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Valle F, Caselle M, Osella M. Exploring the latent space of transcriptomic data with topic modeling. NAR Genom Bioinform 2025; 7:lqaf049. [PMID: 40264683 PMCID: PMC12012681 DOI: 10.1093/nargab/lqaf049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2024] [Revised: 04/03/2025] [Accepted: 04/11/2025] [Indexed: 04/24/2025] Open
Abstract
The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We examine the properties of the latent space recovered by different methods, highlight their differences, and their pros and cons across different tasks. We focus in particular on how different statistical priors can affect the results and their interpretability. Finally, we show that the latent topic space can be a useful low-dimensional embedding space, where a basic neural network classifier can annotate transcriptomic profiles with high accuracy.
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Affiliation(s)
- Filippo Valle
- Physics Department, University of Turin and INFN, Via Pietro Giuria 1, 12125 Torino, Italy
| | - Michele Caselle
- Physics Department, University of Turin and INFN, Via Pietro Giuria 1, 12125 Torino, Italy
| | - Matteo Osella
- Physics Department, University of Turin and INFN, Via Pietro Giuria 1, 12125 Torino, Italy
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2
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Pandey AC, Bezney J, DeAscanis D, Kirsch EB, Ahmed F, Crinklaw A, Choudhary KS, Mandala T, Deason J, Hamidi JS, Siddique A, Ranganathan S, Brown K, Armstrong J, Head S, Ordoukhanian P, Steinmetz LM, Topol EJ. A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics. Nat Commun 2025; 16:4664. [PMID: 40389438 PMCID: PMC12089397 DOI: 10.1038/s41467-025-59880-2] [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: 08/01/2024] [Accepted: 05/03/2025] [Indexed: 05/21/2025] Open
Abstract
Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover biological signatures in different biological contexts. We further emphasize scCLEAN's versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show the possible utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable.
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Affiliation(s)
- Amitabh C Pandey
- Section of Cardiology, Tulane Heart and Vascular Institute, Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA.
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA.
- Department of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA.
| | - Jon Bezney
- Genomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA
- Jumpcode Genomics, San Diego, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ethan B Kirsch
- Department of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA
| | - Farin Ahmed
- Genomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | - Tony Mandala
- Genomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Jasmin S Hamidi
- Department of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | | | | | - Steven Head
- Genomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Lars M Steinmetz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Genome Technology Center, Palo Alto, CA, USA
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Eric J Topol
- Department of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA
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3
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Martin OL, Lynch CRH, Fleming R. Advancing forensic body fluid identification: A comparative analysis of RT-LAMP+CRISPR-Cas12a and established mRNA-based methods. Forensic Sci Int Genet 2025; 78:103297. [PMID: 40347696 DOI: 10.1016/j.fsigen.2025.103297] [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: 11/19/2024] [Revised: 04/29/2025] [Accepted: 05/05/2025] [Indexed: 05/14/2025]
Abstract
In forensic science, the analysis of body fluid evidence determines the cellular origin of a sample, aiding in the reconstruction of a potential crime. Messenger ribonucleic acid (mRNA) based confirmatory tests address limitations of current conventional methods, providing increased specificity and sensitivity, minimal sample consumption, and the detection of a broader range of body fluids. However, they require expensive instrumentation, longer reaction times, and lack portability. Reverse-transcription loop-mediated isothermal amplification (RT-LAMP) coupled with clustered regular interspaced short palindromic repeats (CRISPR) with CRISPR-associated protein 12a (Cas12a) has the potential to overcome these challenges. This approach offers reduced testing time and cost, while potentially providing equivalent sensitivity and specificity, as observed in the field of viral diagnostics. Visual detection capabilities enable the development of rapid, portable screening tests suitable for testing at the crime scene. In the context of a sexual assault investigation, RT-LAMP+CRISPR-Cas12a could potentially increase the efficiency and detection rate. This study compares this novel method to two other mRNA-based methods, endpoint reverse transcription polymerase chain reaction (RT-PCR) multiplex assay CellTyper 2, and a real-time reverse transcription quantitative PCR (RT-qPCR) multiplex assay. The tests' sensitivity and specificity were evaluated on single-source and mixed body fluid samples, including rectal mucosa, a fluid which is minimally explored in forensic literature. The RT-qPCR assay demonstrated the highest sensitivity, specificity, and precision in mixed samples. In addition, RT-qPCR offers a greater linear dynamic range, faster processing time and easier methodology compared to CellTyper 2, only limited by its expensive nature. Notably, rectal mucosa samples exhibited non-specific marker expression of CellTyper 2 markers and expression of CYP2B7P (vaginal fluid) for all methods. This emphasises the need for a dedicated rectal mucosa marker. RT-LAMP+CRISPR-Cas12a exhibited a high specificity, displaying off-target expression of CYP2B7P in two fluid types. However, the method lacked sensitivity and precision for most markers except MMP3 (menstrual blood), demonstrating detection down to 1:10,000 with 100 % specificity. RT-LAMP+CRISPR requires further development, but its quick, inexpensive nature and high specificity suggest it has potential as a confirmatory test that could reduce the limitations of existing methods.
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Affiliation(s)
- Olivia L Martin
- Forensic Science Programme, University of Auckland, Auckland, New Zealand
| | - Courtney R H Lynch
- Forensic Research and Development Team, Institute of Environmental Science and Research Ltd, Auckland, New Zealand
| | - Rachel Fleming
- Forensic Research and Development Team, Institute of Environmental Science and Research Ltd, Auckland, New Zealand.
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4
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Nesselbush MC, Luca BA, Jeon YJ, Jabara I, Meador CB, Garofalo A, Binkley MS, Hui AB, van 't Erve I, Xu N, Shi WY, Liu KJ, Sugio T, Kastelowitz N, Hamilton EG, Liu CL, Olsen M, Bonilla RF, Wang YP, Jiang A, Lau B, Eichholz J, Banwait M, Schroers-Martin J, Boegeholz J, King DA, Luikart H, Esfahani MS, Mehrmohamadi M, Stehr H, Raclin T, Tibshirani R, Khush K, Srinivas S, Yu H, Rogers AJ, Nair VS, Isbell JM, Li BT, Piotrowska Z, Sequist LV, Hata AN, Neal JW, Wakelee HA, Gentles AJ, Alizadeh AA, Diehn M. An ultrasensitive method for detection of cell-free RNA. Nature 2025; 641:759-768. [PMID: 40240612 DOI: 10.1038/s41586-025-08834-1] [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: 12/01/2023] [Accepted: 02/24/2025] [Indexed: 04/18/2025]
Abstract
Sensitive methods for detection of cell-free RNA (cfRNA) could facilitate non-invasive gene expression profiling and monitoring of diseases1-6. Here we describe RARE-seq (random priming and affinity capture of cfRNA fragments for enrichment analysis by sequencing), a method optimized for cfRNA analysis. We demonstrate that platelet contamination can substantially confound cfRNA analyses and develop an approach to overcome it. In analytical validations, we find RARE-seq to be approximately 50-fold more sensitive for detecting tumour-derived cfRNA than whole-transcriptome RNA sequencing (RNA-seq), with a limit of detection of 0.05%. To explore clinical utility, we profiled 437 plasma samples from 369 individuals with cancer or non-malignant conditions and controls. Detection of non-small-cell lung cancer expression signatures in cfRNA increased with stage (6 out of 20 (30%) in stage I; 5 out of 8 (63%) in stage II; 10 out of 15 (67%) in stage III; 80 out of 96 (83% sensitivity) in stage IV at 95% specificity) and RARE-seq was more sensitive than tumour-naive circulating tumour DNA (ctDNA) analysis. In patients with EGFR-mutant non-small-cell lung cancer who developed resistance to tyrosine kinase inhibitors, we detected both histological transformation and mutation-based resistance mechanisms. Finally, we demonstrate the potential utility of RARE-seq for determination of tissue of origin, assessing benign pulmonary conditions and tracking response to mRNA vaccines. These results highlight the potential value of ultrasensitive cfRNA analysis and provide proof of concept for diverse clinical applications.
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Affiliation(s)
- Monica C Nesselbush
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Bogdan A Luca
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Young-Jun Jeon
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Isabel Jabara
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Catherine B Meador
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrea Garofalo
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Michael S Binkley
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Angela B Hui
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Iris van 't Erve
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Nova Xu
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - William Y Shi
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Kevin J Liu
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Takeshi Sugio
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Noah Kastelowitz
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Emily G Hamilton
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Chih Long Liu
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Mari Olsen
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Rene F Bonilla
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Yi Peng Wang
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Alice Jiang
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Brianna Lau
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Jordan Eichholz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mandeep Banwait
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joseph Schroers-Martin
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Jan Boegeholz
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Daniel A King
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Helen Luikart
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Mohammad S Esfahani
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Mahya Mehrmohamadi
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Henning Stehr
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Tyler Raclin
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Kiran Khush
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Sandy Srinivas
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Helena Yu
- Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Angela J Rogers
- Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Viswam S Nair
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington, Seattle, WA, USA
| | - James M Isbell
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bob T Li
- Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zofia Piotrowska
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lecia V Sequist
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Aaron N Hata
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Joel W Neal
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Heather A Wakelee
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | | | - Ash A Alizadeh
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
| | - Maximilian Diehn
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
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5
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Tekola-Ayele F, Biedrzycki RJ, Habtewold TD, Wijesiriwardhana P, Burt A, Marsit CJ, Ouidir M, Wapner R. Sex-differentiated placental methylation and gene expression regulation has implications for neonatal traits and adult diseases. Nat Commun 2025; 16:4004. [PMID: 40312437 PMCID: PMC12045980 DOI: 10.1038/s41467-025-58128-3] [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: 04/25/2024] [Accepted: 03/10/2025] [Indexed: 05/03/2025] Open
Abstract
Sex differences in physiological and disease traits are pervasive and begin during early development, but the genetic architecture of these differences is largely unknown. Here, we leverage the human placenta, a transient organ during pregnancy critical to fetal development, to investigate the impact of sex in the regulatory landscape of placental autosomal methylome and transcriptome, and its relevance to health and disease. We find that placental methylation and its genetic regulation are extensively impacted by fetal sex, whereas sex differences in placental gene expression and its genetic regulation are limited. We identify molecular processes and regulatory targets that are enriched in a sex-specific manner, and find enrichment of imprinted genes in sex-differentiated placental methylation, including female-biased methylation within the well-known KCNQ1OT1/CDKN1C imprinting cluster of genes expressed in a parent-of-origin dependent manner. We establish that several sex-differentiated genetic effects on placental methylation and gene expression colocalize with birthweight and adult disease genetic associations, facilitating mechanistic insights on early life origins of health and disease outcomes shaped by sex.
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Affiliation(s)
- Fasil Tekola-Ayele
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
| | - Richard J Biedrzycki
- Glotech, Inc., contractor for Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Tesfa Dejenie Habtewold
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Prabhavi Wijesiriwardhana
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Amber Burt
- Gangarosa Department of Environmental Health, Rollins School of Public Health of Emory University, Atlanta, GA, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health of Emory University, Atlanta, GA, USA
| | - Marion Ouidir
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
- University of Grenoble Alpes, Inserm, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences, Grenoble, France
| | - Ronald Wapner
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
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Li K, Chen Y, Sheng Y, Tang D, Cao Y, He X. Defects in mRNA splicing and implications for infertility: a comprehensive review and in silico analysis. Hum Reprod Update 2025; 31:218-239. [PMID: 39953708 DOI: 10.1093/humupd/dmae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2024] [Revised: 11/25/2024] [Indexed: 02/17/2025] Open
Abstract
BACKGROUND mRNA splicing is a fundamental process in the reproductive system, playing a pivotal role in reproductive development and endocrine function, and ensuring the proper execution of meiosis, mitosis, and gamete function. Trans-acting factors and cis-acting elements are key players in mRNA splicing whose dysfunction can potentially lead to male and female infertility. Although hundreds of trans-acting factors have been implicated in mRNA splicing, the mechanisms by which these factors influence reproductive processes are fully understood for only a subset. Furthermore, the clinical impact of variations in cis-acting elements on human infertility has not been comprehensively characterized, leading to probable omissions of pathogenic variants in standard genetic analyses. OBJECTIVE AND RATIONALE This review aimed to summarize our current understanding of the factors involved in mRNA splicing regulation and their association with infertility disorders. We introduced methods for prioritizing and functionally validating splicing variants associated with human infertility. Additionally, we explored corresponding abnormal splicing therapies that could potentially provide insight into treating human infertility. SEARCH METHODS Systematic literature searches of human and model organisms were performed in the PubMed database between May 1977 and July 2024. To identify mRNA splicing-related genes and pathogenic variants in infertility, the search terms 'splice', 'splicing', 'variant', and 'mutation' were combined with azoospermia, oligozoospermia, asthenozoospermia, multiple morphological abnormalities of the sperm flagella, acephalic spermatozoa, disorders of sex development, early embryonic arrest, reproductive endocrine disorders, oocyte maturation arrest, premature ovarian failure, primary ovarian insufficiency, zona pellucida, fertilization defects, infertile, fertile, infertility, fertility, reproduction, and reproductive. OUTCOMES Our search identified 5014 publications, of which 291 were included in the final analysis. This review provided a comprehensive overview of the biological mechanisms of mRNA splicing, with a focus on the roles of trans-acting factors and cis-acting elements. We highlighted the disruption of 52 trans-acting proteins involved in spliceosome assembly and catalytic activity and recognized splicing regulatory regions and epigenetic regulation associated with infertility. The 73 functionally validated splicing variants in the cis-acting elements of 54 genes have been reported in 20 types of human infertility; 27 of them were located outside the canonical splice sites and potentially overlooked in standard genetic analysis due to likely benign or of uncertain significance. The in silico prediction of splicing can prioritize potential splicing abnormalities that may be true pathogenic mechanisms. We also summarize the methods for prioritizing splicing variants and strategies for functional validation and review splicing therapy approaches for other diseases, providing a reference for abnormal reproduction treatment. WIDER IMPLICATIONS Our comprehensive review of trans-acting factors and cis-acting elements in mRNA splicing will further promote a more thorough understanding of reproductive regulatory processes, leading to improved pathogenic variant identification and potential treatments for human infertility. REGISTRATION NUMBER N/A.
