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Li X, Zhang G, Wang M, Lu C, Zhang G, Chen Z, Ji Y. Comparison of stromal vascular fraction cell composition between Coleman fat and extracellular matrix/stromal vascular fraction gel. Adipocyte 2024; 13:2360037. [PMID: 38829527 PMCID: PMC11152101 DOI: 10.1080/21623945.2024.2360037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
As a mechanically condensed product of Coleman fat, extracellular matrix/stromal vascular fraction gel (ECM/SVF-gel) eliminates adipocytes, concentrates SVF cells, and improves fat graft retention. This study aims to compare SVF cell composition between Coleman fat and ECM/SVF-gel. Matched Coleman fat and ECM/SVF-gel of 28 healthy women were subjected to RNA-seq, followed by functional enrichment and cell-type-specific enrichment analyses, and deconvolution of SVF cell subsets, reconstructing SVF cell composition in the transcriptome level. ECM/SVF-gels had 9 upregulated and 73 downregulated differentially expressed genes (DEGs). Downregulated DEGs were mainly associated with inflammatory and immune responses, and enriched in fat macrophages. M2 macrophages, resting CD4+ memory T cells, M1 macrophages, resting mast cells, and M0 macrophages ranked in the top five most prevalent immune cells in the two groups. The proportions of the principal non-immune cells (e.g., adipose-derived stem cells, pericytes, preadipocytes, microvascular endothelial cells) had no statistical differences between the two groups. Our findings reveal ECM/SVF-gels share the same dominant immune cells beneficial to fat graft survival with Coleman fat, but exhibiting obvious losses of immune cells (especially macrophages), while non-immune cells necessary for adipose regeneration might have no significant loss in ECM/SVF-gels and their biological effects could be markedly enhanced by the ECM/SVF-gel's condensed nature.
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Affiliation(s)
- Xiaoyun Li
- Department of Pathology, Shantou University Medical College, Shantou, China
| | - Guohong Zhang
- Department of Pathology, Shantou University Medical College, Shantou, China
| | - Mengmeng Wang
- Medical Cosmetic Center, the First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Changhao Lu
- Department of Pathology, Shantou University Medical College, Shantou, China
| | - Guangping Zhang
- Department of Pathology, Shantou University Medical College, Shantou, China
| | - Zhehui Chen
- Medical Cosmetic Center, the First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yingchang Ji
- Medical Cosmetic Center, the First Affiliated Hospital of Shantou University Medical College, Shantou, China
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2
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Lee S, Weiss T, Bühler M, Mena J, Lottenbach Z, Wegmann R, Sun M, Bihl M, Augustynek B, Baumann SP, Goetze S, van Drogen A, Pedrioli PGA, Penton D, Festl Y, Buck A, Kirschenbaum D, Zeitlberger AM, Neidert MC, Vasella F, Rushing EJ, Wollscheid B, Hediger MA, Weller M, Snijder B. High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity. Nat Med 2024:10.1038/s41591-024-03224-y. [PMID: 39304781 DOI: 10.1038/s41591-024-03224-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 07/31/2024] [Indexed: 09/22/2024]
Abstract
Glioblastoma, the most aggressive primary brain cancer, has a dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we systematically screened repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling more than 2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug-target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of more than 1 million compounds with high patient validation accuracy. Deep multimodal profiling confirmed Ca2+-driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the anti-depressant vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology.
