activation.
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Affiliation(s)
- Zhiqiang Huang
- Department of Biosciences and Nutrition, Karolinska Institutet, 14183 Huddinge, Sweden
| | - Astradeni Efthymiadou
- Department of Biosciences and Nutrition, Karolinska Institutet, 14183 Huddinge, Sweden
| | - Ning Liang
- Department of Biosciences and Nutrition, Karolinska Institutet, 14183 Huddinge, Sweden
| | - Rongrong Fan
- Correspondence may also be addressed to Rongrong Fan. Tel: +46 8 524 81161;
| | - Eckardt Treuter
- To whom correspondence should be addressed. Tel: +46 8 524 81060;
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29
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Appiah CO, Singh M, May L, Bakshi I, Vaidyanathan A, Dent P, Ginder G, Grant S, Bear H, Landry J. The epigenetic regulation of cancer cell recovery from therapy exposure and its implications as a novel therapeutic strategy for preventing disease recurrence. Adv Cancer Res 2023; 158:337-385. [PMID: 36990536 DOI: 10.1016/bs.acr.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The ultimate goal of cancer therapy is the elimination of disease from patients. Most directly, this occurs through therapy-induced cell death. Therapy-induced growth arrest can also be a desirable outcome, if prolonged. Unfortunately, therapy-induced growth arrest is rarely durable and the recovering cell population can contribute to cancer recurrence. Consequently, therapeutic strategies that eliminate residual cancer cells reduce opportunities for recurrence. Recovery can occur through diverse mechanisms including quiescence or diapause, exit from senescence, suppression of apoptosis, cytoprotective autophagy, and reductive divisions resulting from polyploidy. Epigenetic regulation of the genome represents a fundamental regulatory mechanism integral to cancer-specific biology, including the recovery from therapy. Epigenetic pathways are particularly attractive therapeutic targets because they are reversible, without changes in DNA, and are catalyzed by druggable enzymes. Previous use of epigenetic-targeting therapies in combination with cancer therapeutics has not been widely successful because of either unacceptable toxicity or limited efficacy. The use of epigenetic-targeting therapies after a significant interval following initial cancer therapy could potentially reduce the toxicity of combination strategies, and possibly exploit essential epigenetic states following therapy exposure. This review examines the feasibility of targeting epigenetic mechanisms using a sequential approach to eliminate residual therapy-arrested populations, that might possibly prevent recovery and disease recurrence.
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Affiliation(s)
- Christiana O Appiah
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States; Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, United States
| | - Manjulata Singh
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Lauren May
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Ishita Bakshi
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Ashish Vaidyanathan
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Paul Dent
- Department of Biochemistry and Molecular Biology, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States
| | - Gordon Ginder
- Department of Internal Medicine, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States
| | - Steven Grant
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States; Department of Internal Medicine, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States; Department of Biochemistry and Molecular Biology, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States; Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Massey Cancer Center, Richmond, Richmond, VA, United States
| | - Harry Bear
- Department of Surgery, Virginia Commonwealth University School of Medicine, Massey Cancer Center, Richmond, VA, United States; Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Massey Cancer Center, Richmond, Richmond, VA, United States
| | - Joseph Landry
- Department of Human and Molecular Genetics, VCU Institute of Molecular Medicine, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.
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30
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Feng Z, Duren Z, Xin J, Yuan Q, He Y, Su B, Wong WH, Wang Y. Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification. eLife 2022; 11:82535. [PMID: 36525361 PMCID: PMC9810332 DOI: 10.7554/elife.82535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar, copy archived at swh:1:rev:cf27438d3f8245c34c357ec5f077528e6befe829.
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Affiliation(s)
- Zhanying Feng
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Jingxue Xin
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Qiuyue Yuan
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of SciencesHangzhouChina
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31
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Chromatin structure in cancer. BMC Mol Cell Biol 2022; 23:35. [PMID: 35902807 PMCID: PMC9331575 DOI: 10.1186/s12860-022-00433-6] [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: 08/20/2021] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
In the past decade, we have seen the emergence of sequence-based methods to understand chromosome organization. With the confluence of in situ approaches to capture information on looping, topological domains, and larger chromatin compartments, understanding chromatin-driven disease is becoming feasible. Excitingly, recent advances in single molecule imaging with capacity to reconstruct “bulk-cell” features of chromosome conformation have revealed cell-to-cell chromatin structural variation. The fundamental question motivating our analysis of the literature is, can altered chromatin structure drive tumorigenesis? As our community learns more about rare disease, including low mutational frequency cancers, understanding “chromatin-driven” pathology will illuminate the regulatory structures of the genome. We describe recent insights into altered genome architecture in human cancer, highlighting multiple pathways toward disruptions of chromatin structure, including structural variation, noncoding mutations, metabolism, and de novo mutations to architectural regulators themselves. Our analysis of the literature reveals that deregulation of genome structure is characteristic in distinct classes of chromatin-driven tumors. As we begin to integrate the findings from single cell imaging studies and chromatin structural sequencing, we will be able to understand the diversity of cells within a common diagnosis, and begin to define structure–function relationships of the misfolded genome.