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Affiliation(s)
- Kuokuo Li
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
- Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, Anhui, China
| | - Yuge Chen
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
- Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, Anhui, China
| | - Yuying Sheng
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
- Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, Anhui, China
| | - Dongdong Tang
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
- Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, Anhui, China
| | - Yunxia Cao
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
- Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, Hefei, Anhui, China
| | - Xiaojin He
- Department of Obstetrics and Gynecology, Reproductive Medicine Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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7
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Awasthi BW, Paulo JA, Burkhart DL, Smith IR, Collins RL, Harper JW, Gygi SP, Haigis KM. The network response to Egf is tissue-specific. iScience 2025; 28:112146. [PMID: 40171493 PMCID: PMC11960661 DOI: 10.1016/j.isci.2025.112146] [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: 04/22/2024] [Revised: 07/29/2024] [Accepted: 02/27/2025] [Indexed: 04/03/2025] Open
Abstract
Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.
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Affiliation(s)
- Beatrice W. Awasthi
- Center for Systems Biology, Department of Radiation Oncology, and Center for Cancer Research, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - João A. Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Deborah L. Burkhart
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Ian R. Smith
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ryan L. Collins
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Cancer Program, Broad Institute of M.I.T. and Harvard, Cambridge, MA 02115, USA
| | - J. Wade Harper
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Steven P. Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Kevin M. Haigis
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
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8
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Mouhou E, Genty F, El M'selmi W, Chouali H, Zagury JF, Le Clerc S, Proudhon C, Noirel J. High tissue specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA. Sci Rep 2025; 15:12941. [PMID: 40234550 PMCID: PMC12000428 DOI: 10.1038/s41598-024-82393-9] [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: 03/21/2024] [Accepted: 12/05/2024] [Indexed: 04/17/2025] Open
Abstract
Several studies have made it possible to envision a translational application of plasma DNA sequencing in cancer diagnosis and monitoring. However, the extremely low concentration of circulating tumour DNA (ctDNA) fragments among the total cell-free DNA (cfDNA) remains a formidable challenge to overcome and statistical models have yet to be improved enough to become of practical use. In this study, we set about appraising the predictive value of a variety of binary classification models based on cfDNA sequencing using fragmentation features extracted around transcription start sites (TSSs). We investigated (1) features summarising mapped fragment density around each TSS, (2) long non-coding RNA (lncRNA) genes versus coding genes and (3) selection criteria to generate gene classes to be assigned by the model. Given that, in healthy samples, most of the cfDNA comes from lymphomyeloid lineages, we could identify the model parametrisation with the best accuracy in those lineages using publicly available datasets of healthy patients' cfDNA. Our results show that (1) the way tissue-specific gene classes are defined matters more than what fragmentation features are included, and (2) in particular, lncRNAs are more tissue specific than coding genes and stand out in terms of both sensitivity and specificity in our results.
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Affiliation(s)
- Elyas Mouhou
- Laboratoire GBCM (EA7528), Conservatoire national des arts et métiers (CNAM), Paris, France
| | - Fabien Genty
- Infotel Conseil, 13, rue Madeleine-Michelis, Neuilly-sur-Seine, France
| | | | - Hanae Chouali
- BioinfOmics, GenoToul Bioinformatics facility, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan, France
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan, France
| | - Jean-François Zagury
- Laboratoire GBCM (EA7528), Conservatoire national des arts et métiers (CNAM), Paris, France
| | - Sigrid Le Clerc
- Laboratoire GBCM (EA7528), Conservatoire national des arts et métiers (CNAM), Paris, France
| | | | - Josselin Noirel
- Laboratoire GBCM (EA7528), Conservatoire national des arts et métiers (CNAM), Paris, France.
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9
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Novák J, Takács T, Tilajka Á, László L, Oravecz O, Farkas E, Than NG, Buday L, Balogh A, Vas V. The sweet and the bitter sides of galectin-1 in immunity: its role in immune cell functions, apoptosis, and immunotherapies for cancer with a focus on T cells. Semin Immunopathol 2025; 47:24. [PMID: 40178639 PMCID: PMC11968517 DOI: 10.1007/s00281-025-01047-8] [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: 07/11/2024] [Accepted: 02/07/2025] [Indexed: 04/05/2025]
Abstract
Galectin-1 (Gal-1), a member of the β-galactoside-binding soluble lectin family, is a double-edged sword in immunity. On one hand, it plays a crucial role in regulating diverse immune cell functions, including the apoptosis of activated T cells. These processes are key in resolving inflammation and preventing autoimmune diseases. On the other hand, Gal-1 has significant implications in cancer, where tumor cells and the tumor microenvironment (TME) (e.g., tumor-associated fibroblasts, myeloid-derived suppressor cells) secrete Gal-1 to evade immune surveillance and promote cancer cell growth. Within the TME, Gal-1 enhances the differentiation of tolerogenic dendritic cells, induces the apoptosis of effector T cells, and enhances the proliferation of regulatory T cells, collectively facilitating tumor immune escape. Therefore, targeting Gal-1 holds the potential to boost anti-tumor immunity and improve the efficacy of cancer immunotherapy. This review provides insights into the intricate role of Gal-1 in immune cell regulation, with an emphasis on T cells, and elucidates how tumors exploit Gal-1 for immune evasion and growth. Furthermore, we discuss the potential of Gal-1 as a therapeutic target to augment current immunotherapies across various cancer types.
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Affiliation(s)
- Julianna Novák
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Tamás Takács
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, Budapest, 1117, Hungary
| | - Álmos Tilajka
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, Budapest, 1117, Hungary
| | - Loretta László
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, Budapest, 1117, Hungary
| | - Orsolya Oravecz
- Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, Budapest, 1117, Hungary
- Systems Biology of Reproduction Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Emese Farkas
- Systems Biology of Reproduction Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Károly Rácz Conservative Medicine Division, Doctoral College, Semmelweis University, Budapest, 1091, Hungary
| | - Nándor Gábor Than
- Systems Biology of Reproduction Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, 1088, Hungary
| | - László Buday
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Department of Molecular Biology, Semmelweis University, Budapest, 1094, Hungary
| | - Andrea Balogh
- Systems Biology of Reproduction Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Virág Vas
- Signal Transduction and Functional Genomics Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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10
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Lambourne L, Mattioli K, Santoso C, Sheynkman G, Inukai S, Kaundal B, Berenson A, Spirohn-Fitzgerald K, Bhattacharjee A, Rothman E, Shrestha S, Laval F, Carroll BS, Plassmeyer SP, Emenecker RJ, Yang Z, Bisht D, Sewell JA, Li G, Prasad A, Phanor S, Lane R, Moyer DC, Hunt T, Balcha D, Gebbia M, Twizere JC, Hao T, Holehouse AS, Frankish A, Riback JA, Salomonis N, Calderwood MA, Hill DE, Sahni N, Vidal M, Bulyk ML, Fuxman Bass JI. Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. Mol Cell 2025; 85:1445-1466.e13. [PMID: 40147441 PMCID: PMC12121496 DOI: 10.1016/j.molcel.2025.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 12/06/2024] [Accepted: 03/05/2025] [Indexed: 03/29/2025]
Abstract
Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.
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Affiliation(s)
- Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kaia Mattioli
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Clarissa Santoso
- Department of Biology, Boston University, Boston, MA 02215, USA; Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Gloria Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Babita Kaundal
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anna Berenson
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA 02215, USA
| | - Kerstin Spirohn-Fitzgerald
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Anukana Bhattacharjee
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Elisabeth Rothman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | | | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; TERRA Teaching and Research Centre, University of Liège, Gembloux 5030, Belgium; Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège 4000, Belgium
| | - Brent S Carroll
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Stephen P Plassmeyer
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA; Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Ryan J Emenecker
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA; Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Zhipeng Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Deepa Bisht
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jared A Sewell
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Guangyuan Li
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Anisa Prasad
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Harvard College, Cambridge, MA 02138, USA
| | - Sabrina Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Lane
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Devlin C Moyer
- Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CD10 1SD, UK
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, ON M5G 1X5, Canada
| | - Jean-Claude Twizere
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; TERRA Teaching and Research Centre, University of Liège, Gembloux 5030, Belgium; Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège 4000, Belgium
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alex S Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA; Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CD10 1SD, UK
| | - Josh A Riback
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - Martha L Bulyk
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Juan I Fuxman Bass
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biology, Boston University, Boston, MA 02215, USA; Bioinformatics Program, Boston University, Boston, MA 02215, USA; Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA 02215, USA.
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11
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Previderè C, Bonin S, Cuttaia C, Argentiero G, Livieri T, Cecchetto G, Oliva A, Fattorini P. Are pre-analytical factors fully considered in forensic FFPE molecular analyses? A systematic review reveals the need for standardised procedures. Int J Legal Med 2025:10.1007/s00414-025-03480-8. [PMID: 40172636 DOI: 10.1007/s00414-025-03480-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 03/19/2025] [Indexed: 04/04/2025]
Abstract
The need for molecular analyses has become increasingly common in the forensic sciences, particularly in forensic pathology, to better shape the causes of death. This approach is called the "molecular autopsy," where conventional medico-legal findings are often enhanced with specific molecular tests to provide reliable clinical and forensic diagnoses. In this context, FFPE (Formalin-Fixed Paraffin-Embedded) tissue samples collected during forensic autopsies are the only available specimens in retrospective studies for molecular DNA and/or RNA analyses. It is well known that pre-analytical parameters such as the agonal time, the PMI (Post-Mortem Interval), the fixation procedures, and the FFPE ageing and storage conditions can deeply impact the quality and quantity of the recovered nucleic acids, thus influencing the reliability of the downstream molecular tests. In the present study, we reviewed the recent forensic literature to establish whether these parameters are reported. Our survey showed that up to 34.9% and 40.5% of the 50 selected studies on DNA and RNA, respectively, reported the pre-analytical parameters mentioned above. Many publications did not report the length of agony (if any), which is an important parameter in RNA-based studies and estimations of the PMI; in addition, even relevant information on formalin tissue fixation procedures was often missing, thus impairing any critical evaluation of the PCR-based results. To address these issues, we propose the use of a simple form we set up to be filled out by Forensic Pathologists, where each pre-analytical step concerning the tissue samples collected during autopsy is accurately described and reported. In our opinion, this standardization will help the forensic community compare and evaluate the results of different molecular tests, thus increasing the reliability of the molecular results in forensics.
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Affiliation(s)
- Carlo Previderè
- Department of Public Health, Experimental and Forensic Medicine, Section of Legal Medicine and Forensic Sciences, University of Pavia, 27100, Pavia, Italy
| | - Serena Bonin
- Department of Medical Sciences, University of Trieste, 34149, Trieste, Italy
| | - Calogero Cuttaia
- Department of Medical Sciences, University of Trieste, 34149, Trieste, Italy
| | - Gianmarco Argentiero
- Department of Public Health, Experimental and Forensic Medicine, Section of Legal Medicine and Forensic Sciences, University of Pavia, 27100, Pavia, Italy
| | - Tommaso Livieri
- Department of Medical Sciences, University of Trieste, 34149, Trieste, Italy
| | - Giovanni Cecchetto
- Department of Public Health, Experimental and Forensic Medicine, Section of Legal Medicine and Forensic Sciences, University of Pavia, 27100, Pavia, Italy
| | - Antonio Oliva
- Department of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - Paolo Fattorini
- Department of Medical Sciences, University of Trieste, 34149, Trieste, Italy.
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12
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Chen Y, Davidson NM, Wan YK, Yao F, Su Y, Gamaarachchi H, Sim A, Patel H, Low HM, Hendra C, Wratten L, Hakkaart C, Sawyer C, Iakovleva V, Lee PL, Xin L, Ng HEV, Loo JM, Ong X, Ng HQA, Wang J, Koh WQC, Poon SYP, Stanojevic D, Tran HD, Lim KHE, Toh SY, Ewels PA, Ng HH, Iyer NG, Thiery A, Chng WJ, Chen L, DasGupta R, Sikic M, Chan YS, Tan BOP, Wan Y, Tam WL, Yu Q, Khor CC, Wüstefeld T, Lezhava A, Pratanwanich PN, Love MI, Goh WSS, Ng SB, Oshlack A, Göke J. A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. Nat Methods 2025; 22:801-812. [PMID: 40082608 PMCID: PMC11978509 DOI: 10.1038/s41592-025-02623-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/04/2025] [Indexed: 03/16/2025]
Abstract
The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.