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Affiliation(s)
- Sohyon Lee
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marcel Bühler
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Julien Mena
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Zuzanna Lottenbach
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Rebekka Wegmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Miaomiao Sun
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michel Bihl
- Institute of Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Bartłomiej Augustynek
- Membrane Transport Discovery Lab, Department of Nephrology and Hypertension and Department of Biomedical Research, Inselspital, University of Bern, Bern, Switzerland
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bern, Switzerland
| | - Sven P Baumann
- Membrane Transport Discovery Lab, Department of Nephrology and Hypertension and Department of Biomedical Research, Inselspital, University of Bern, Bern, Switzerland
| | - Sandra Goetze
- Department of Health Sciences and Technology, Institute of Translational Medicine (ITM), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - Audrey van Drogen
- Department of Health Sciences and Technology, Institute of Translational Medicine (ITM), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - Patrick G A Pedrioli
- Department of Health Sciences and Technology, Institute of Translational Medicine (ITM), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - David Penton
- Electrophysiology Facility, University of Zurich, Zurich, Switzerland
| | - Yasmin Festl
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Alicia Buck
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniel Kirschenbaum
- Department of Neuropathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Anna M Zeitlberger
- Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Marian C Neidert
- Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Flavio Vasella
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Elisabeth J Rushing
- Department of Neuropathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bernd Wollscheid
- Department of Health Sciences and Technology, Institute of Translational Medicine (ITM), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - Matthias A Hediger
- Membrane Transport Discovery Lab, Department of Nephrology and Hypertension and Department of Biomedical Research, Inselspital, University of Bern, Bern, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Comprehensive Cancer Center Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Berend Snijder
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Comprehensive Cancer Center Zurich, University Hospital Zurich, Zurich, Switzerland.
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3
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Xie Y, Zhao Y, Zhou Y, Jiang Y, Zhang Y, Du J, Cai M, Fu J, Liu H. Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes. Schizophr Bull 2024; 50:1243-1254. [PMID: 38973257 PMCID: PMC11349026 DOI: 10.1093/schbul/sbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS The gut-brain axis plays important roles in both gastrointestinal diseases (GI diseases) and schizophrenia (SCZ). Moreover, both GI diseases and SCZ exhibit notable abnormalities in brain subcortical volumes. However, the genetic mechanisms underlying the comorbidity of these diseases and the shared alterations in brain subcortical volumes remain unclear. STUDY DESIGN Using the genome-wide association studies data of SCZ, 14 brain subcortical volumes, and 8 GI diseases, the global polygenic overlap and local genetic correlations were identified, as well as the shared genetic variants among those phenotypes. Furthermore, we conducted multi-trait colocalization analyses to bolster our findings. Functional annotations, cell-type enrichment, and protein-protein interaction (PPI) analyses were carried out to reveal the critical etiology and pathology mechanisms. STUDY RESULTS The global polygenic overlap and local genetic correlations informed the close relationships between SCZ and both GI diseases and brain subcortical volumes. Moreover, 84 unique lead-shared variants were identified. The associated genes were linked to vital biological processes within the immune system. Additionally, significant correlations were observed with key immune cells and the PPI analysis identified several histone-associated hub genes. These findings highlighted the pivotal roles played by the immune system for both SCZ and GI diseases, along with the shared alterations in brain subcortical volumes. CONCLUSIONS These findings revealed the shared genetic architecture contributing to SCZ and GI diseases, as well as their shared alterations in brain subcortical volumes. These insights have substantial implications for the concurrent development of intervention and therapy targets for these diseases.
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Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yao Zhao
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujing Zhou
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yurong Jiang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiaojiao Du
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jilian Fu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
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Surana P, Dutta P, Davuluri RV. TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups. Bioinformatics 2024; 40:btae475. [PMID: 39120880 PMCID: PMC11319638 DOI: 10.1093/bioinformatics/btae475] [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: 03/21/2024] [Revised: 06/12/2024] [Accepted: 08/08/2024] [Indexed: 08/10/2024] Open
Abstract
MOTIVATION Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance. RESULTS We developed TransTEx (Transcript-level Tissue Expression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P-values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199 166 human transcripts into different groups as 17 999 tissue-specific (TSp), 7436 tissue-enhanced, 36 783 widely expressed (Wide), 79 191 lowly expressed (Low), and 57 757 no expression (Null) transcripts. Testis has the most (13 466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers. AVAILABILITY AND IMPLEMENTATION TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx.
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Affiliation(s)
- Pallavi Surana
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Pratik Dutta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ramana V Davuluri
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
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5
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Enduru N, Fernandes BS, Bahrami S, Dai Y, Andreassen OA, Zhao Z. Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence. Mol Psychiatry 2024; 29:2447-2458. [PMID: 38499654 DOI: 10.1038/s41380-024-02510-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/20/2024]
Abstract
The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.