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Liang J, Jin W, Xu H. An efficient five-lncRNA signature for lung adenocarcinoma prognosis, with AL606489.1 showing sexual dimorphism. Front Genet 2022; 13:1052092. [PMID: 36531243 PMCID: PMC9748423 DOI: 10.3389/fgene.2022.1052092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/16/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Lung adenocarcinoma (LUAD) is a sex-biased and easily metastatic malignant disease. A signature based on 5 long non-coding RNAs (lncRNAs) has been established to promote the overall survival (OS) prediction effect on LUAD.Methods: The RNA expression profiles of LUAD patients were obtained from The Cancer Genome Atlas. OS-associated lncRNAs were identified based on the differential expression analysis between LUAD and normal samples followed by survival analysis, univariate and multivariate Cox proportional hazards regression analyses. OS-associated lncRNA with sex dimorphism was determined based on the analysis of expression between males and females. Functional enrichment analysis of the Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed to explore the possible mechanisms of 5-lncRNA signatures.Results: A 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) was found to be effective in predicting high-risk LUAD patients as well as applicable to female and male subgroups and <65-year and ≥65-year age subgroups. The forecasted effect of the 5-lncRNA signature was more efficient and stable than the TNM stage and other clinical risk factors (such as sex and age). Functional enrichment analysis revealed that the mRNA co-expressed with these five OS-related lncRNAs was associated with RNA regulation within the nucleus. AL606489.1 demonstrated a sexual dimorphism that may be associated with microtubule activity.Conclusion: Our 5-lncRNA signature could efficaciously predict the OS of LUAD patients. AL606489.1 demonstrated gender dimorphism, which provides a new direction for mechanistic studies on sexual dimorphism.
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Affiliation(s)
- Jiali Liang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weifeng Jin
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Huaping Xu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
- *Correspondence: Huaping Xu,
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Drekolia MK, Talyan S, Cordellini Emídio R, Boon RA, Guenther S, Looso M, Dumbović G, Bibli SI. Unravelling the impact of aging on the human endothelial lncRNA transcriptome. Front Genet 2022; 13:1035380. [PMID: 36338971 PMCID: PMC9634578 DOI: 10.3389/fgene.2022.1035380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/06/2022] [Indexed: 11/21/2022] Open
Abstract
The incidence and prevalence of cardiovascular disease is highest among the elderly. There is a need to further understand the mechanisms behind endothelial cell aging in order to achieve vascular rejuvenation and minimize the onset of age-related vascular diseases. Long non-coding RNAs (lncRNAs) have been proposed to regulate numerous processes in the human genome, yet their function in vascular aging and their therapeutic potential remain largely unknown. This is primarily because the majority of studies investigating the impact of aging on lncRNA expression heavily rely on in vitro studies based on replicative senescence. Here, using a unique collection of young and aged endothelial cells isolated from native human arteries, we sought to characterize the age-related alterations in lncRNA expression profiles. We were able to detect a total of 4463 lncRNAs expressed in the human endothelium from which ∼17% (798) were altered in advanced age. One of the most affected lncRNAs in aging was the primate-specific, Prostate Cancer Associated Transcript (PCAT) 14. In our follow up analysis, using single molecule RNA FISH, we showed that PCAT14 is relatively abundant, localized almost exclusively in the nucleus of young endothelial cells, and silenced in the aged endothelium. Functionally, our studies proposed that downregulation of PCAT14 alters endothelial cell transcription profile and cell functions including endothelial cell migration, sprouting and inflammatory responses in vitro. Taken together, our data highlight that endothelial cell aging correlates with altered expression of lncRNAs, which could impair the endothelial regenerative capacity and enhance inflammatory phenotypes.