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Affiliation(s)
- Ying Chen
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Nadia M Davidson
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, Australia
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Yuk Kei Wan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Fei Yao
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Yan Su
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Hasindu Gamaarachchi
- School of Computer Science and Engineering, UNSW Sydney, Sydney, New South Wales, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Andre Sim
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | - Hwee Meng Low
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Christopher Hendra
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Laura Wratten
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | - Chelsea Sawyer
- Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - Viktoriia Iakovleva
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Division of Gastroenterology and Hepatology, Weill Cornell Medicine, New York, NY, USA
| | - Puay Leng Lee
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Lixia Xin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Hui En Vanessa Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Jia Min Loo
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Xuewen Ong
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Hui Qi Amanda Ng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Jiaxu Wang
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wei Qian Casslynn Koh
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Suk Yeah Polly Poon
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Dominik Stanojevic
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Hoang-Dai Tran
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Kok Hao Edwin Lim
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Shen Yon Toh
- National Cancer Centre Singapore, Singapore, Singapore
| | | | - Huck-Hui Ng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - N Gopalakrishna Iyer
- National Cancer Centre Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Alexandre Thiery
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Wee Joo Chng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Department of Hematology-Oncology, National University Cancer Institute of Singapore, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Leilei Chen
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ramanuj DasGupta
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Mile Sikic
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Yun-Shen Chan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Boon Ooi Patrick Tan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Yue Wan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wai Leong Tam
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Qiang Yu
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Chiea Chuan Khor
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Torsten Wüstefeld
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- National Cancer Centre Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Alexander Lezhava
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Ploy N Pratanwanich
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Chula Intelligent and Complex Systems Research Unit, Chulalongkorn University, Bangkok, Thailand
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wee Siong Sho Goh
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Sarah B Ng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Alicia Oshlack
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
| | - Jonathan Göke
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.
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13
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Willis C, White JD, Minto MS, Quach BC, Han S, Tao R, Shin JH, Deep-Soboslay A, Hyde TM, Mayfield RD, Webb BT, Johnson EO, Kleinman JE, Bierut LJ, Hancock DB. Gene expression differences associated with alcohol use disorder in human brain. Mol Psychiatry 2025; 30:1617-1626. [PMID: 39394458 PMCID: PMC11919698 DOI: 10.1038/s41380-024-02777-1] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 09/25/2024] [Accepted: 09/30/2024] [Indexed: 10/13/2024]
Abstract
Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (NNAc = 28 AUD, 29 non-AUD; NPFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.
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Affiliation(s)
- Caryn Willis
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
| | - Julie D White
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Melyssa S Minto
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Bryan C Quach
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Shizhong Han
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Ran Tao
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | | | - Thomas M Hyde
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - R Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - Bradley T Webb
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Dana B Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
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14
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Ura H, Hatanaka H, Togi S, Niida Y. Computational Comparison of Differential Splicing Tools for Targeted RNA Long-Amplicon Sequencing (rLAS). Int J Mol Sci 2025; 26:3220. [PMID: 40244027 PMCID: PMC11989494 DOI: 10.3390/ijms26073220] [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: 01/22/2025] [Revised: 03/24/2025] [Accepted: 03/27/2025] [Indexed: 04/18/2025] Open
Abstract
RNA sequencing (RNA-Seq) is a powerful technique for the quantification of transcripts and the analysis of alternative splicing. Previously, our laboratory developed the targeted RNA long-amplicon sequencing (rLAS) method, which has the advantage of allowing deep analysis of targeted specific transcripts. The computational tools for analyzing RNA-Seq data have boosted alternative splicing research by detecting and quantifying splicing events. However, the performance of these splicing tools has not yet been investigated for rLAS. Here, we evaluated the performance of four splicing tools (MAJIQ, rMATS, MISO, and SplAdder) using samples with different types of known splicing events (exon-skipping, multiple-exon-skipping, alternative 5' splicing, and alternative 3' splicing). MAJIQ was able to detect all of the types of events, but it was unable to detect one of the exon-skipping events. On the other hand, rMATS was able to detect all of the exon-skipping events. However, rMATS failed to detect other types of events besides exon-skipping events. Both MISO and SplAdder were unable to detect any of the events. These results indicate that MAJIQ presents better performance for the different types of splicing events in rLAS and that rMATS shows better performance for exon-skipping splicing events.
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Affiliation(s)
- Hiroki Ura
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan
| | - Hisayo Hatanaka
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan (S.T.); (Y.N.)
| | - Sumihito Togi
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan
| | - Yo Niida
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Ishikawa, Japan
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15
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Harms FL, Müller C, Kortüm F, Hempel M, Alawi M, Zaki MS, Elhossini RM, Abdel-Hamid MS, AlAbdi L, Alkuraya FS, Kurdi W, Celse T, Spodenkiewicz M, Laurens T, Dieterich K, Jagadeesh S, Salvankar S, Girisha KM, Kutsche K. Novel biallelic COL25A1 variants broaden the clinical spectrum from congenital cranial dysinnervation disorders to fetal lethal phenotypes. Eur J Hum Genet 2025:10.1038/s41431-025-01839-4. [PMID: 40158061 DOI: 10.1038/s41431-025-01839-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Revised: 01/27/2025] [Accepted: 03/19/2025] [Indexed: 04/01/2025] Open
Abstract
Biallelic variants in COL25A1 have been associated with isolated congenital cranial dysinnervation disorders (CCDDs) and arthrogryposis multiplex congenital (AMC) with or without CCDD. COL25A1 encodes collagen XXV that belongs to the subfamily of membrane-associated collagens with interrupted triple helices. COL25A1 contains four non-collagenous and three collagenous domains. Three alternatively spliced COL25A1 transcript variants are known. In mice, Col25a1 is required for intramuscular motor innervation and cranial motor neuron development. We report seven subjects with novel biallelic COL25A1 pathogenic variants, including three AMC-affected individuals, one of whom died in infancy, and four unrelated fetuses. We expand the associated phenotypic spectrum as fetuses showed lethal phenotypes including reduced or no movement, contractures, and hydrops in three and growth retardation and skeletal abnormalities in one. The molecular spectrum includes two microdeletions encompassing several 5' or 3' exons, two missense, one nonsense, one frameshift, and one variant affecting splicing. In fibroblasts of the subject who was compound heterozygous for the c.367G > C and c.1198G > T variants, we identified skipping of exon 3 in COL25A1 mRNAs due to the G-to-C change. These aberrantly spliced transcripts were subject to nonsense-mediated mRNA decay. Analysis of transcriptome sequencing data from primary human fibroblasts without COL25A1 pathogenic variants revealed novel COL25A1 exon-exon junctions and 13 not previously annotated alternatively spliced in-frame exons. We hypothesized that interindividual variation in the splicing of COL25A1 exons in different tissues may underlie the variable phenotypes in the affected individuals.
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Affiliation(s)
- Frederike L Harms
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Müller
- Bioinformatics Core, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fanny Kortüm
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maja Hempel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Malik Alawi
- Bioinformatics Core, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maha S Zaki
- Clinical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt
| | - Rasha M Elhossini
- Clinical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt
| | - Mohamed S Abdel-Hamid
- Medical Molecular Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt
| | - Lama AlAbdi
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Wesam Kurdi
- Department of Obstetrics and Gynecology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Tristan Celse
- Service de Génétique Médicale, Centre Hospitalier Universitaire de La Réunion, Saint-Denis, France
| | - Marta Spodenkiewicz
- Service de Génétique Médicale, Centre Hospitalier Universitaire de La Réunion, Saint-Denis, France
| | - Tiphany Laurens
- Service de Génétique Médicale, Centre Hospitalier Universitaire de La Réunion, Saint-Denis, France
| | - Klaus Dieterich
- Universite Grenoble Alpes, Inserm, U1209, CHU Grenoble Alpes, Grenoble, France
- Medical Genetics, Institute of Advanced Biosciences, Grenoble, France
| | | | | | - Katta M Girisha
- Suma Genomics Private Limited, Manipal, India
- Department of Medical Genetics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
- Department of Genetics, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman
| | - Kerstin Kutsche
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- German Center for Child and Adolescent Health (DZKJ), partner site Hamburg, Hamburg, Germany.
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16
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Yao J, Gazal S. Evaluating genetic-ancestry inference from single-cell RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.25.645175. [PMID: 40196550 PMCID: PMC11974901 DOI: 10.1101/2025.03.25.645175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Characterizing the ancestry of donors in single-cell RNA sequencing (scRNA-seq) studies is critical to ensure the genetic homogeneity of the dataset and reduce biases in analyses, to identify ancestry-specific regulatory mechanisms and understand their downstream role in diseases, and to ensure that existing datasets are representative of human genetic diversity. While scRNA-seq is now widely available, the information on the ancestry of the donors is often missing, hindering further analysis. Here we propose a framework to evaluate methods for inferring genetic-ancestry from genetic polymorphisms detected from scRNA-seq reads. We demonstrate that widely used tools (e.g., ADMIXTURE) provide accurate inference of genetic-ancestry and admixture proportions despite the limited number of genetic polymorphisms identified and imperfect variant calling from scRNA-seq reads. We inferred genetic-ancestry for 196 donors from four scRNA-seq datasets from the Human Cell Atlas and highlighted an extremely large proportion of donors of European ancestry. For researchers generating single-cell datasets, we recommend reporting genetic-ancestry inference for all donors and generating datasets that represent diverse ancestries.
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Affiliation(s)
- Jianing Yao
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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17
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Zhang Y, Qing J, Li Y, Gao X, Lu D, Wang Y, Gu L, Zhang H, Li Z, Wang X, Zhou Y, Zhang P. PRMT7-Mediated PTEN Activation Enhances Bone Regeneration in Female Mice. Int J Mol Sci 2025; 26:2981. [PMID: 40243588 PMCID: PMC11988880 DOI: 10.3390/ijms26072981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2025] [Revised: 03/17/2025] [Accepted: 03/20/2025] [Indexed: 04/18/2025] Open
Abstract
Epigenetic regulation provides new insights into the mechanisms of osteogenic differentiation and identifies potential targets for treating bone-related diseases. However, the specific regulatory networks and mechanisms involved still need further investigation. In this study, we identify PRMT7 as a novel epigenetic regulator of mesenchymal stem cells (MSCs) osteogenic commitment. Conditional knockout of Prmt7 in mice reveals a significant impairment in osteogenesis and bone regeneration, specifically in females, affecting both femurs and mandibles, with no noticeable effect in males. Mechanistically, PRMT7 modulates MSCs osteogenic differentiation by activating PTEN. Specifically, PRMT7 enhances PTEN transcription by increasing H3R2me1 levels at the PTEN promoter. Additionally, PRMT7 interacts with the PTEN protein and stabilizes nuclear PTEN, revealing an unprecedented pathway. Notably, overexpression of PTEN alleviates the osteogenic deficits observed in Prmt7-deficient mice. This research establishes PRMT7 as a potential therapeutic target for promoting bone formation/regeneration and offers novel molecular insights into the PRMT7-PTEN regulatory axis, underscoring its significance in bone biology and regenerative medicine.
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Affiliation(s)
- Yingfei Zhang
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Jia Qing
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Yang Li
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Xin Gao
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Dazhuang Lu
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Yiyang Wang
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Lanxin Gu
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Hui Zhang
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Zechuan Li
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Xu Wang
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Yongsheng Zhou
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
| | - Ping Zhang
- Department of Prosthodontics, Peking University Hospital of Stomatology, Beijing 100081, China; (Y.Z.); (J.Q.); (Y.L.); (X.G.); (D.L.); (Y.W.); (L.G.); (H.Z.); (Z.L.); (X.W.)
- National Clinical Research Center for Oral Diseases, Peking University Hospital of Stomatology, Beijing 100081, China
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18
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Johansen VBI, Gradel AKJ, Holm SK, Cuenco J, Merrild C, Petersen N, Demozay D, Mani BK, Suppli MP, Grøndahl MFG, Lund AB, Knop FK, Prada-Medina CA, Hogendorf WFJ, Lykkesfeldt J, Merkestein M, Sakamoto K, Holst B, Clemmensen C. Regulation of LEAP2 by insulin and glucagon in mice and humans. Cell Rep Med 2025; 6:101996. [PMID: 40056903 PMCID: PMC11970398 DOI: 10.1016/j.xcrm.2025.101996] [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: 02/12/2024] [Revised: 09/14/2024] [Accepted: 02/10/2025] [Indexed: 03/10/2025]
Abstract
Liver-expressed antimicrobial peptide 2 (LEAP2) is an endogenous antagonist and inverse agonist of the ghrelin receptor, countering ghrelin's effects on cell signaling and feeding. However, despite an emerging interest in LEAP2's physiology and pharmacology, its endocrine regulation remains unclear. Here, we report that plasma LEAP2 levels decrease significantly upon glucagon infusions during somatostatin clamps in humans. This effect is preserved in patients with obesity and type 2 diabetes while diminished following a hypercaloric diet and a sedentary lifestyle for 2 weeks. Additionally, insulin receptor antagonism offsets the upregulation of LEAP2 during the postprandial state in mice. Finally, insulin and glucagon receptor-expressing hepatocytes are the primary source of hepatic LEAP2 expression, coinciding with a putative enhancer-like signature bound by insulin- and glucagon-regulated transcription factors at the LEAP2 locus. Collectively, our findings implicate insulin and glucagon in regulating LEAP2 and warrant further investigations into the exact mechanisms orchestrating this endocrine axis.