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Affiliation(s)
- Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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6
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Dai Y, Itai T, Pei G, Yan F, Chu Y, Jiang X, Weinberg SM, Mukhopadhyay N, Marazita ML, Simon LM, Jia P, Zhao Z. DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development. HGG ADVANCES 2024; 5:100312. [PMID: 38796699 PMCID: PMC11193024 DOI: 10.1016/j.xhgg.2024.100312] [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: 02/08/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 05/28/2024] Open
Abstract
Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Toshiyuki Itai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Guangsheng Pei
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fangfang Yan
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yan Chu
- Center for Secure Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Nandita Mukhopadhyay
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
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7
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Itai T, Yan F, Liu A, Dai Y, Iwaya C, Curtis SW, Leslie EJ, Simon LM, Jia P, Chen X, Iwata J, Zhao Z. Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts. HGG ADVANCES 2024; 5:100313. [PMID: 38807368 PMCID: PMC11318074 DOI: 10.1016/j.xhgg.2024.100313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 05/30/2024] Open
Abstract
Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs' deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p = 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p = 0.032, OMIM genes: adjusted p < 0.0002), while only DNV genes were enriched in the endothelium (adjusted p = 0.032). Although we did not achieve significant findings using CPO gene sets, which was mainly due to the limited number of DNV genes, scRNA-seq analysis implicated various expression patterns among DNV and OMIM genes. Our results suggest that combinatory pathway and scRNA-seq data analyses are helpful for contextualizing genes in OFC development.
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Affiliation(s)
- Toshiyuki Itai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fangfang Yan
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Chihiro Iwaya
- Department of Diagnostic & Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; Center for Craniofacial Research, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA
| | - Sarah W Curtis
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Elizabeth J Leslie
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiangning Chen
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Junichi Iwata
- Department of Diagnostic & Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; Center for Craniofacial Research, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; Pediatric Research Center, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
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Zheng S, Tsao PS, Pan C. Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation. Nat Commun 2024; 15:5652. [PMID: 38969659 PMCID: PMC11226445 DOI: 10.1038/s41467-024-49921-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 06/25/2024] [Indexed: 07/07/2024] Open
Abstract
Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.
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Affiliation(s)
- Shufen Zheng
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Guangzhou, China
- Center for Evolutionary Biology, Intelligent Medicine Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Philip S Tsao
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
- Stanford Cardiovascular Institute, Stanford University, California, USA.
- VA Palo Alto Health Care System, Palo Alto, California, USA.
| | - Cuiping Pan
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Guangzhou, China.
- Center for Evolutionary Biology, Intelligent Medicine Institute, School of Life Sciences, Fudan University, Shanghai, China.
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Giraudi PJ, Laraño AA, Monego SD, Pravisani R, Bonazza D, Gondolesi G, Tiribelli C, Baralle F, Baccarani U, Licastro D. Genome-wide DNA methylation and transcriptomic analysis of liver tissues subjected to early ischemia/reperfusion injury upon human liver transplantation. Ann Hepatol 2024; 29:101506. [PMID: 38710471 DOI: 10.1016/j.aohep.2024.101506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/11/2024] [Accepted: 03/27/2024] [Indexed: 05/08/2024]
Abstract
INTRODUCTION AND OBJECTIVES Epigenetic changes represent a mechanism connecting external stresses with long-term modifications of gene expression programs. In solid organ transplantation, ischemia-reperfusion injury (IRI) appears to induce epigenomic changes in the graft, although the currently available data are extremely limited. The present study aimed to characterize variations in DNA methylation and their effects on the transcriptome in liver transplantation from brain-dead donors. PATIENTS AND METHODS 12 liver grafts were evaluated through serial biopsies at different timings in the procurement-transplantation process: T0 (warm procurement, in donor), T1 (bench surgery), and T2 (after reperfusion, in recipient). DNA methylation (DNAm) and transcriptome profiles of biopsies were analyzed using microarrays and RNAseq. RESULTS Significant variations in DNAm were identified, particularly between T2 and T0. Functional enrichment of the best 1000 ranked differentially methylated promoters demonstrated that 387 hypermethylated and 613 hypomethylated promoters were involved in spliceosomal assembly and response to biotic stimuli, and inflammatory immune responses, respectively. At the transcriptome level, T2 vs. T0 showed an upregulation of 337 and downregulation of 61 genes, collectively involved in TNF-α, NFKB, and interleukin signaling. Cell enrichment analysis individuates macrophages, monocytes, and neutrophils as the most significant tissue-cell type in the response. CONCLUSIONS In the process of liver graft procurement-transplantation, IRI induces significant epigenetic changes that primarily act on the signaling pathways of inflammatory responses dependent on TNF-α, NFKB, and interleukins. Our DNAm datasets are the early IRI methylome literature and will serve as a launch point for studying the impact of epigenetic modification in IRI.