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Affiliation(s)
- Maria-Kyriaki Drekolia
- Institute for Vascular Signalling, Centre for Molecular Medicine, Goethe University, Frankfurt, Germany
| | - Sweta Talyan
- Bioinformatics Core Unit, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | | | - Reinier Abraham Boon
- Institute for Cardiovascular Regeneration, Goethe University, Frankfurt, Germany
- German Center for Cardiovascular Research (DZHK), partner site Rhein/Main, Frankfurt, Germany
| | - Stefan Guenther
- Bioinformatics Core Unit, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- German Center for Cardiovascular Research (DZHK), partner site Rhein/Main, Frankfurt, Germany
| | - Mario Looso
- Bioinformatics Core Unit, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- German Center for Cardiovascular Research (DZHK), partner site Rhein/Main, Frankfurt, Germany
| | - Gabrijela Dumbović
- Institute for Cardiovascular Regeneration, Goethe University, Frankfurt, Germany
- *Correspondence: Sofia-Iris Bibli, ; Gabrijela Dumbović,
| | - Sofia-Iris Bibli
- Institute for Vascular Signalling, Centre for Molecular Medicine, Goethe University, Frankfurt, Germany
- German Center for Cardiovascular Research (DZHK), partner site Rhein/Main, Frankfurt, Germany
- *Correspondence: Sofia-Iris Bibli, ; Gabrijela Dumbović,
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34
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Mew M, Caldwell KA, Caldwell GA. From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models. Hum Mol Genet 2022; 31:R37-R46. [PMID: 35994032 PMCID: PMC9585664 DOI: 10.1093/hmg/ddac203] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 02/02/2023] Open
Abstract
The exponential accumulation of DNA sequencing data has opened new avenues for discovering the causative roles of single-nucleotide polymorphisms (SNPs) in neurological diseases. The opportunities emerging from this are staggering, yet only as good as our abilities to glean insights from this surplus of information. Whereas computational biology continues to improve with respect to predictions and molecular modeling, the differences between in silico and in vivo analysis remain substantial. Invertebrate in vivo model systems represent technically advanced, experimentally mature, high-throughput, efficient and cost-effective resources for investigating a disease. With a decades-long track record of enabling investigators to discern function from DNA, fly (Drosophila) and worm (Caenorhabditis elegans) models have never been better poised to serve as living engines of discovery. Both of these animals have already proven useful in the classification of genetic variants as either pathogenic or benign across a range of neurodevelopmental and neurodegenerative disorders-including autism spectrum disorders, ciliopathies, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's disease. Pathogenic SNPs typically display distinctive phenotypes in functional assays when compared with null alleles and frequently lead to protein products with gain-of-function or partial loss-of-function properties that contribute to neurological disease pathogenesis. The utility of invertebrates is logically limited by overt differences in anatomical and physiological characteristics, and also the evolutionary distance in genome structure. Nevertheless, functional annotation of disease-SNPs using invertebrate models can expedite the process of assigning cellular and organismal consequences to mutations, ascertain insights into mechanisms of action, and accelerate therapeutic target discovery and drug development for neurological conditions.
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Affiliation(s)
- Melanie Mew
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Kim A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Alabama Research Institute on Aging, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Guy A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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35
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Abstract
Current estimates suggest that nearly half a billion people worldwide are affected by hearing loss. Because of the major psychological, social, economic, and health ramifications, considerable efforts have been invested in identifying the genes and molecular pathways involved in hearing loss, whether genetic or environmental, to promote prevention, improve rehabilitation, and develop therapeutics. Genomic sequencing technologies have led to the discovery of genes associated with hearing loss. Studies of the transcriptome and epigenome of the inner ear have characterized key regulators and pathways involved in the development of the inner ear and have paved the way for their use in regenerative medicine. In parallel, the immense preclinical success of using viral vectors for gene delivery in animal models of hearing loss has motivated the industry to work on translating such approaches into the clinic. Here, we review the recent advances in the genomics of auditory function and dysfunction, from patient diagnostics to epigenetics and gene therapy.