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Affiliation(s)
- Valdemar Brimnes Ingemann Johansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Diabetes Pharmacology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark; Diabetes and Metabolism Biology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Anna Katrina Jógvansdóttir Gradel
- Diabetes Pharmacology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark; Section of Preclinical Disease Biology, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie Kjærulff Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joyceline Cuenco
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christoffer Merrild
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Natalia Petersen
- Diabetes and Metabolism Biology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Damien Demozay
- Diabetes and Metabolism Biology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Bharath Kumar Mani
- Obesity and NASH Research, Global Drug Discovery, Novo Nordisk, Lexington, MA, USA
| | - Malte Palm Suppli
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Magnus F G Grøndahl
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Asger Bach Lund
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Filip Krag Knop
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Cesar A Prada-Medina
- Systems Biology and Target Discovery, AI and Digital Research, Novo Nordisk Research Center Oxford, Novo Nordisk A/S, Oxford, UK
| | | | - Jens Lykkesfeldt
- Section of Preclinical Disease Biology, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Myrte Merkestein
- Diabetes Pharmacology, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Kei Sakamoto
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Holst
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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19
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Guo K, Zhong Z, Zeng H, Zhang C, Chitotombe TT, Teng J, Gao Y, Zhang Z. Comparative analysis of genotype imputation strategies for SNPs calling from RNA-seq. BMC Genomics 2025; 26:245. [PMID: 40082746 PMCID: PMC11907794 DOI: 10.1186/s12864-025-11411-5] [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: 12/14/2024] [Accepted: 02/27/2025] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND RNA sequencing (RNA-seq) is a powerful tool for transcriptome profiling, enabling integrative studies of expression quantitative trait loci (eQTL). As it identifies fewer genetic variants than DNA sequencing (DNA-seq), reference panel-based genotype imputation is often required to enhance its utility. RESULTS This study evaluated the accuracy of genotype imputation using SNPs called from RNA-seq data (RNA-SNPs). SNP features from 6,567 RNA-seq samples across 28 pig tissues were used to mask whole genome sequencing (WGS) data, with the Pig Genomic Reference Panel (PGRP) serving as the reference panel. Three imputation software tools (i.e., Beagle, Minimac4, and Impute5) were employed to perform the imputation. The result showed that RNA-SNPs achieved higher imputation accuracy (CR: 0.895 ~ 0.933; r²: 0.745 ~ 0.817) than SNPs from GeneSeek Genomic Profiler Porcine SNP50 BeadChip (Chip-SNPs) (CR: 0.873 ~ 0.909; r²: 0.629 ~ 0.698), and lower accuracy in "intergenic" regions. After imputation, quality control (QC) by minor allele frequency (MAF) and imputation quality (DR²) could improve r² but reduce SNP retention. Among software, Minimac4 takes the least runtime in single-thread setting, while Beagle performed best in multi-thread setting and phasing. Impute5 takes up minimal memory usage but requires the maximum runtime. All tools demonstrated comparable global accuracy (CR: 0.906 ~ 0.917; r²: 0.780 ~ 0.787). CONCLUSIONS This study offers practical guidance for conducting RNA-SNP imputation strategies in genome and transcriptome research.
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Affiliation(s)
- Kaixuan Guo
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Zhanming Zhong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Changliang Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Teddy Tinashe Chitotombe
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
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20
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Asiaee A, Abrams ZB, Pua HH, Coombes KR. Transcriptome Complexity Disentangled: A Regulatory Molecules Approach. Int J Mol Sci 2025; 26:2510. [PMID: 40141153 PMCID: PMC11942001 DOI: 10.3390/ijms26062510] [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: 01/05/2025] [Revised: 02/20/2025] [Accepted: 02/25/2025] [Indexed: 03/28/2025] Open
Abstract
Transcription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue-Aware model attained an R2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.
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Affiliation(s)
- Amir Asiaee
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA
| | - Zachary B. Abrams
- Institute for Informatics, Washington University, 4444 Forest Park Avenue, St. Louis, MO 63108, USA;
| | - Heather H. Pua
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, 1161 Medical Center Drive, Nashville, TN 37240, USA;
| | - Kevin R. Coombes
- Department of Population Health Science, Medical College of Georgia, 1120 15th Street, Augusta, GA 30912, USA;
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21
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Asiaee A, Abrams ZB, Pua HH, Coombes KR. Transcriptome Complexity Disentangled: A Regulatory Molecules Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2023.04.17.537241. [PMID: 37131792 PMCID: PMC10153180 DOI: 10.1101/2023.04.17.537241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Transcription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue-Aware model attained anR 2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.
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Affiliation(s)
- Amir Asiaee
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA
| | - Zachary B. Abrams
- Institute for Informatics, Washington University, 4444 Forest Park Avenue, St. Louis, MO 63108, USA
| | - Heather H. Pua
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, 1161 Medical Center Drive, Nashville, TN 37240, USA
| | - Kevin R. Coombes
- Department of Population Health Science, Medical College of Georgia, 1120 15th Street, Augusta, GA 30912, USA
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22
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Hysi PG, Hammond CJ. An Ocular Gene-Set Expression Library for Heritability Partition and Cell Line Enrichment Analyses. Invest Ophthalmol Vis Sci 2025; 66:11. [PMID: 40042876 PMCID: PMC11892535 DOI: 10.1167/iovs.66.3.11] [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: 07/14/2024] [Accepted: 12/23/2024] [Indexed: 03/12/2025] Open
Abstract
Purpose Use of genome-wide association studies (GWASs) in combination with transcriptomic arrays of different tissues or cell lines can reveal relevant cellular profiles and significantly improve understanding of the mechanisms of diseases. However, due to difficulty of access, few ocular transcriptomics datasets are available. This work aimed to create and make available an expression library platform that can be used with popular and versatile tools such as the linkage disequilibrium score (LDSC) regression techniques to identify specific cell lines enriched in ocular diseases. Methods We used transcriptome information from six publicly available single-cell and single-nucleus RNA sequence datasets to obtain matrices of normalized gene expression and estimated enrichment of the 10% most expressed transcripts in each cell line. We tested for type 1 error using simulated GWAS datasets and then used LDSC regression analyses to study the enrichment in different eye diseases. Results Gene expression databases for over 197 ocular cell lines were generated. Simulations of 1000 random GWASs of varying sample sizes showed no genomic inflation. Cell line-specific analyses of GWAS results found that genes near single nucleotide polymorphisms (SNPs) associated with refractive error were significantly enriched in cones (P = 0.008), rods (P = 0.002) and peripheral retina Müller cells (P = 0.002), juxtacanalicular cribriform cells (P = 0.0017), stromal keratocytes (P = 0.0018), and one beam-cell subpopulation (P = 0.0028) in primary open-angle glaucoma, emphasizing the importance of intraocular pressure in its etiology. Conclusions We have built a structured ocular transcriptomics library to estimate cell line enrichment among association signals from genome-wide association studies that may be extended by incorporating other datasets. We identified cells that appear important in the genetics of common eye diseases.
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Affiliation(s)
- Pirro G. Hysi
- Academic Ophthalmology, King's College London, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Sørlandet Sykehus Arendal, Arendal, Norway
| | - Christopher J. Hammond
- Academic Ophthalmology, King's College London, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
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23
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Velz J, Freudenmann LK, Medici G, Dubbelaar M, Mohme M, Ghasemi DR, Scheid J, Kowalewski DJ, Patterson AB, Zeitlberger AM, Lamszus K, Westphal M, Eyrich M, Messing-Jünger M, Röhrig A, Reinhard H, Beccaria K, Craveiro RB, Frey BM, Sill M, Nahnsen S, Gauder M, Kapolou K, Silginer M, Weiss T, Wirsching HG, Roth P, Grotzer M, Krayenbühl N, Bozinov O, Regli L, Rammensee HG, Rushing EJ, Sahm F, Walz JS, Weller M, Neidert MC. Mapping naturally presented T cell antigens in medulloblastoma based on integrative multi-omics. Nat Commun 2025; 16:1364. [PMID: 39904979 PMCID: PMC11794601 DOI: 10.1038/s41467-025-56268-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/14/2025] [Indexed: 02/06/2025] Open
Abstract
Medulloblastoma is the most frequent malignant primary brain tumor in children. Despite recent advances in integrated genomics, the prognosis in children with high-risk medulloblastoma remains devastating, and new tumor-specific therapeutic approaches are needed. Here, we present an atlas of naturally presented T cell antigens in medulloblastoma. We map the human leukocyte antigen (HLA)-presented peptidomes of 28 tumors and perform comparative immunopeptidome profiling against an in-house benign database. Medulloblastoma is shown to be a rich source of tumor-associated antigens, naturally presented on HLA class I and II molecules. Remarkably, most tumor-associated peptides and proteins are subgroup-specific, whereas shared presentation among all subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) is rare. Functional testing of top-ranking novel candidate antigens demonstrates the induction of peptide-specific T cell responses, supporting their potential for T cell immunotherapy. This study is an in-depth mapping of naturally presented T cell antigens in medulloblastoma. Integration of immunopeptidomics, transcriptomics, and epigenetic data leads to the identification of a large set of actionable targets that can be further used for the translation into the clinical setting by facilitating the informed design of immunotherapeutic approaches to children with medulloblastoma.
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Affiliation(s)
- Julia Velz
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
- Divison of Pediatric Neurosurgery, University Children's Hospital Zurich, Zurich, Switzerland
| | - Lena K Freudenmann
- Institute of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
| | - Gioele Medici
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Marissa Dubbelaar
- Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - David R Ghasemi
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonas Scheid
- Institute of Immunology, University of Tübingen, Tübingen, Germany
- Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | | | - Angelica B Patterson
- Institute of Immunobiology, Cantonal Hospital St.Gallen, St. Gallen, Switzerland
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
| | - Anna M Zeitlberger
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Eyrich
- Department of Pediatric Haematology, Oncology and Stem Cell Transplantation, University Children's Hospital, University Medical Center, University of Würzburg, Würzburg, Germany
| | | | - Andreas Röhrig
- Department of Neurosurgery, Asklepios Children's Hospital, Sankt Augustin, Germany
| | - Harald Reinhard
- Department of Pediatrics, Asklepios Children's Hospital, Sankt Augustin, Germany
| | - Kévin Beccaria
- Department of Pediatric Neurosurgery, Necker Enfants Malades Hospital, APHP, Université Paris Cite, Paris, France
| | - Rogeiro B Craveiro
- Department of Orthodontic, Dental Clinic, University Hospital of RWTH Aachen, Aachen, Germany
| | - Beat M Frey
- Blood Transfusion Service, Swiss Red Cross, Schlieren, Switzerland
| | - Martin Sill
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Department for Computer Science, Biomedical Data Science, University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital, Tübingen, Baden- Württemberg, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Marie Gauder
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
| | - Konstantina Kapolou
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Manuela Silginer
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Hans-Georg Wirsching
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Patrick Roth
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Grotzer
- Department of Oncology, University Children's Hospital Zürich, Zürich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
- Divison of Pediatric Neurosurgery, University Children's Hospital Zurich, Zurich, Switzerland
| | - Oliver Bozinov
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Hans-Georg Rammensee
- Institute of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Elisabeth J Rushing
- Department of Neuropathology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, and CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Juliane S Walz
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
- Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Michael Weller
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Marian C Neidert
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland.