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Affiliation(s)
- Pablo J Giraudi
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato, Trieste, Italy.
| | - Allen A Laraño
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato, Trieste, Italy; Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines
| | | | - Riccardo Pravisani
- Liver-Kidney Transplant Unit, Department of Medicine, University of Udine, Italy
| | - Deborah Bonazza
- Anatomia ed Istologia Patologica, Cattinara Hospital, ASUGI, Trieste, Italy
| | - Gabriel Gondolesi
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería, Universidad Favaloro, Buenos Aires, Argentina
| | - Claudio Tiribelli
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato, Trieste, Italy
| | - Francisco Baralle
- Metabolic Liver Disease Unit, Fondazione Italiana Fegato, Trieste, Italy
| | - Umberto Baccarani
- Liver-Kidney Transplant Unit, Department of Medicine, University of Udine, Italy
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10
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Hu R, Wang R, Yuan J, Lin Z, Hutchins E, Landin B, Liao Z, Liu G, Scherzer CR, Dong X. Transcriptional pathobiology and multi-omics predictors for Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599639. [PMID: 38948706 PMCID: PMC11212969 DOI: 10.1101/2024.06.18.599639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction.
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Affiliation(s)
- Ruifeng Hu
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Ruoxuan Wang
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Jie Yuan
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Zechuan Lin
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Elizabeth Hutchins
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | | | - Zhixiang Liao
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ganqiang Liu
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Clemens R. Scherzer
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Xianjun Dong
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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11
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He S, Gubin MM, Rafei H, Basar R, Dede M, Jiang X, Liang Q, Tan Y, Kim K, Gillison ML, Rezvani K, Peng W, Haymaker C, Hernandez S, Solis LM, Mohanty V, Chen K. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles. iScience 2024; 27:110096. [PMID: 38957791 PMCID: PMC11217617 DOI: 10.1016/j.isci.2024.110096] [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: 12/14/2023] [Revised: 04/03/2024] [Accepted: 05/21/2024] [Indexed: 07/04/2024] Open
Abstract
Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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Affiliation(s)
- Shan He
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Matthew M. Gubin
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hind Rafei
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rafet Basar
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xianli Jiang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Qingnan Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kunhee Kim
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maura L. Gillison
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Weiyi Peng
- Department of Biology and Biochemistry, The University of Houston, Houston, TX, USA
| | - Cara Haymaker
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharia Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luisa M. Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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12
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Qi H, Xie YY, Yang XJ, Xia J, Liu K, Zhang FX, Peng WJ, Wen FY, Li BX, Zhang BW, Yao XY, Li BY, Meng HD, Shi ZM, Wang Y, Zhang L. Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure. BMC Genomics 2024; 25:612. [PMID: 38890564 PMCID: PMC11184770 DOI: 10.1186/s12864-024-10409-9] [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: 03/08/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. METHODS AND RESULTS A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. CONCLUSIONS Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.
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Affiliation(s)
- Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, 100088, China
| | - Yun-Yi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xiao-Jun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Feng-Xu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Fu-Yuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bing-Xiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Wen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xin-Yue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Ya Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Hong-Dao Meng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zu-Min Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Yang Wang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China.