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Affiliation(s)
- Shahar Taiber
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; ,
| | - Kathleen Gwilliam
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA; ,
| | - Ronna Hertzano
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA; ,
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Karen B Avraham
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; ,
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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36
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Trendafilova T, Adhikari K, Schmid AB, Patel R, Polgár E, Chisholm KI, Middleton SJ, Boyle K, Dickie AC, Semizoglou E, Perez-Sanchez J, Bell AM, Ramirez-Aristeguieta LM, Khoury S, Ivanov A, Wildner H, Ferris E, Chacón-Duque JC, Sokolow S, Saad Boghdady MA, Herchuelz A, Faux P, Poletti G, Gallo C, Rothhammer F, Bedoya G, Zeilhofer HU, Diatchenko L, McMahon SB, Todd AJ, Dickenson AH, Ruiz-Linares A, Bennett DL. Sodium-calcium exchanger-3 regulates pain "wind-up": From human psychophysics to spinal mechanisms. Neuron 2022; 110:2571-2587.e13. [PMID: 35705078 PMCID: PMC7613464 DOI: 10.1016/j.neuron.2022.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/31/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022]
Abstract
Repeated application of noxious stimuli leads to a progressively increased pain perception; this temporal summation is enhanced in and predictive of clinical pain disorders. Its electrophysiological correlate is "wind-up," in which dorsal horn spinal neurons increase their response to repeated nociceptor stimulation. To understand the genetic basis of temporal summation, we undertook a GWAS of wind-up in healthy human volunteers and found significant association with SLC8A3 encoding sodium-calcium exchanger type 3 (NCX3). NCX3 was expressed in mouse dorsal horn neurons, and mice lacking NCX3 showed normal, acute pain but hypersensitivity to the second phase of the formalin test and chronic constriction injury. Dorsal horn neurons lacking NCX3 showed increased intracellular calcium following repetitive stimulation, slowed calcium clearance, and increased wind-up. Moreover, virally mediated enhanced spinal expression of NCX3 reduced central sensitization. Our study highlights Ca2+ efflux as a pathway underlying temporal summation and persistent pain, which may be amenable to therapeutic targeting.
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Affiliation(s)
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, UK; Department of Genetics, Evolution and Environment, University College London, London, UK; Department of Cell and Developmental Biology, University College London, London, UK
| | - Annina B Schmid
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Ryan Patel
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Erika Polgár
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Kim I Chisholm
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Steven J Middleton
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Kieran Boyle
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Allen C Dickie
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | | | | | - Andrew M Bell
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | | | - Samar Khoury
- McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Aleksandar Ivanov
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Hendrik Wildner
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Eleanor Ferris
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Juan-Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, University College London, London, UK; Centre for Palaeogenetics, Stockholm, Sweden; Department of Archaeology and Classical Studies, Stockholm University, Stockholm, Sweden
| | - Sophie Sokolow
- Laboratoire de Pharmacodynamie et de Thérapeutique Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium; School of Nursing, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - André Herchuelz
- Laboratoire de Pharmacodynamie et de Thérapeutique Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium
| | - Pierre Faux
- CNRS, EFS, ADES, Aix-Marseille Université, Marseille, France
| | - Giovanni Poletti
- Unidad de Neurobiologia Molecular y Genética, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Carla Gallo
- Unidad de Neurobiologia Molecular y Genética, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellin, Colombia
| | - Hanns Ulrich Zeilhofer
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland; Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Luda Diatchenko
- McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Stephen B McMahon
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Andrew J Todd
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Anthony H Dickenson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Andres Ruiz-Linares
- Department of Genetics, Evolution and Environment, University College London, London, UK; CNRS, EFS, ADES, Aix-Marseille Université, Marseille, France; Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK.
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37
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Mellul M, Lahav S, Imashimizu M, Tokunaga Y, Lukatsky DB, Ram O. Repetitive DNA symmetry elements negatively regulate gene expression in embryonic stem cells. Biophys J 2022; 121:3126-3135. [PMID: 35810331 PMCID: PMC9463640 DOI: 10.1016/j.bpj.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/13/2022] [Accepted: 07/07/2022] [Indexed: 11/30/2022] Open
Abstract
Transcription factor (TF) binding to genomic DNA elements constitutes one of the key mechanisms that regulates gene expression program in cells. Both consensus and nonconsensus DNA sequence elements influence the recognition specificity of TFs. Based on the analysis of experimentally determined c-Myc binding preferences to genomic DNA, here we statistically predict that certain repetitive, nonconsensus DNA symmetry elements can relatively reduce TF-DNA binding preferences. This is in contrast to a different set of repetitive, nonconsensus symmetry elements that can increase the strength of TF-DNA binding. Using c-Myc enhancer reporter system containing consensus motif flanked by nonconsensus sequences in embryonic stem cells, we directly demonstrate that the enrichment in such negatively regulating repetitive symmetry elements is sufficient to reduce the gene expression level compared with native genomic sequences. Negatively regulating repetitive symmetry elements around consensus c-Myc motif and DNA sequences containing consensus c-Myc motif flanked by entirely randomized sequences show similar expression baseline. A possible explanation for this observation is that rather than complete repression, negatively regulating repetitive symmetry elements play a regulatory role in fine-tuning the reduction of gene expression, most probably by binding TFs other than c-Myc.