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24
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Li S, Liu J, Xu W, Zhang S, Zhao M, Miao L, Hui M, Wang Y, Hou Y, Cong B, Wang Z. A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification. Forensic Sci Int Genet 2025; 75:103180. [PMID: 39591840 DOI: 10.1016/j.fsigen.2024.103180] [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: 06/30/2024] [Revised: 09/30/2024] [Accepted: 11/19/2024] [Indexed: 11/28/2024]
Abstract
MicroRNAs (miRNAs) are promising biomarkers for forensic body fluid identification owing to their small size, stability against degradation, and differential expression patterns. However, the expression of most body fluid-miRNAs is relative (differentially expressed in certain body fluids) rather than absolute (exclusively expressed in a specific body fluid). Moreover, different body fluids contain heterogeneous cell types, complicating their identification. Therefore, appropriate normalization strategies to eliminate non-biological variations and robust models to interpret expression levels accurately are necessary prerequisites for applying miRNAs in body fluid identification. In this study, the expression stability of six candidate reference genes (RGs) across five body fluids was validated using geNorm, NormFinder, BestKeeper and RankAggreg, and the most suitable combination of RGs (hsa-miR-484 and hsa-miR-191-5p) was identified under our experimental conditions. Subsequently, we systematically evaluated the expression patterns of the 28 most promising body fluid-specific miRNA markers using TaqMan RT-qPCR and selected the optimal combination of markers (12 miRNAs) to establish a multi-class support vector machine (MSVM) classification model. An independent test set (60 samples) was used to validate the accuracy of the proposed classification model, while an additional 30 casework samples were used to assess its robustness. The MSVM model accurately predicted the body fluid origin for almost all (59/60) single-source samples. Moreover, this model demonstrated the capability to identify aged forensic samples and to predict the primary components of mixed stains to a certain extent. In summary, this study presented a miRNA-based MSVM classification model for forensic body fluid identification using the qPCR platform. However, extensive validation, especially inter-laboratory collaborative exercises, is necessary before miRNA can be routinely applied in forensic identification practice.
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Affiliation(s)
- Suyu Li
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Shijiazhuang 050017, China
| | - Wei Xu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Criminal Investigation Detachment of Huainan Public Security Bureau, Huainan 232000, China
| | - Shuyuan Zhang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Mengyao Zhao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Lu Miao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Criminal Investigation Detachment of Huainan Public Security Bureau, Huainan 232000, China
| | - Minxiao Hui
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Yuan Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Anhui Hopegenerich Biotechnology, Hefei 230031, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Shijiazhuang 050017, China.
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
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25
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DeMeo DL. Sex, Gender, and COPD. Annu Rev Physiol 2025; 87:471-490. [PMID: 39586033 DOI: 10.1146/annurev-physiol-042022-014322] [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: 11/27/2024]
Abstract
Sex and gender have emerged as critical considerations relevant to chronic obstructive pulmonary disease (COPD). Sex differences in lung development and physiologic response to hormones and environmental exposures influence COPD susceptibility, progression, severity, morbidity, and mortality. Gender has been poorly measured in the context of COPD, and gendered exposures further impact biology. The hormonal milieu is critical to study across the life course. Differences in immunity and inflammation likely impact sex- and gender-related features of COPD. Emerging evidence from multiple types of omics data is revealing new genes and pathways to consider as relevant to sex- and gender-divergent features of COPD. Much research to date has focused on autosomes, but the growing awareness of a role for allosomes is highlighting knowledge gaps. Reproductive aging impacts lung function and requires more investigation. Network medicine holds promise as an approach to sex and gender omics to uncover drivers of COPD in men and women.
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Affiliation(s)
- Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
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26
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Porter RS, An S, Gavilan MC, Nagai M, Murata-Nakamura Y, Zhou B, Bonefas KM, Dionne O, Manuel JM, St-Germain J, Gascon S, Kim J, Browning L, Laurent B, Cho US, Iwase S. Coordinated neuron-specific splicing events restrict nucleosome engagement of the LSD1 histone demethylase complex. Cell Rep 2025; 44:115213. [PMID: 39817906 PMCID: PMC11864812 DOI: 10.1016/j.celrep.2024.115213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/15/2024] [Accepted: 12/24/2024] [Indexed: 01/18/2025] Open
Abstract
Chromatin regulatory proteins are expressed broadly and assumed to exert the same intrinsic function across cell types. Here, we report that 14 chromatin regulators undergo evolutionary-conserved neuron-specific splicing events involving microexons. Among them are two components of a histone demethylase complex: LSD1 H3K4 demethylase and the H3K4me0-reader PHF21A. We found that neuronal LSD1 splicing reduces the enzymes' affinity to the nucleosome. Meanwhile, neuronal PHF21A splicing significantly attenuates histone H3 binding and further ablates the DNA-binding function exerted by an AT-hook motif. Furthermore, in vitro reconstitution of the canonical and neuronal PHF21A-LSD1 complexes, combined with in vivo methylation mapping, identified the neuronal complex as a hypomorphic H3K4 demethylating machinery. The neuronal PHF21A, albeit with its weaker nucleosome binding, is necessary for normal gene expression and the H3K4 landscape in the developing brain. Thus, ubiquitously expressed chromatin regulatory complexes can exert neuron-specific functions via alternative splicing of their subunits.
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Affiliation(s)
- Robert S Porter
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Sojin An
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Maria C Gavilan
- Genetics and Genomics Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Masayoshi Nagai
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Yumie Murata-Nakamura
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Bo Zhou
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Katherine M Bonefas
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Olivier Dionne
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jeru Manoj Manuel
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Joannie St-Germain
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Suzanne Gascon
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Jacqueline Kim
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Liam Browning
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Benoit Laurent
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Uhn-Soo Cho
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Shigeki Iwase
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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27
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Fang H, Jiang L, da Veiga Leprevost F, Jian R, Chan J, Glinos D, Lappalainen T, Nesvizhskii AI, Reiner AP, Consortium GTE, Snyder MP, Tang H. Regulation of protein abundance in normal human tissues. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.01.10.25320181. [PMID: 39867362 PMCID: PMC11759590 DOI: 10.1101/2025.01.10.25320181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2025]
Abstract
We report a systematic quantification of 10,841 unique proteins from over 700 GTEx samples, representing five human tissues. Sex, age and genetic factors are associated with variation in protein abundance. In total, 1981 cis-protein quantitative trait loci (cis-pQTL) are identified, of which a majority of protein targets have not been assayed in the recent plasma-based proteogenomic studies. Integrating transcriptomic information from matching tissues delineates concordant as well as discordant expression patterns at RNA and protein levels. Juxtaposition of data from different tissues indicates both shared and tissue-specific genetic architecture that underlie protein abundance. Complementing genomic annotation, RNA-based eQTL studies, as well as the recent establishment of plasma-based proteogenomic characterization, tissue-pQTLs shed light on biology underlying genotype-phenotype association of complex traits and diseases.
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Gu LL, Wu HS, Liu TY, Zhang YJ, He JC, Liu XL, Wang ZY, Chen GB, Jiang D, Fang M. Rapid and accurate multi-phenotype imputation for millions of individuals. Nat Commun 2025; 16:387. [PMID: 39755672 DOI: 10.1038/s41467-024-55496-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/11/2024] [Indexed: 01/06/2025] Open
Abstract
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. We then apply PIXANT to the UKB data of 277,301 unrelated White British citizens and 425 traits, and GWAS is subsequently performed on the imputed phenotypes, 18.4% more GWAS loci are identified than before imputation (8710 vs 7355). The increased statistical power of GWAS identified some additional candidate genes affecting heart rate, such as RNF220, SCN10A, and RGS6, suggesting that the use of imputed phenotype data from a large cohort may lead to the discovery of additional candidate genes for complex traits.
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Affiliation(s)
- Lin-Lin Gu
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Hong-Shan Wu
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Tian-Yi Liu
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Yong-Jie Zhang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Jing-Cheng He
- Center for Data Science, School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Xiao-Lei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, People's Republic of China
| | - Zhi-Yong Wang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Guo-Bo Chen
- Center for General Practice Medicine, Department of General Practice Medicine, Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, People's Republic of China.
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, Zhejiang, People's Republic of China.
| | - Dan Jiang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China.
| | - Ming Fang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China.
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Hancks DC. An Evolutionary Framework Exploiting Virologs and Their Host Origins to Inform Poxvirus Protein Functions. Methods Mol Biol 2025; 2860:257-272. [PMID: 39621273 DOI: 10.1007/978-1-0716-4160-6_17] [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: 12/13/2024]
Abstract
Poxviruses represent evolutionary successful infectious agents. As a family, poxviruses can infect a wide variety of species including humans, fish, and insects. While many other viruses are species-specific, an individual poxvirus species is often capable of infecting diverse hosts and cell types. For example, the prototypical poxvirus, vaccinia, is well known to infect numerous human cell types but can also infect cells from divergent hosts like frog neurons. Notably, poxvirus infections result in both detrimental human and animal diseases. The most infamous disease linked to a poxvirus is smallpox caused by variola virus. Poxviruses are large double-stranded DNA viruses, which uniquely replicate in the cytoplasm of cells. The model poxvirus genome encodes ~200 nonoverlapping protein-coding open reading frames (ORFs). Poxvirus gene products impact various biological processes like the production of virus particles, the host range of infectivity, and disease pathogenesis. In addition, poxviruses and their gene products have biomedical application with several species commonly engineered for use as vaccines and oncolytic virotherapy. Nevertheless, we still have an incomplete understanding of the functions associated with many poxvirus genes. In this chapter, we outline evolutionary insights that can complement ongoing studies of poxvirus gene functions and biology, which may serve to elucidate new molecular activities linked to this biomedically relevant class of viruses.
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Affiliation(s)
- Dustin C Hancks
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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Liu Z, Wang J, Li Z, Zhang G. mRNA for Body Fluid and Individual Identification. Electrophoresis 2025; 46:44-55. [PMID: 39498727 DOI: 10.1002/elps.202400077] [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: 04/21/2024] [Revised: 08/02/2024] [Accepted: 10/20/2024] [Indexed: 11/07/2024]
Abstract
Biological stains are one of the most important pieces of evidence, playing a multifaceted role in forensic investigations. An integral facet of forensic practice involves the identification of body fluids, typically achieved through chemical and enzymatic reactions. In recent decades, the introduction of mRNA markers has been posited as a pivotal advancement to augment the capabilities of body fluid identification (BFID). The mRNA coding region single-nucleotide polymorphisms (cSNPs) also present notable advantages, particularly in the task of individual identification. Here, we review the specificity and stability of mRNA markers in the context of BFID and the prowess of mRNA polymorphism in individual identification. Additionally, innovative methods for mRNA detection are discussed.
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Affiliation(s)
- Zidong Liu
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
| | - Jiaqi Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
| | - Zeqin Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
| | - Gengqian Zhang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
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Fu M, Lu S, Gong L, Zhou Y, Wei F, Duan Z, Xiang R, Gonzalez FJ, Li G. Intermittent fasting shifts the diurnal transcriptome atlas of transcription factors. Mol Cell Biochem 2025; 480:491-504. [PMID: 38528297 DOI: 10.1007/s11010-024-04928-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/05/2024] [Indexed: 03/27/2024]
Abstract
Intermittent fasting remains a safe and effective strategy to ameliorate various age-related diseases, but its specific mechanisms are not fully understood. Considering that transcription factors (TFs) determine the response to environmental signals, here, we profiled the diurnal expression of 600 samples across four metabolic tissues sampled every 4 over 24 h from mice placed on five different feeding regimens to provide an atlas of TFs in biological space, time, and feeding regimen. Results showed that 1218 TFs exhibited tissue-specific and temporal expression profiles in ad libitum mice, of which 974 displayed significant oscillations at least in one tissue. Intermittent fasting triggered more than 90% (1161 in 1234) of TFs to oscillate somewhere in the body and repartitioned their tissue-specific expression. A single round of fasting generally promoted TF expression, especially in skeletal muscle and adipose tissues, while intermittent fasting mainly suppressed TF expression. Intermittent fasting down-regulated aging pathway and upregulated the pathway responsible for the inhibition of mammalian target of rapamycin (mTOR). Intermittent fasting shifts the diurnal transcriptome atlas of TFs, and mTOR inhibition may orchestrate intermittent fasting-induced health improvements. This atlas offers a reference and resource to understand how TFs and intermittent fasting may contribute to diurnal rhythm oscillation and bring about specific health benefits.
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Affiliation(s)
- Min Fu
- Department of Neurology, The Fourth Hospital of Changsha, Affiliated Changsha Hospital of Hunan Normal University, Changsha, 410006, Hunan, China
| | - Siyu Lu
- Key Laboratory of Hunan Province for Model Animal and Stem Cell Biology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Lijun Gong
- Key Laboratory of Hunan Province for Model Animal and Stem Cell Biology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Yiming Zhou
- Key Laboratory of Hunan Province for Model Animal and Stem Cell Biology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Fang Wei
- Department of Neurology, The Fourth Hospital of Changsha, Affiliated Changsha Hospital of Hunan Normal University, Changsha, 410006, Hunan, China.
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China.
| | - Zhigui Duan
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Rong Xiang
- Department of Cell Biology, School of Life Sciences, Central South University, Changsha, 41001, Hunan, China
| | - Frank J Gonzalez
- Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Guolin Li
- Key Laboratory of Hunan Province for Model Animal and Stem Cell Biology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China.
- Center for Aging Biomedicine, National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China.