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13
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Cai W, Song W, Yu S, Zhao M, Lin GN. Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution. Gen Psychiatr 2024; 37:e101425. [PMID: 38770356 PMCID: PMC11103204 DOI: 10.1136/gpsych-2023-101425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/14/2024] [Indexed: 05/22/2024] Open
Abstract
Background The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.
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Affiliation(s)
- Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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14
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He S, Gubin MM, Rafei H, Basar R, Dede M, Jiang X, Liang Q, Tan Y, Kim K, Gillison ML, Rezvani K, Peng W, Haymaker C, Hernandez S, Solis LM, Mohanty V, Chen K. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593433. [PMID: 38798470 PMCID: PMC11118452 DOI: 10.1101/2024.05.10.593433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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15
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Wang FQ, Shao L, Dang X, Wang YF, Chen S, Liu Z, Mao Y, Jiang Y, Hou F, Guo X, Li J, Zhang L, Sang Y, Zhao X, Ma R, Zhang K, Zhang Y, Yang J, Wen X, Liu J, Wei W, Zhang C, Li W, Qin X, Lei Y, Feng H, Yang X, She CH, Zhang C, Su H, Chen X, Yang J, Lau YL, Wu Q, Ban B, Song Q, Yang W. Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states. Arthritis Res Ther 2024; 26:99. [PMID: 38741185 DOI: 10.1186/s13075-024-03327-4] [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: 02/17/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
OBJECTIVES This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission. METHODS We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks. RESULTS Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse. CONCLUSION Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.
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Affiliation(s)
- Frank Qingyun Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Li Shao
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Xiao Dang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Yong-Fei Wang
- School of Life and Health Sciences, School of Medicine, and Warshel Institute for Computational Biology, The Chinese University of Hong Kong - Shenzhen, Shenzhen, Guangdong, China
| | - Shuxiong Chen
- Medical Research Center, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
- Department of Endocrinology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Zhongyi Liu
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yujing Mao
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Yuping Jiang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Fei Hou
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Xianghua Guo
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Jian Li
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Lili Zhang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Yuting Sang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Xuan Zhao
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Ruirui Ma
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Kai Zhang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Yanfang Zhang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Jing Yang
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Xiwu Wen
- Medical Research Center, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Jiong Liu
- Medical Research Center, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Wei Wei
- Medical Laboratory of Jining Medical University, Jining Medical University, Jining, Shandong, China
| | - Chuanpeng Zhang
- Medical Laboratory of Jining Medical University, Jining Medical University, Jining, Shandong, China
| | - Weiyang Li
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Xiao Qin
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Yao Lei
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Feng
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Xingtian Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Chun Hing She
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Caicai Zhang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Huidong Su
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Xinxin Chen
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Jing Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Qingjun Wu
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Bo Ban
- Department of Endocrinology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Qin Song
- Department of Rheumatology and Lupus Research Institute, Affiliated Hospital of Jining Medical University, Jining, Shandong, China.
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.
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16
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Sanders KL, Manuel AM, Liu A, Leng B, Chen X, Zhao Z. Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach. EPIGENOMES 2024; 8:14. [PMID: 38651367 PMCID: PMC11036294 DOI: 10.3390/epigenomes8020014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.
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Affiliation(s)
- Keith L. Sanders
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Astrid M. Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
| | - Boyan Leng
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Xiangning Chen
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
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Zhao X, Hu C, Chen X, Ren S, Gao F. Drug Repositioning of Inflammatory Bowel Disease Based on Co-Target Gene Expression Signature of Glucocorticoid Receptor and TET2. BIOLOGY 2024; 13:82. [PMID: 38392301 PMCID: PMC10886832 DOI: 10.3390/biology13020082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024]
Abstract
The glucocorticoid receptor (GR) and ten-eleven translocation 2 (TET2), respectively, play a crucial role in regulating immunity and inflammation, and GR interacts with TET2. However, their synergetic roles in inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), remain unclear. This study aimed to investigate the co-target gene signatures of GR and TET2 in IBD and provide potential therapeutic interventions for IBD. By integrating public data, we identified 179 GR- and TET2-targeted differentially expressed genes (DEGs) in CD and 401 in UC. These genes were found to be closely associated with immunometabolism, inflammatory responses, and cell stress pathways. In vitro inflammatory cellular models were constructed using LPS-treated HT29 and HCT116 cells, respectively. Drug repositioning based on the co-target gene signatures of GR and TET2 derived from transcriptomic data of UC, CD, and the in vitro model was performed using the Connectivity Map (CMap). BMS-536924 emerged as a top therapeutic candidate, and its validation experiment within the in vitro inflammatory model confirmed its efficacy in mitigating the LPS-induced inflammatory response. This study sheds light on the pathogenesis of IBD from a new perspective and may accelerate the development of novel therapeutic agents for inflammatory diseases including IBD.