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Affiliation(s)
- Meir Mellul
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel
| | - Shlomtzion Lahav
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel
| | - Masahiko Imashimizu
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Yuji Tokunaga
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
| | - David B Lukatsky
- Department of Chemistry, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Oren Ram
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel.
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38
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Tetikol HS, Turgut D, Narci K, Budak G, Kalay O, Arslan E, Demirkaya-Budak S, Dolgoborodov A, Kabakci-Zorlu D, Semenyuk V, Jain A, Davis-Dusenbery BN. Pan-African genome demonstrates how population-specific genome graphs improve high-throughput sequencing data analysis. Nat Commun 2022; 13:4384. [PMID: 35927245 PMCID: PMC9352875 DOI: 10.1038/s41467-022-31724-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 06/30/2022] [Indexed: 11/29/2022] Open
Abstract
Graph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference to represent the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based toolkits for NGS read alignment and variant calling, methods to curate genomic variants and subsequently construct genome graphs remain an understudied problem that inevitably determines the effectiveness of the overall bioinformatics pipeline. In this study, we discuss obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and demonstrate this approach on the whole-genome samples of African ancestry. Our results show that population-specific graphs, as more representative alternatives to linear or generic graph references, can achieve significantly lower read mapping errors and enhanced variant calling sensitivity, in addition to providing the improvements of joint variant calling without the need of computationally intensive post-processing steps.
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Affiliation(s)
| | | | - Kubra Narci
- Seven Bridges Genomics, Charlestown, MA, USA
| | | | - Ozem Kalay
- Seven Bridges Genomics, Charlestown, MA, USA
| | - Elif Arslan
- Seven Bridges Genomics, Charlestown, MA, USA
| | | | | | | | | | - Amit Jain
- Seven Bridges Genomics, Charlestown, MA, USA
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39
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Gutierrez J, Davis BA, Nevonen KA, Ward S, Carbone L, Maslen CL. DNA Methylation Analysis of Turner Syndrome BAV. Front Genet 2022; 13:872750. [PMID: 35711915 PMCID: PMC9194862 DOI: 10.3389/fgene.2022.872750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/13/2022] [Indexed: 11/30/2022] Open
Abstract
Turner Syndrome (TS) is a rare cytogenetic disorder caused by the complete loss or structural variation of the second sex chromosome. The most common cause of early mortality in TS results from a high incidence of left-sided congenital heart defects, including bicuspid aortic valve (BAV), which occurs in about 30% of individuals with TS. BAV is also the most common congenital heart defect in the general population with a prevalence of 0.5–2%, with males being three-times more likely to have a BAV than females. TS is associated with genome-wide hypomethylation when compared to karyotypically normal males and females. Alterations in DNA methylation in primary aortic tissue are associated with BAV in euploid individuals. Here we show significant differences in DNA methylation patterns associated with BAV in TS found in peripheral blood by comparing TS BAV (n = 12), TS TAV (n = 13), and non-syndromic BAV (n = 6). When comparing TS with BAV to TS with no heart defects we identified a differentially methylated region encompassing the BAV-associated gene MYRF, and enrichment for binding sites of two known transcription factor contributors to BAV. When comparing TS with BAV to euploid women with BAV, we found significant overlapping enrichment for ChIP-seq transcription factor targets including genes in the NOTCH1 pathway, known for involvement in the etiology of non-syndromic BAV, and other genes that are essential regulators of heart valve development. Overall, these findings suggest that altered DNA methylation affecting key aortic valve development genes contributes to the greatly increased risk for BAV in TS.