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Montibus B, Cain JA, Martinez-Nunez RT, Oakey RJ. Global identification of mammalian host and nested gene pairs reveal tissue-specific transcriptional interplay. Genome Res 2024; 34:2163-2175. [PMID: 39578100 PMCID: PMC11694760 DOI: 10.1101/gr.279430.124] [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: 04/03/2024] [Accepted: 10/17/2024] [Indexed: 11/24/2024]
Abstract
Nucleotide sequences along a gene provide instructions to transcriptional and cotranscriptional machinery allowing genome expansion into the transcriptome. Nucleotide sequence can often be shared between two genes and in some occurrences, a gene is located completely within a different gene; these are known as host/nested gene pairs. In these instances, if both genes are transcribed, overlap can result in a transcriptional crosstalk where genes regulate each other. Despite this, a comprehensive annotation of where such genes are located and their expression patterns is lacking. To address this, we provide an up-to-date catalog of host/nested gene pairs in mouse and human, showing that over a tenth of all genes contain a nested gene. We discovered that transcriptional co-occurrence is often tissue specific. This coexpression was especially prevalent within the transcriptionally permissive tissue, testis. We use this developmental system and scRNA-seq analysis to demonstrate that the coexpression of pairs can occur in single cells and transcription in the same place at the same time can enhance the transcript diversity of the host gene. In agreement, host genes are more transcript-diverse than the rest of the transcriptome. Host/nested gene configurations are common in both human and mouse, suggesting that interplay between gene pairs is a feature of the mammalian genome. This highlights the relevance of transcriptional crosstalk between genes which share nucleic acid sequence. The results and analysis are available on an Rshiny application (https://hngeneviewer.sites.er.kcl.ac.uk/hn_viewer/).
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Affiliation(s)
- Bertille Montibus
- Department of Medical and Molecular Genetics, King's College London, London SE1 9RT, United Kingdom;
| | - James A Cain
- Department of Medical and Molecular Genetics, King's College London, London SE1 9RT, United Kingdom
| | - Rocio T Martinez-Nunez
- Department of Infectious Diseases, King's College London, London SE1 9RT, United Kingdom
| | - Rebecca J Oakey
- Department of Medical and Molecular Genetics, King's College London, London SE1 9RT, United Kingdom;
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Liu H, Nie X, Wang F, Chen D, Zeng Z, Shu P, Huang J. An integrated transcriptomic analysis of brain aging and strategies for healthy aging. Front Aging Neurosci 2024; 16:1450337. [PMID: 39713269 PMCID: PMC11659761 DOI: 10.3389/fnagi.2024.1450337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 11/18/2024] [Indexed: 12/24/2024] Open
Abstract
Background It is been noted that the expression levels of numerous genes undergo changes as individuals age, and aging stands as a primary factor contributing to age-related diseases. Nevertheless, it remains uncertain whether there are common aging genes across organs or tissues, and whether these aging genes play a pivotal role in the development of age-related diseases. Methods In this study, we screened for aging genes using RNAseq data of 32 human tissues from GTEx. RNAseq datasets from GEO were used to study whether aging genes drives age-related diseases, or whether anti-aging solutions could reverse aging gene expression. Results Aging transcriptome alterations showed that brain aging differ significantly from the rest of the body, furthermore, brain tissues were divided into four group according to their aging transcriptome alterations. Numerous genes were downregulated during brain aging, with functions enriched in synaptic function, ubiquitination, mitochondrial translation and autophagy. Transcriptome analysis of age-related diseases and retarding aging solutions showed that downregulated aging genes in the hippocampus further downregulation in Alzheimer's disease but were effectively reversed by high physical activity. Furthermore, the neuron loss observed during aging was reversed by high physical activity. Conclusion The downregulation of many genes is a major contributor to brain aging and neurodegeneration. High levels of physical activity have been shown to effectively reactivate these genes, making it a promising strategy to slow brain aging.
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Affiliation(s)
- Haiying Liu
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xin Nie
- HBN Research Institute and Biological Laboratory, Shenzhen Hujia Technology Co., Ltd., Shenzhen, Guangdong, China
| | - Fengwei Wang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Dandan Chen
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhuo Zeng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Peng Shu
- HBN Research Institute and Biological Laboratory, Shenzhen Hujia Technology Co., Ltd., Shenzhen, Guangdong, China
- State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, Xinjiang, China
| | - Junjiu Huang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
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Colonna G. Interactomic Analyses and a Reverse Engineering Study Identify Specific Functional Activities of One-to-One Interactions of the S1 Subunit of the SARS-CoV-2 Spike Protein with the Human Proteome. Biomolecules 2024; 14:1549. [PMID: 39766256 PMCID: PMC12121346 DOI: 10.3390/biom14121549] [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: 10/03/2024] [Revised: 11/21/2024] [Accepted: 11/22/2024] [Indexed: 06/01/2025] Open
Abstract
The S1 subunit of SARS-CoV-2 Spike is crucial for ACE2 recognition and viral entry into human cells. It has been found in the blood of COVID-19 patients and vaccinated individuals. Using BioGRID, I identified 146 significant human proteins that interact with S1. I then created an interactome model that made it easier to study functional activities. Through a reverse engineering approach, 27 specific one-to-one interactions of S1 with the human proteome were selected. S1 interacts in this manner independently from the biological context in which it operates, be it infection or vaccination. Instead, when it works together with viral proteins, they carry out multiple attacks on single human proteins, showing a different functional engagement. The functional implications and tropism of the virus for human organs/tissues were studied using Cytoscape. The nervous system, liver, blood, and lungs are among the most affected. As a single protein, S1 operates in a complex metabolic landscape which includes 2557 Biological Processes (GO), much more than the 1430 terms controlled when operating in a group. A Data Merging approach shows that the total proteins involved by S1 in the cell are over 60,000 with an average involvement per single biological process of 26.19. However, many human proteins become entangled in over 100 different biological activities each. Clustering analysis showed significant activations of many molecular mechanisms, like those related to hepatitis B infections. This suggests a potential involvement in carcinogenesis, based on a viral strategy that uses the ubiquitin system to impair the tumor suppressor and antiviral functions of TP53, as well as the role of RPS27A in protein turnover and cellular stress responses.
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Affiliation(s)
- Giovanni Colonna
- Unit of Medical Informatics-AOU Luigi Vanvitelli, University of Campania, 80138 Naples, Italy
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Margasyuk S, Kuznetsova A, Zavileyskiy L, Vlasenok M, Skvortsov D, Pervouchine D. Human introns contain conserved tissue-specific cryptic poison exons. NAR Genom Bioinform 2024; 6:lqae163. [PMID: 39664813 PMCID: PMC11632617 DOI: 10.1093/nargab/lqae163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 10/10/2024] [Accepted: 11/10/2024] [Indexed: 12/13/2024] Open
Abstract
Eukaryotic cells express a large number of transcripts from a single gene due to alternative splicing. Despite hundreds of thousands of splice isoforms being annotated in databases, it has been reported that the current exon catalogs remain incomplete. At the same time, introns of human protein-coding (PC) genes contain a large number of evolutionarily conserved elements with unknown function. Here, we explore the possibility that some of them represent cryptic exons that are expressed in rare conditions. We identified a group of cryptic exons that are similar to the annotated exons in terms of evolutionary conservation and RNA-seq read coverage in the Genotype-Tissue Expression dataset. Most of them were poison, i.e. generated an nonsense-mediated decay (NMD) isoform upon inclusion, and many showed signs of tissue-specific and cancer-specific expression and regulation. We performed RNA-seq in A549 cell line treated with cycloheximide to inactivate NMD and confirmed using quantitative polymerase chain reaction that seven of eight exons tested are, indeed, expressed. This study shows that introns of human PC genes contain cryptic poison exons, which reside in conserved intronic regions and remain not fully annotated due to insufficient representation in RNA-seq libraries.
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Affiliation(s)
- Sergey Margasyuk
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
| | - Antonina Kuznetsova
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
| | - Lev Zavileyskiy
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
| | - Maria Vlasenok
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
| | - Dmitry Skvortsov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
- Faculty of Chemistry, Moscow State University, Ul Kolmogorova, 1, 119991, Moscow, Russia
| | - Dmitri D Pervouchine
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Bulvar, 30, 121205, Moscow, Russia
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Wang P, Li F, Sun Y, Li Y, Xie X, Du X, Liu L, Wu Y, Song D, Xiong H, Chen J, Li X. Novel insights into the circadian modulation of lipid metabolism in chicken livers revealed by RNA sequencing and weighted gene co-expression network analysis. Poult Sci 2024; 103:104321. [PMID: 39361997 PMCID: PMC11474196 DOI: 10.1016/j.psj.2024.104321] [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: 07/19/2024] [Revised: 09/06/2024] [Accepted: 09/07/2024] [Indexed: 10/05/2024] Open
Abstract
The circadian clock is crucial for maintaining lipid metabolism homeostasis in mammals. Despite the economic importance of fat content in poultry, research on the regulatory effects and molecular mechanisms of the circadian clock on avian hepatic lipid metabolism has been limited. In this study, we observed significant diurnal variations (P<0.05) in triglyceride (TG), free fatty acids (FFA), fatty acid synthase (FAS), and total cholesterol (TC) levels in the chicken embryonic liver under 12-h light/12-h dark incubation conditions, with TG, FFA, and TC concentrations showing significant cosine rhythmic oscillations (P<0.05). However, such rhythmic variations were not observed under complete darkness incubation conditions. Using transcriptome sequencing technology, we identified 157 genes significantly upregulated at night and 313 genes significantly upregulated during the 12-h light/12-h dark cycle. These circadian differential genes are involved in processes and pathways such as lipid catabolic process regulation, meiotic cell cycle, circadian rhythm regulation, positive regulation of the MAPK cascade, and glycerolipid metabolism. Weighted gene co-expression network analysis (WGCNA) revealed 3 modules-green, blue, and red-that significantly correlate with FFA, FAS, and TG, respectively. Genes within these modules were enriched in processes and pathways including the cell cycle, light stimulus response, circadian rhythm regulation, phosphorylation, positive regulation of the MAPK cascade, and lipid biosynthesis. Notably, we identified ten hub genes, including protein kinase C delta (PRKCD), polo like kinase 4 (PLK4), clock circadian regulator (CLOCK), steroid 5 alpha-reductase 3 (SRD5A3), BUB1 mitotic checkpoint serine/threonine kinase (BUB1B), shugoshin 1 (SGO1), NDC80 kinetochore complex component (NDC80), NIMA related kinase 2 (NEK2), minichromosome maintenance complex component 4 (MCM4), polo like kinase 1 (PLK1), potentially link circadian regulation with lipid metabolic homeostasis. These findings demonstrate the regulatory role of the circadian clock in chicken liver lipid metabolism homeostasis and provide a theoretical basis and molecular targets for optimizing the circadian clock to reduce excessive fat deposition in chickens, which is significant for the healthy development of the poultry industry.
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Affiliation(s)
- Panlin Wang
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Fang Li
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Yanyan Sun
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yunlei Li
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xiuyu Xie
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Xue Du
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Lu Liu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Yongshu Wu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Dan Song
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
| | - Hui Xiong
- Beijing Seeme Medical Technology Co Ltd, Beijing, 100093, China
| | - Jilan Chen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Xiangchen Li
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China.
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Sullivan KA, Kainer D, Lane M, Cashman M, Miller JI, Garvin MR, Townsend A, Quach BC, Willis C, Kruse P, Gaddis NC, Mathur R, Corradin O, Maher BS, Scacheri PC, Sanchez-Roige S, Palmer AA, Troiani V, Chesler EJ, Kember RL, Kranzler HR, Justice AC, Xu K, Aouizerat BE, Hancock DB, Johnson EO, Jacobson DA, VA Million Veteran Program. Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction. Biol Psychiatry 2024:S0006-3223(24)01781-5. [PMID: 39615775 PMCID: PMC12119972 DOI: 10.1016/j.biopsych.2024.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 11/03/2024] [Accepted: 11/18/2024] [Indexed: 01/25/2025]
Abstract
BACKGROUND Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. METHODS To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals. RESULTS We identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression. CONCLUSIONS Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.
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Affiliation(s)
- Kyle A Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - David Kainer
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Matthew Lane
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, Tennessee
| | - Mikaela Cashman
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - J Izaak Miller
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Michael R Garvin
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Alice Townsend
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, Tennessee
| | - Bryan C Quach
- RTI International, Research Triangle Park, North Carolina
| | - Caryn Willis
- RTI International, Research Triangle Park, North Carolina
| | - Peter Kruse
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, Tennessee
| | | | - Ravi Mathur
- RTI International, Research Triangle Park, North Carolina
| | - Olivia Corradin
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, California; Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, California; Institute for Genomic Medicine, University of California San Diego, La Jolla, California
| | - Vanessa Troiani
- Geisinger College of Health Sciences, Scranton, Pennsylvania
| | | | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut; Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
| | - Ke Xu
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut; Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Bradley E Aouizerat
- Bluestone Center for Clinical Research, College of Dentistry, New York University, New York, New York
| | - Dana B Hancock
- RTI International, Research Triangle Park, North Carolina.
| | - Eric O Johnson
- RTI International, Research Triangle Park, North Carolina; Fellow Program, RTI International, Research Triangle Park, North Carolina.