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Affiliation(s)
- Xianglin Zhao
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
- School of Life Sciences, Henan University, Kaifeng 475004, China
- Shenzhen Research Institute of Henan University, Henan University, Shenzhen 518000, China
| | - Chenghao Hu
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Xinyu Chen
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Shuqiang Ren
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Fei Gao
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
- HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou 310022, China
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
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Ferreira LGA, Kizys MML, Gama GAC, Pachernegg S, Robevska G, Sinclair AH, Ayers KL, Dias-da-Silva MR. COUP-TFII regulates early bipotential gonad signaling and commitment to ovarian progenitors. Cell Biosci 2024; 14:3. [PMID: 38178246 PMCID: PMC10768475 DOI: 10.1186/s13578-023-01182-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND The absence of expression of the Y-chromosome linked testis-determining gene SRY in early supporting gonadal cells (ESGC) leads bipotential gonads into ovarian development. However, genetic variants in NR2F2, encoding three isoforms of the transcription factor COUP-TFII, represent a novel cause of SRY-negative 46,XX testicular/ovotesticular differences of sex development (T/OT-DSD). Thus, we hypothesized that COUP-TFII is part of the ovarian developmental network. COUP-TFII is known to be expressed in interstitial/mesenchymal cells giving rise to steroidogenic cells in fetal gonads, however its expression and function in ESGCs have yet to be explored. RESULTS By differentiating induced pluripotent stem cells into bipotential gonad-like cells in vitro and by analyzing single cell RNA-sequencing datasets of human fetal gonads, we identified that NR2F2 expression is highly upregulated during bipotential gonad development along with markers of bipotential state. NR2F2 expression was detected in early cell populations that precede the steroidogenic cell emergence and that retain a multipotent state in the undifferentiated gonad. The ESGCs differentiating into fetal Sertoli cells lost NR2F2 expression, whereas pre-granulosa cells remained NR2F2-positive. When examining the NR2F2 transcript variants individually, we demonstrated that the canonical isoform A, disrupted by frameshift variants previously reported in 46,XX T/OT-DSD patients, is nearly 1000-fold more highly expressed than other isoforms in bipotential gonad-like cells. To investigate the genetic network under COUP-TFII regulation in human gonadal cell context, we generated a NR2F2 knockout (KO) in the human granulosa-like cell line COV434 and studied NR2F2-KO COV434 cell transcriptome. NR2F2 ablation downregulated markers of ESGC and pre-granulosa cells. NR2F2-KO COV434 cells lost the enrichment for female-supporting gonadal progenitor and acquired gene signatures more similar to gonadal interstitial cells. CONCLUSIONS Our findings suggest that COUP-TFII has a role in maintaining a multipotent state necessary for commitment to the ovarian development. We propose that COUP-TFII regulates cell fate during gonad development and impairment of its function may disrupt the transcriptional plasticity of ESGCs. During early gonad development, disruption of ESGC plasticity may drive them into commitment to the testicular pathway, as observed in 46,XX OT-DSD patients with NR2F2 haploinsufficiency.