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Affiliation(s)
- Jacob Gutierrez
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Brett A Davis
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Kimberly A Nevonen
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Samantha Ward
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Lucia Carbone
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States.,Department of Medicine, Oregon Health and Science University, Portland, OR, United States.,Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, United States.,Division of Genetics, Oregon National Primate Research Center, Beaverton, OR, United States
| | - Cheryl L Maslen
- Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, United States
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40
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Molenaar TM, van Leeuwen F. SETD2: from chromatin modifier to multipronged regulator of the genome and beyond. Cell Mol Life Sci 2022; 79:346. [PMID: 35661267 PMCID: PMC9167812 DOI: 10.1007/s00018-022-04352-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/09/2022] [Accepted: 05/05/2022] [Indexed: 12/13/2022]
Abstract
Histone modifying enzymes play critical roles in many key cellular processes and are appealing proteins for targeting by small molecules in disease. However, while the functions of histone modifying enzymes are often linked to epigenetic regulation of the genome, an emerging theme is that these enzymes often also act by non-catalytic and/or non-epigenetic mechanisms. SETD2 (Set2 in yeast) is best known for associating with the transcription machinery and methylating histone H3 on lysine 36 (H3K36) during transcription. This well-characterized molecular function of SETD2 plays a role in fine-tuning transcription, maintaining chromatin integrity, and mRNA processing. Here we give an overview of the various molecular functions and mechanisms of regulation of H3K36 methylation by Set2/SETD2. These fundamental insights are important to understand SETD2’s role in disease, most notably in cancer in which SETD2 is frequently inactivated. SETD2 also methylates non-histone substrates such as α-tubulin which may promote genome stability and contribute to the tumor-suppressor function of SETD2. Thus, to understand its role in disease, it is important to understand and dissect the multiple roles of SETD2 within the cell. In this review we discuss how histone methylation by Set2/SETD2 has led the way in connecting histone modifications in active regions of the genome to chromatin functions and how SETD2 is leading the way to showing that we also have to look beyond histones to truly understand the physiological role of an ‘epigenetic’ writer enzyme in normal cells and in disease.
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41
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Manu DM, Mwinyi J, Schiöth HB. Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health. Int J Mol Sci 2022; 23:ijms23105856. [PMID: 35628666 PMCID: PMC9147258 DOI: 10.3390/ijms23105856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 12/10/2022] Open
Abstract
The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene–environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.
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Lysine demethylase 2B regulates angiogenesis via Jumonji C dependent suppression of angiogenic transcription factors. Biochem Biophys Res Commun 2022; 605:16-23. [DOI: 10.1016/j.bbrc.2022.03.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 11/23/2022]
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Toriumi K, Wang GZ, Berto S, Usui N. Editorial: Decoding Brain Function Through Genetics. Front Genet 2022; 13:874350. [PMID: 35480329 PMCID: PMC9035695 DOI: 10.3389/fgene.2022.874350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kazuya Toriumi
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Guang-Zhong Wang
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Stefano Berto
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States
| | - Noriyoshi Usui
- Department of Neuroscience and Cell Biology, Graduate School of Medicine, Osaka University, Osaka, Japan
- United Graduate School of Child Development, Osaka University, Suita, Japan
- Global Center for Medical Engineering and Informatics, Osaka University, Osaka, Japan
- Addiction Research Unit, Osaka Psychiatric Research Center, Osaka Psychiatric Medical Center, Osaka, Japan
- *Correspondence: Noriyoshi Usui,
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An Amish founder population reveals rare-population genetic determinants of the human lipidome. Commun Biol 2022; 5:334. [PMID: 35393526 PMCID: PMC8989972 DOI: 10.1038/s42003-022-03291-2] [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: 08/27/2021] [Accepted: 03/17/2022] [Indexed: 12/02/2022] Open
Abstract
Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants. A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids.
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Avraham KB, Khalaily L, Noy Y, Kamal L, Koffler-Brill T, Taiber S. The noncoding genome and hearing loss. Hum Genet 2022; 141:323-333. [PMID: 34491412 DOI: 10.1007/s00439-021-02359-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/29/2021] [Indexed: 12/16/2022]
Abstract
The age of sequencing has provided unprecedented insights into the human genome. The coding region of the genome comprises nearly 20,000 genes, of which approximately 4000 are associated with human disease. Beyond the protein-coding genome, which accounts for only 3% of the genome, lies a vast pool of regulatory elements in the form of promoters, enhancers, RNA species, and other intricate elements. These features undoubtably influence human health and disease, and as a result, a great deal of effort is currently being invested in deciphering their identity and mechanism. While a paucity of material has caused a lag in identifying these elements in the inner ear, the emergence of technologies for dealing with a minimal number of cells now has the field working overtime to catch up. Studies on microRNAs (miRNAs), long non-coding RNAs (lncRNAs), methylation, histone modifications, and more are ongoing. A number of microRNAs and other noncoding elements are known to be associated with hearing impairment and there is promise that regulatory elements will serve as future tools and targets of therapeutics and diagnostics. This review covers the current state of the field and considers future directions for the noncoding genome and implications for hearing loss.