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
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Hooda Y, Sente A, Judy RM, Smalinskaitė L, Peak-Chew SY, Naydenova K, Malinauskas T, Hardwick SW, Chirgadze DY, Aricescu AR, Hegde RS. Mechanism of NACHO-mediated assembly of pentameric ligand-gated ion channels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.28.620708. [PMID: 39553992 PMCID: PMC11565801 DOI: 10.1101/2024.10.28.620708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
Pentameric ligand-gated ion channels (pLGICs) are cell surface receptors of crucial importance for animal physiology1-4. This diverse protein family mediates the ionotropic signals triggered by major neurotransmitters and includes γ-aminobutyric acid receptors (GABAARs) and acetylcholine receptors (nAChRs). Receptor function is fine-tuned by a myriad of endogenous and pharmacological modulators3. A functional pLGIC is built from five homologous, sometimes identical, subunits, each containing a β-scaffold extracellular domain (ECD), a four-helix transmembrane domain (TMD) and intracellular loops of variable length. Although considerable progress has been made in understanding pLGICs in structural and functional terms, the molecular mechanisms that enable their assembly at the endoplasmic reticulum (ER)5 in a vast range of potential subunit configurations6 remain unknown. Here, we identified candidate pLGICs assembly factors selectively associated with an unassembled GABAAR subunit. Focusing on one of the candidates, we determined the cryo-EM structure of an assembly intermediate containing two α1 subunits of GABAAR each bound to an ER-resident membrane protein NACHO. The structure showed how NACHO shields the principal (+) transmembrane interface of α1 subunits containing an immature extracellular conformation. Crosslinking and structure-prediction revealed an adjacent surface on NACHO for β2 subunit interactions to guide stepwise oligimerisation. Mutations of either subunit-interacting surface on NACHO also impaired the formation of homopentameric α7 nAChRs, pointing to a generic framework for pLGIC assembly. Our work provides the foundation for understanding the regulatory principles underlying pLGIC structural diversity.
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Affiliation(s)
- Yogesh Hooda
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
- Equal contribution
| | - Andrija Sente
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
- Equal contribution
| | - Ryan M. Judy
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
- Equal contribution
| | - Luka Smalinskaitė
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
| | - Sew-Yeu Peak-Chew
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
| | | | - Tomas Malinauskas
- Division of Structural Biology, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Steven W. Hardwick
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, United Kingdom
| | - Dimitri Y. Chirgadze
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, United Kingdom
| | - A. Radu Aricescu
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom
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Sposato AL, Hollins HL, Llewellyn DR, Weber JM, Schrock MN, Farrell JA, Gagnon JA. Germ cell progression through zebrafish spermatogenesis declines with age. Development 2024; 151:dev204319. [PMID: 39470160 DOI: 10.1242/dev.204319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Accepted: 10/14/2024] [Indexed: 10/30/2024]
Abstract
Vertebrate spermatogonial stem cells maintain sperm production over the lifetime of an animal, but fertility declines with age. Although morphological studies have informed our understanding of typical spermatogenesis, the molecular and cellular mechanisms underlying the maintenance and decline of spermatogenesis are not yet understood. We used single-cell RNA sequencing to generate a developmental atlas of the aging zebrafish testis. All testes contained spermatogonia, but we observed a progressive decline in spermatogenesis that correlated with age. Testes from some older males only contained spermatogonia and a reduced population of spermatocytes. Spermatogonia in older males were transcriptionally distinct from spermatogonia in testes capable of robust spermatogenesis. Immune cells including macrophages and lymphocytes drastically increased in abundance in testes that could not complete spermatogenesis. Our developmental atlas reveals the cellular changes as the testis ages and defines a molecular roadmap for the regulation of spermatogenesis.
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Affiliation(s)
- Andrea L Sposato
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Hailey L Hollins
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Darren R Llewellyn
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jenna M Weber
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Madison N Schrock
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jeffrey A Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - James A Gagnon
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
- Henry Eyring Center for Cell and Genome Science, University of Utah, Salt Lake City, UT 84112, USA
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Thomas MJ, Xu H, Wang A, Beg MA, Sorci-Thomas MG. PCPE2: Expression of multifunctional extracellular glycoprotein associated with diverse cellular functions. J Lipid Res 2024; 65:100664. [PMID: 39374805 PMCID: PMC11567036 DOI: 10.1016/j.jlr.2024.100664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 09/21/2024] [Accepted: 10/01/2024] [Indexed: 10/09/2024] Open
Abstract
Procollagen C-endopeptidase enhancer 2, known as PCPE2 or PCOC2 (gene name, PCOLCE2) is a glycoprotein that resides in the extracellular matrix, and is similar in domain organization to PCPE1/PCPE, PCOC1 (PCOLCE1/PCOLCE). Due to the many similarities between the two related proteins, PCPE2 has been assumed to have biological functions similar to PCPE. PCPE is a well-established enhancer of procollagen processing activating the enzyme, BMP-1. However, reports show that PCPE2 has a strikingly different tissue expression profile compared to PCPE. With that in mind and given the paucity of published studies on PCPE2, this review examines the current literature citing PCPE2 and its association with specific cell types and signaling pathways. Additionally, this review will present a brief history of PCPE2's discovery, highlighting structural and functional similarities and differences compared to PCPE. Considering the widespread use of RNA sequencing techniques to examine associations between cell-specific gene expression and disease states, we will show that PCPE2 is repeatedly found as a differentially regulated gene (DEG) significantly associated with a number of cellular processes, well beyond the scope of procollagen fibril processing.
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Affiliation(s)
- Michael J Thomas
- Division of Endocrinology and Molecular Medicine, Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA; Cardiovascular Research Center, Division of Endocrinology and Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Hao Xu
- Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Angela Wang
- Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mirza Ahmar Beg
- Division of Endocrinology and Molecular Medicine, Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA; Cardiovascular Research Center, Division of Endocrinology and Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA; Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mary G Sorci-Thomas
- Division of Endocrinology and Molecular Medicine, Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA; Cardiovascular Research Center, Division of Endocrinology and Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA; Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
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41
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Bencomo T, Lee CS. Gene expression landscape of cutaneous squamous cell carcinoma progression. Br J Dermatol 2024; 191:760-774. [PMID: 38867481 DOI: 10.1093/bjd/ljae249] [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: 12/12/2023] [Revised: 04/14/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Cutaneous squamous cell carcinomas (cSCCs) are the second most common human cancer and have been characterized by RNA sequencing (RNA-Seq); however, the transferability of findings from individual studies may be limited by small sample sizes and diverse analysis protocols. OBJECTIVES To define the transcriptome landscape at different stages in the progression of normal skin to cSCC via a meta-analysis of publicly available RNA-Seq samples. METHODS Whole-transcriptome data from 73 clinically normal skin samples, 46 actinic keratoses (AK) samples, 16 in situ SCC samples, 13 keratoacanthoma (KA) samples and 147 cSCC samples [including 30 samples from immunocompromised patients and 8 from individuals with recessive dystrophic epidermolysis bullosa (RDEB)] were uniformly processed to harmonize gene expression. Differential expression, fusion detection and cell-type deconvolution analyses were performed. RESULTS Individual RNA-Seq studies of cSCC demonstrated study-specific clustering and varied widely in their differential gene expression detection. Following batch correction, we defined a consensus set of differentially expressed genes (DEGs), including those altered in the preinvasive stages of cSCC development, and used single-cell RNA-Seq data to demonstrate that DEGs are often - but not always - expressed by tumour-specific keratinocytes (TSKs). Analysis of the cellular composition of cSCC, KA and RDEB-cSCC identified an increase in differentiated keratinocytes in KA, while RDEB-cSCC contained the most TSKs. Compared with cSCC arising in immunocompetent individuals, cSCC samples from immunosuppressed patients demonstrated fewer memory B cells and CD8+ T cells. A comprehensive and unbiased search for fusion transcripts in cSCC and intermediate disease stages identified few candidates that recurred in >1% of all specimens, suggesting that most cSCC are not driven by oncogenic gene fusions. Finally, using Genotype-Tissue Expression (GTEx) data, we distilled a novel 300-gene signature of chronic sun exposure that affirms greater cumulative ultraviolet (UV) exposure in later stages of cSCC development. CONCLUSIONS Our results define the gene expression landscape of cSCC progression, characterize cell subpopulation heterogeneity in cSCC subtypes that contribute to their distinct clinical phenotypes, demonstrate that gene fusions are not a common cause of cSCC and identify UV-responsive genes associated with cSCC development.
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Affiliation(s)
- Tomas Bencomo
- Stanford Program in Epithelial Biology, Stanford University, Stanford, CA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Carolyn S Lee
- Stanford Program in Epithelial Biology, Stanford University, Stanford, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
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Chang T, Alvarez J, Chappidi S, Crockett S, Sorouri M, Orchard RC, Hancks DC. Metabolic reprogramming tips vaccinia virus infection outcomes by stabilizing interferon-γ induced IRF1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.10.617691. [PMID: 39416205 PMCID: PMC11482883 DOI: 10.1101/2024.10.10.617691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Interferon (IFN) induced activities are critical, early determinants of immune responses and infection outcomes. A key facet of IFN responses is the upregulation of hundreds of mRNAs termed interferon-stimulated genes (ISGs) that activate intrinsic and cell-mediated defenses. While primary interferon signaling is well-delineated, other layers of regulation are less explored but implied by aberrant ISG expression signatures in many diseases in the absence of infection. Consistently, our examination of tonic ISG levels across uninfected human tissues and individuals revealed three ISG subclasses. As tissue identity and many comorbidities with increased virus susceptibility are characterized by differences in metabolism, we characterized ISG responses in cells grown in media known to favor either aerobic glycolysis (glucose) or oxidative phosphorylation (galactose supplementation). While these conditions over time had a varying impact on the expression of ISG RNAs, the differences were typically greater between treatments than between glucose/galactose. Interestingly, extended interferon-priming led to divergent expression of two ISG proteins: upregulation of IRF1 in IFN-γ/glucose and increased IFITM3 in galactose by IFN-α and IFN-γ. In agreement with a hardwired response, glucose/galactose regulation of interferon-γ induced IRF1 is conserved in unrelated mouse and cat cell types. In galactose conditions, proteasome inhibition restored interferon-γ induced IRF1 levels to that of glucose/interferon-γ. Glucose/interferon-γ decreased replication of the model poxvirus vaccinia at low MOI and high MOIs. Vaccinia replication was restored by IRF1 KO. In contrast, but consistent with differential regulation of IRF1 protein by glucose/galactose, WT and IRF1 KO cells in galactose media supported similar levels of vaccinia replication regardless of IFN-γ priming. Also associated with glucose/galactose is a seemingly second block at a very late stage in viral replication which results in reductions in herpes- and poxvirus titers but not viral protein expression. Collectively, these data illustrate a novel layer of regulation for the key ISG protein, IRF1, mediated by glucose/galactose and imply unappreciated subprograms embedded in the interferon response. In principle, such cellular circuitry could rapidly adapt immune responses by sensing changing metabolite levels consumed during viral replication and cell proliferation.
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Ren X, Zheng L, Maliskova L, Tam TW, Sun Y, Liu H, Lee J, Takagi MA, Li B, Ren B, Wang W, Shen Y. CRISPR tiling deletion screens reveal functional enhancers of neuropsychiatric risk genes and allelic compensation effects (ACE) on transcription. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.08.616922. [PMID: 39416108 PMCID: PMC11483005 DOI: 10.1101/2024.10.08.616922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Precise transcriptional regulation is critical for cellular function and development, yet the mechanism of this process remains poorly understood for many genes. To gain a deeper understanding of the regulation of neuropsychiatric disease risk genes, we identified a total of 39 functional enhancers for four dosage-sensitive genes, APP, FMR1, MECP2, and SIN3A, using CRISPR tiling deletion screening in human induced pluripotent stem cell (iPSC)-induced excitatory neurons. We found that enhancer annotation provides potential pathological insights into disease-associated copy number variants. More importantly, we discovered that allelic enhancer deletions at SIN3A could be compensated by increased transcriptional activities from the other intact allele. Such allelic compensation effects (ACE) on transcription is stably maintained during differentiation and, once established, cannot be reversed by ectopic SIN3A expression. Further, ACE at SIN3A occurs through dosage sensing by the promoter. Together, our findings unravel a regulatory compensation mechanism that ensures stable and precise transcriptional output for SIN3A, and potentially other dosage-sensitive genes.
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Affiliation(s)
- Xingjie Ren
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Lina Zheng
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Lenka Maliskova
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Tsz Wai Tam
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Yifan Sun
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Hongjiang Liu
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Jerry Lee
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Maya Asami Takagi
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Wei Wang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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Usmanova DR, Plata G, Vitkup D. Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution. Mol Biol Evol 2024; 41:msae200. [PMID: 39431545 PMCID: PMC11523136 DOI: 10.1093/molbev/msae200] [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: 04/18/2024] [Revised: 07/29/2024] [Accepted: 08/05/2024] [Indexed: 10/22/2024] Open
Abstract
Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.