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Affiliation(s)
- Lucas G A Ferreira
- Laboratory of Molecular and Translational Endocrinology (LEMT), Endocrinology Division, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- Murdoch Children's Research Institute, Melbourne, Australia
| | - Marina M L Kizys
- Laboratory of Molecular and Translational Endocrinology (LEMT), Endocrinology Division, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gabriel A C Gama
- Laboratory of Molecular and Translational Endocrinology (LEMT), Endocrinology Division, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Svenja Pachernegg
- Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | | | - Andrew H Sinclair
- Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Katie L Ayers
- Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Magnus R Dias-da-Silva
- Laboratory of Molecular and Translational Endocrinology (LEMT), Endocrinology Division, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil.
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19
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Iwasaki T, Watanabe R, Ito H, Fujii T, Ohmura K, Yoshitomi H, Murata K, Murakami K, Onishi A, Tanaka M, Matsuda S, Matsuda F, Morinobu A, Hashimoto M. Monocyte-derived transcriptomes explain the ineffectiveness of abatacept in rheumatoid arthritis. Arthritis Res Ther 2024; 26:1. [PMID: 38167328 PMCID: PMC10759752 DOI: 10.1186/s13075-023-03236-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: 09/27/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The biological mechanisms underlying the differential response to abatacept in patients with rheumatoid arthritis (RA) are unknown. Here, we aimed to identify cellular, transcriptomic, and proteomic features that predict resistance to abatacept in patients with RA. METHODS Blood samples were collected from 22 RA patients treated with abatacept at baseline and after 3 months of treatment. Response to treatment was defined by the European League Against Rheumatism (EULAR) response criteria at 3 months, and seven patients were classified as responders and the others as non-responders. We quantified gene expression levels by RNA sequencing, 67 plasma protein levels, and the expression of surface molecules (CD3, 19, and 56) by flow cytometry. In addition, three gene expression data sets, comprising a total of 27 responders and 50 non-responders, were used to replicate the results. RESULTS Among the clinical characteristics, the number of monocytes was significantly higher in the non-responders before treatment. Cell type enrichment analysis showed that differentially expressed genes (DEGs) between responders and non-responders were enriched in monocytes. Gene set enrichment analysis, together with single-cell analysis and deconvolution analysis, identified that Toll-like receptor 5 (TLR5) and interleukin-17 receptor A (IL17RA) pathway in monocytes was upregulated in non-responders. Hepatocyte growth factor (HGF) correlated with this signature showed higher concentrations in non-responders before treatment. The DEGs in the replication set were also enriched for the genes expressed in monocytes, not for the TLR5 and IL17RA pathway but for the oxidative phosphorylation (OXPHOS) pathway. CONCLUSIONS Monocyte-derived transcriptomic features before treatment underlie the differences in abatacept efficacy in patients with RA. The pathway activated in monocytes was the TLR5 and IL17RA-HGF signature in the current study, while it was the OXPHOS pathway in the replication set. Elevated levels of HGF before treatment may serve as a potential biomarker for predicting poor responses to abatacept. These findings provide insights into the biological mechanisms of abatacept resistance, contributing valuable evidence for stratifying patients with RA.
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Grants
- Nagahama City, Shiga, Japan, Toyooka City, Hyogo, Japan, and five pharmaceutical companies (Mitsubishi Tanabe Pharma Co., Chugai Pharmaceutical Co. Ltd, UCB Japan Co. Ltd, AYUMI Pharmaceutical Co., and Asahi Kasei Pharma Corp.).
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Affiliation(s)
- Takeshi Iwasaki
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryu Watanabe
- Department of Clinical Immunology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.
| | - Hiromu Ito
- Department of Orthopaedic Surgery, Kurashiki Central Hospital, Okayama, Japan
| | - Takayuki Fujii
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Hiroyuki Yoshitomi
- Department of Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichi Murata
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kosaku Murakami
- Division of Clinical Immunology and Cancer Immunotherapy, Center for Cancer Immunotherapy and Immunobiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Onishi
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masao Tanaka
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuichi Matsuda
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akio Morinobu
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Motomu Hashimoto
- Department of Clinical Immunology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.