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Affiliation(s)
- Karen B Avraham
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel.
| | - Lama Khalaily
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Yael Noy
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Lara Kamal
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Tal Koffler-Brill
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Shahar Taiber
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
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46
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Wonnacott A, Denby L, Coward RJM, Fraser DJ, Bowen T. MicroRNAs and their delivery in diabetic fibrosis. Adv Drug Deliv Rev 2022; 182:114045. [PMID: 34767865 DOI: 10.1016/j.addr.2021.114045] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/21/2021] [Accepted: 11/04/2021] [Indexed: 12/11/2022]
Abstract
The global prevalence of diabetes mellitus was estimated to be 463 million people in 2019 and is predicted to rise to 700 million by 2045. The associated financial and societal costs of this burgeoning epidemic demand an understanding of the pathology of this disease, and its complications, that will inform treatment to enable improved patient outcomes. Nearly two decades after the sequencing of the human genome, the significance of noncoding RNA expression is still being assessed. The family of functional noncoding RNAs known as microRNAs regulates the expression of most genes encoded by the human genome. Altered microRNA expression profiles have been observed both in diabetes and in diabetic complications. These transcripts therefore have significant potential and novelty as targets for therapy, therapeutic agents and biomarkers.
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Affiliation(s)
- Alexa Wonnacott
- Wales Kidney Research Unit, Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Heath Park, Cardiff CF14 4XN, UK
| | - Laura Denby
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Richard J M Coward
- Bristol Renal, Dorothy Hodgkin Building, Bristol Medical School, University of Bristol, Bristol BS1 3NY, UK
| | - Donald J Fraser
- Wales Kidney Research Unit, Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Heath Park, Cardiff CF14 4XN, UK
| | - Timothy Bowen
- Wales Kidney Research Unit, Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
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47
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Blaxter M, Archibald JM, Childers AK, Coddington JA, Crandall KA, Di Palma F, Durbin R, Edwards SV, Graves JAM, Hackett KJ, Hall N, Jarvis ED, Johnson RN, Karlsson EK, Kress WJ, Kuraku S, Lawniczak MKN, Lindblad-Toh K, Lopez JV, Moran NA, Robinson GE, Ryder OA, Shapiro B, Soltis PS, Warnow T, Zhang G, Lewin HA. Why sequence all eukaryotes? Proc Natl Acad Sci U S A 2022; 119:e2115636118. [PMID: 35042801 PMCID: PMC8795522 DOI: 10.1073/pnas.2115636118] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Life on Earth has evolved from initial simplicity to the astounding complexity we experience today. Bacteria and archaea have largely excelled in metabolic diversification, but eukaryotes additionally display abundant morphological innovation. How have these innovations come about and what constraints are there on the origins of novelty and the continuing maintenance of biodiversity on Earth? The history of life and the code for the working parts of cells and systems are written in the genome. The Earth BioGenome Project has proposed that the genomes of all extant, named eukaryotes-about 2 million species-should be sequenced to high quality to produce a digital library of life on Earth, beginning with strategic phylogenetic, ecological, and high-impact priorities. Here we discuss why we should sequence all eukaryotic species, not just a representative few scattered across the many branches of the tree of life. We suggest that many questions of evolutionary and ecological significance will only be addressable when whole-genome data representing divergences at all of the branchings in the tree of life or all species in natural ecosystems are available. We envisage that a genomic tree of life will foster understanding of the ongoing processes of speciation, adaptation, and organismal dependencies within entire ecosystems. These explorations will resolve long-standing problems in phylogenetics, evolution, ecology, conservation, agriculture, bioindustry, and medicine.