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Affiliation(s)
- Dinara R Usmanova
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Germán Plata
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
- BiomEdit, Fishers, IN 46037, USA
| | - Dennis Vitkup
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
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Cortes-Guzman MA, Treviño V. CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners. PLoS One 2024; 19:e0309961. [PMID: 39365797 PMCID: PMC11451983 DOI: 10.1371/journal.pone.0309961] [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: 01/18/2024] [Accepted: 08/21/2024] [Indexed: 10/06/2024] Open
Abstract
MOTIVATION Coexpression estimations are helpful for analysis of pathways, cofactors, regulators, targets, and human health and disease. Ideally, coexpression estimations should consider as many diverse cell types as possible and consider that available data is not uniform across tissues. Importantly, the coexpression estimations accessible today are performed on a "tissue level", which is based on cell type standardized formulations. Little or no attention is paid to overall gene expression levels. The tissue-level estimation assumes that variance expression levels are more important than mean expression levels. Here, we challenge this assumption by estimating a coexpression calculation at the "system level", which is estimated without standardization by tissue, and show that it provides valuable information. We made available a resource to view, download, and analyze both, tissue- and system-level coexpression estimations from GTEx human data. METHODS GTEx v8 expression data was globally normalized, batch-processed, and filtered. Then, PCA, clustering, and tSNE stringent procedures were applied to generate 42 distinct and curated tissue clusters. Coexpression was estimated from these 42 tissue clusters computing the correlation of 33,445 genes by sampling 70 samples per tissue cluster to avoid tissue overrepresentation. This process was repeated 20 times, extracting the minimum value provided as a robust estimation. Three metrics were calculated (Pearson, Spearman, and G-statistic) in two data processing modes, at the system-level (TPM scale) and tissue levels (z-score scale). RESULTS We first validate our tissue-level estimations compared with other databases. Then, by specific analyses in several examples and literature validations of predictions, we show that system-level coexpression estimation differs from tissue-level estimations and that both contain valuable information reflected in biological pathways. We also show that coexpression estimations are associated to transcriptional regulation. Finally, we present CoGTEx, a valuable resource for viewing and analyzing coexpressed genes in human adult tissues from GTEx v8 data. We introduce our web resource to list, view and explore the coexpressed genes from GTEx data. CONCLUSION We conclude that system-level coexpression is a novel and interesting coexpression metric capable of generating plausible predictions and biological hypotheses; and that CoGTEx is a valuable resource to view, compare, and download system- and tissue- level coexpression estimations from GTEx data. AVAILABILITY The web resource is available at http://bioinformatics.mx/cogtex.
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Affiliation(s)
| | - Víctor Treviño
- Tecnologico de Monterrey, Escuela de Medicina, Bioinformática, Monterrey, Nuevo León, México
- Tecnologico de Monterrey, OriGen Project, Monterrey, Nuevo León, México
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Pang W, Zhu J, Yang K, Zhu X, Zhou W, Jiang L, Zhuang X, Liu Y, Wei J, Lu X, Yin Y, Chen Z, Xiang Y. Generation of human region-specific brain organoids with medullary spinal trigeminal nuclei. Cell Stem Cell 2024; 31:1501-1512.e8. [PMID: 39208804 DOI: 10.1016/j.stem.2024.08.004] [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: 01/10/2024] [Revised: 06/16/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
Brain organoids with nucleus-specific identities provide unique platforms for studying human brain development and diseases at a finer resolution. Despite its essential role in vital body functions, the medulla of the hindbrain has seen a lack of in vitro models, let alone models resembling specific medullary nuclei, including the crucial spinal trigeminal nucleus (SpV) that relays peripheral sensory signals to the thalamus. Here, we report a method to differentiate human pluripotent stem cells into region-specific brain organoids resembling the dorsal domain of the medullary hindbrain. Importantly, organoids specifically recapitulated the development of the SpV derived from the dorsal medulla. We also developed an organoid system to create the trigeminothalamic projections between the SpV and the thalamus by fusing these organoids, namely human medullary SpV-like organoids (hmSpVOs), with organoids representing the thalamus (hThOs). Our study provides a platform for understanding SpV development, nucleus-based circuit organization, and related disorders in the human brain.
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Affiliation(s)
- Wei Pang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jinkui Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Kexin Yang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xiaona Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wei Zhou
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Linlin Jiang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xuran Zhuang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yantong Liu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jianfeng Wei
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xiaoxiang Lu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yao Yin
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Ziling Chen
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yangfei Xiang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China.
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Wang J, Zhang Z, Lu Z, Mancuso N, Gazal S. Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions. Am J Hum Genet 2024; 111:2117-2128. [PMID: 39191255 PMCID: PMC11480800 DOI: 10.1016/j.ajhg.2024.07.021] [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: 12/21/2023] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/29/2024] Open
Abstract
Multi-ancestry genome-wide association studies (GWASs) have highlighted the existence of variants with ancestry-specific effect sizes. Understanding where and why these ancestry-specific effects occur is fundamental to understanding the genetic basis of human diseases and complex traits. Here, we characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-sequencing data in peripheral blood mononuclear cells for 21 individuals with East Asian (EAS) ancestry and 23 individuals with European (EUR) ancestry (172,385 cells); then, we tested whether variants surrounding those genes were enriched in disease variants with ancestry-specific effect sizes by leveraging ancestry-matched GWASs of 31 diseases and complex traits (average n ∼ 90,000 and ∼ 267,000 in EAS and EUR, respectively). We observed that ancDE genes tended to be cell-type specific and enriched in genes interacting with the environment and in variants with ancestry-specific disease effect sizes, which suggests cell-type-specific, gene-by-environment interactions shared between regulatory and disease architectures. Finally, we illustrated how different environments might have led to ancestry-specific myeloid cell leukemia 1 (MCL1) expression in B cells and ancestry-specific allele effect sizes in lymphocyte count GWASs for variants surrounding MCL1. Our results imply that large single-cell and GWAS datasets from diverse ancestries are required to improve our understanding of human diseases.
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Affiliation(s)
- Juehan Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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48
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Chang T, Alvarez J, Chappidi S, Crockett S, Sorouri M, Orchard RC, Hancks DC. Metabolic reprogramming tips vaccinia virus infection outcomes by stabilizing interferon-γ induced IRF1. PLoS Pathog 2024; 20:e1012673. [PMID: 39475961 PMCID: PMC11554218 DOI: 10.1371/journal.ppat.1012673] [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: 07/10/2024] [Revised: 11/11/2024] [Accepted: 10/16/2024] [Indexed: 11/06/2024] Open
Abstract
Interferon (IFN) induced activities are critical, early determinants of immune responses and infection outcomes. A key facet of IFN responses is the upregulation of hundreds of mRNAs termed interferon-stimulated genes (ISGs) that activate intrinsic and cell-mediated defenses. While primary interferon signaling is well-delineated, other layers of regulation are less explored but implied by aberrant ISG expression signatures in many diseases in the absence of infection. Consistently, our examination of tonic ISG levels across uninfected human tissues and individuals revealed three ISG subclasses. As tissue identity and many comorbidities with increased virus susceptibility are characterized by differences in metabolism, we characterized ISG responses in cells grown in media known to favor either aerobic glycolysis (glucose) or oxidative phosphorylation (galactose supplementation). While these conditions over time had a varying impact on the expression of ISG RNAs, the differences were typically greater between treatments than between glucose/galactose. Interestingly, extended interferon-priming led to divergent expression of two ISG proteins: upregulation of IRF1 in IFN-γ/glucose and increased IFITM3 in galactose by IFN-α and IFN-γ. In agreement with a hardwired response, glucose/galactose regulation of interferon-γ induced IRF1 is conserved in unrelated mouse and cat cell types. In galactose conditions, proteasome inhibition restored interferon-γ induced IRF1 levels to that of glucose/interferon-γ. Glucose/interferon-γ decreased replication of the model poxvirus vaccinia at low MOI and high MOIs. Vaccinia replication was restored by IRF1 KO. In contrast, but consistent with differential regulation of IRF1 protein by glucose/galactose, WT and IRF1 KO cells in galactose media supported similar levels of vaccinia replication regardless of IFN-γ priming. Also associated with glucose/galactose is a seemingly second block at a very late stage in viral replication which results in reductions in herpes- and poxvirus titers but not viral protein expression. Collectively, these data illustrate a novel layer of regulation for the key ISG protein, IRF1, mediated by glucose/galactose and imply unappreciated subprograms embedded in the interferon response. In principle, such cellular circuitry could rapidly adapt immune responses by sensing changing metabolite levels consumed during viral replication and cell proliferation.
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Affiliation(s)
- Tyron Chang
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Genetics, Development, and Disease Ph.D. program, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Jessica Alvarez
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Molecular Microbiology Ph.D. program, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Sruthi Chappidi
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Stacey Crockett
- Molecular Microbiology Ph.D. program, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Mahsa Sorouri
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Robert C. Orchard
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Dustin C. Hancks
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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49
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Yazarlou F, Martinez I, Lipovich L. Radiotherapy and breast cancer: finally, an lncRNA perspective on radiosensitivity and radioresistance. Front Oncol 2024; 14:1437542. [PMID: 39346726 PMCID: PMC11427263 DOI: 10.3389/fonc.2024.1437542] [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: 05/23/2024] [Accepted: 08/01/2024] [Indexed: 10/01/2024] Open
Abstract
Radiotherapy (RT) serves as one of the key adjuvant treatments in management of breast cancer. Nevertheless, RT has two major problems: side effects and radioresistance. Given that patients respond differently to RT, it is imperative to understand the molecular mechanisms underlying these differences. Two-thirds of human genes do not encode proteins, as we have realized from genome-scale studies conducted after the advent of the genomic era; nevertheless, molecular understanding of breast cancer to date has been attained almost entirely based on protein-coding genes and their pathways. Long non-coding RNAs (lncRNAs) are a poorly understood but abundant class of human genes that yield functional non-protein-coding RNA transcripts. Here, we canvass the field to seek evidence for the hypothesis that lncRNAs contribute to radioresistance in breast cancer. RT-responsive lncRNAs ranging from "classical" lncRNAs discovered at the dawn of the post-genomic era (such as HOTAIR, NEAT1, and CCAT), to long intergenic lncRNAs such as LINC00511 and LINC02582, antisense lncRNAs such as AFAP-AS1 and FGD5-AS1, and pseudogene transcripts such as DUXAP8 were found during our screen of the literature. Radiation-related pathways modulated by these lncRNAs include DNA damage repair, cell cycle, cancer stem cells phenotype and apoptosis. Thus, providing a clear picture of these lncRNAs' underlying RT-relevant molecular mechanisms should help improve overall survival and optimize the best radiation dose for each individual patient. Moreover, in healthy humans, lncRNAs show greater natural expression variation than protein-coding genes, even across individuals, alluding to their exceptional potential for targeting in truly personalized, precision medicine.
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Affiliation(s)
- Fatemeh Yazarlou
- Center for Childhood Cancer, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, United States
| | - Ivan Martinez
- Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV, United States
| | - Leonard Lipovich
- Department of Biology, College of Science, Mathematics, and Technology, Wenzhou-Kean University, Wenzhou, China
- Wenzhou Municipal Key Laboratory for Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou, China
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou, China
- Shenzhen Huayuan Biological Science Research Institute, Shenzhen Huayuan Biotechnology Co. Ltd., Shenzhen, China
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, United States
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50
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Sahu SK, Reddy P, Lu J, Shao Y, Wang C, Tsuji M, Delicado EN, Rodriguez Esteban C, Belmonte JCI. Targeted partial reprogramming of age-associated cell states improves markers of health in mouse models of aging. Sci Transl Med 2024; 16:eadg1777. [PMID: 39259812 DOI: 10.1126/scitranslmed.adg1777] [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: 12/07/2022] [Revised: 11/13/2023] [Accepted: 08/22/2024] [Indexed: 09/13/2024]
Abstract
Aging is a complex multifactorial process associated with epigenome dysregulation, increased cellular senescence, and decreased rejuvenation capacity. Short-term cyclic expression of octamer-binding transcription factor 4 (Oct4), sex-determining region Y-box 2 (Sox2), Kruppel-like factor 4 (Klf4), and cellular myelocytomatosis oncogene (cMyc) (OSKM) in wild-type mice improves health but fails to distinguish cell states, posing risks to healthy cells. Here, we delivered a single dose of adeno-associated viruses (AAVs) harboring OSK under the control of the cyclin-dependent kinase inhibitor 2a (Cdkn2a) promoter to specifically partially reprogram aged and stressed cells in a mouse model of Hutchinson-Gilford progeria syndrome (HGPS). Mice showed reduced expression of proinflammatory cytokines and extended life spans upon aged cell-specific OSK expression. The bone marrow and spleen, in particular, showed pronounced gene expression changes, and partial reprogramming in aged HGPS mice led to a shift in the cellular composition of the hematopoietic stem cell compartment toward that of young mice. Administration of AAVs carrying Cdkn2a-OSK to naturally aged wild-type mice also delayed aging phenotypes and extended life spans without altering the incidence of tumor development. Furthermore, intradermal injection of AAVs carrying Cdkn2a-OSK led to improved wound healing in aged wild-type mice. Expression of CDKN2A-OSK in aging or stressed human primary fibroblasts led to reduced expression of inflammation-related genes but did not alter the expression of cell cycle-related genes. This targeted partial reprogramming approach may therefore facilitate the development of strategies to improve health and life span and enhance resilience in the elderly.
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Affiliation(s)
| | | | | | | | - Chao Wang
- Altos Labs, San Diego, CA 92122, USA
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