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Affiliation(s)
- Mark Blaxter
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom;
| | - John M Archibald
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS B3H 4H7, Canada
| | - Anna K Childers
- Bee Research Laboratory, Agricultural Research Service, US Department of Agriculture (USDA), Beltsville, MD 20705
| | - Jonathan A Coddington
- Global Genome Initiative, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC 20052
- Department of Invertebrate Zoology, Smithsonian Institution, Washington, DC 20013
| | - Federica Di Palma
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Richard Durbin
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Scott V Edwards
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138
| | - Jennifer A M Graves
- School of Life Sciences, La Trobe University, Bundoora, VIC 751 23, Australia
- University of Canberra, Bruce, ACT 2617, Australia
| | - Kevin J Hackett
- Crop Production and Protection, Office of National Programs, Agricultural Research Service, USDA, Beltsville, MD 20705
| | - Neil Hall
- Earlham Institute, Norwich, Norfolk NR4 7UZ, United Kingdom
| | - Erich D Jarvis
- Laboratory of the Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- Howard Hughes Medical Institute, Chevy Chase, MD 20815
| | - Rebecca N Johnson
- National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - W John Kress
- Botany, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013-7012
| | - Shigehiro Kuraku
- Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | | | - Kerstin Lindblad-Toh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala 751 23, Sweden
| | - Jose V Lopez
- Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Dania Beach, FL 33004
- Guy Harvey Oceanographic Center, Dania Beach, FL 33004
| | - Nancy A Moran
- Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Gene E Robinson
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Department of Entomology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Oliver A Ryder
- Conservation Genetics, Division of Biology, San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior and Ecology, University of California, San Diego, La Jolla, CA 92039
| | - Beth Shapiro
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95064
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
- Biodiversity Institute, University of Florida, Gainesville, FL 32611
| | - Tandy Warnow
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61301
| | - Guojie Zhang
- Villum Center for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen 2100, Denmark
- China National Genebank, Beijing Genomics Institute-Shenzhen, Shenzhen 518083, China
| | - Harris A Lewin
- Department of Evolution and Ecology, College of Biological Sciences, University of California, Davis, CA 95616
- Department of Population Health and Reproduction, University of California, Davis, CA 95616
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48
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Ha D, Kim D, Kim I, Oh Y, Kong J, Han S, Kim S. OUP accepted manuscript. Nucleic Acids Res 2022; 50:1849-1863. [PMID: 35137181 PMCID: PMC8887464 DOI: 10.1093/nar/gkac050] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 11/14/2022] Open
Abstract
Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN).
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Affiliation(s)
- Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | | | - Youngchul Oh
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Sanguk Kim
- To whom correspondence should be addressed. Tel: +82 54 279 2348; Fax: +82 54 279 2199;
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49
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Ypsilanti AR, Pattabiraman K, Catta-Preta R, Golonzhka O, Lindtner S, Tang K, Jones IR, Abnousi A, Juric I, Hu M, Shen Y, Dickel DE, Visel A, Pennachio LA, Hawrylycz M, Thompson CL, Zeng H, Barozzi I, Nord AS, Rubenstein JL. Transcriptional network orchestrating regional patterning of cortical progenitors. Proc Natl Acad Sci U S A 2021; 118:e2024795118. [PMID: 34921112 PMCID: PMC8713794 DOI: 10.1073/pnas.2024795118] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2021] [Indexed: 12/23/2022] Open
Abstract
We uncovered a transcription factor (TF) network that regulates cortical regional patterning in radial glial stem cells. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1, and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer-gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning in the mouse.
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Affiliation(s)
- Athéna R Ypsilanti
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158;
| | - Kartik Pattabiraman
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158
| | - Rinaldo Catta-Preta
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA 95618
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA 95618
| | - Olga Golonzhka
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158
| | - Susan Lindtner
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158
| | - Ke Tang
- Precise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Ian R Jones
- Institute for Human Genetics, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195
| | - Ivan Juric
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Diane E Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
- School of Natural Sciences, University of California, Merced, CA 95343
- US Department of Energy Joint Genome Institute, Berkeley, CA 94720
| | - Len A Pennachio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
- US Department of Energy Joint Genome Institute, Berkeley, CA 94720
- Comparative Biochemistry Program, University of California, Berkeley, CA 94720
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109
| | - Iros Barozzi
- Faculty of Medicine, Department of Surgery and Cancer, Imperial College, London SW7 2AZ, United Kingdom
| | - Alex S Nord
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA 95618
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA 95618
| | - John L Rubenstein
- Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158;
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50
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Yi X, Zheng Z, Xu H, Zhou Y, Huang D, Wang J, Feng X, Zhao K, Fan X, Zhang S, Dong X, Wang Z, Shen Y, Cheng H, Shi L, Li MJ. Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin. iScience 2021; 24:103468. [PMID: 34888502 PMCID: PMC8634045 DOI: 10.1016/j.isci.2021.103468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/23/2021] [Accepted: 11/12/2021] [Indexed: 12/14/2022] Open
Abstract
Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin. Computational inference of transcriptional regulator (TR) cooperation in 3D chromatin A landscape of 3D TR cooperation across 40 human tissue/cell types TR cooperation can better interpret the disease-causal variants identified by GWAS Cooperation of certain TRs shapes context-specific gene regulation in cell development
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Affiliation(s)
- Xianfu Yi
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China.,Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Zhanye Zheng
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hang Xu
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Dandan Huang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiangling Feng
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Ke Zhao
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Shijie Zhang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiaobao Dong
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Zhao Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yujun Shen
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Chinese Academy of Medical Sciences, Tianjin 300070, China
| | - Lei Shi
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
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