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20
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Dai Y, Hsu YC, Fernandes BS, Zhang K, Li X, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach. J Alzheimers Dis 2024; 97:1807-1827. [PMID: 38306043 DOI: 10.3233/jad-231020] [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] [Indexed: 02/03/2024]
Abstract
Background The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yu-Chun Hsu
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kai Zhang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoyang Li
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Astrid M Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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21
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Joo S, Dhaygude K, Westerberg S, Krebs R, Puhka M, Holmström E, Syrjälä S, Nykänen AI, Lemström K. Transcriptomic Landscape of Circulating Extracellular Vesicles in Heart Transplant Ischemia-Reperfusion. Genes (Basel) 2023; 14:2101. [PMID: 38003044 PMCID: PMC10671425 DOI: 10.3390/genes14112101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Ischemia-reperfusion injury (IRI) is an inevitable event during heart transplantation, which is known to exacerbate damage to the allograft. However, the precise mechanisms underlying IRI remain incompletely understood. Here, we profiled the whole transcriptome of plasma extracellular vesicles (EVs) by RNA sequencing from 41 heart transplant recipients immediately before and at 12 h after transplant reperfusion. We found that the expression of 1317 protein-coding genes in plasma EVs was changed at 12 h after reperfusion. Upregulated genes of plasma EVs were related to metabolism and immune activation, while downregulated genes were related to cell survival and extracellular matrix organization. In addition, we performed correlation analyses between EV transcriptome and intensity of graft IRI (i.e., cardiomyocyte injury), as well as EV transcriptome and primary graft dysfunction, as well as any biopsy-proven acute rejection after heart transplantation. We ultimately revealed that at 12 h after reperfusion, 4 plasma EV genes (ITPKA, DDIT4L, CD19, and CYP4A11) correlated with both cardiomyocyte injury and primary graft dysfunction, suggesting that EVs are sensitive indicators of reperfusion injury reflecting lipid metabolism-induced stress and imbalance in calcium homeostasis. In conclusion, we show that profiling plasma EV gene expression may enlighten the mechanisms of heart transplant IRI.
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Affiliation(s)
- SeoJeong Joo
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
| | - Kishor Dhaygude
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
| | - Sofie Westerberg
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
| | - Rainer Krebs
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
| | - Maija Puhka
- Institute for Molecular Medicine Finland FIMM, EV and HiPREP Core, University of Helsinki, 00014 Helsinki, Finland;
| | - Emil Holmström
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
| | - Simo Syrjälä
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
- Heart and Lung Center, Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland
| | - Antti I. Nykänen
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
- Heart and Lung Center, Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland
| | - Karl Lemström
- Translational Immunology Research Program, Transplantation Laboratory, University of Helsinki, 00014 Helsinki, Finland; (S.J.); (K.D.); (S.W.); (R.K.); (E.H.); (S.S.); (A.I.N.)
- Heart and Lung Center, Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland
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22
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Fujiwara M, Ferdousi F, Isoda H. Investigation into Molecular Brain Aging in Senescence-Accelerated Mouse (SAM) Model Employing Whole Transcriptomic Analysis in Search of Potential Molecular Targets for Therapeutic Interventions. Int J Mol Sci 2023; 24:13867. [PMID: 37762170 PMCID: PMC10530366 DOI: 10.3390/ijms241813867] [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: 08/09/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
With the progression of an aging society, cognitive aging has emerged as a pressing concern necessitating attention. The senescence-accelerated mouse-prone 8 (SAMP8) model has proven instrumental in investigating the early stages of cognitive aging. Through an extensive examination of molecular changes in the brain cortex, utilizing integrated whole-genome transcriptomics, our principal aim was to uncover potential molecular targets with therapeutic applications and relevance to drug screening. Our investigation encompassed four distinct conditions, comparing the same strain at different time points (1 year vs. 16 weeks) and the same time point across different strains (SAMP8 vs. SAMR1), namely: physiological aging, accelerated aging, early events in accelerated aging, and late events in accelerated aging. Focusing on key functional alterations associated with aging in the brain, including neurogenesis, synapse dynamics, neurometabolism, and neuroinflammation, we identified candidate genes linked to these processes. Furthermore, employing protein-protein interaction (PPI) analysis, we identified pivotal hub genes involved in interactions within these functional domains. Additionally, gene-set perturbation analysis allowed us to uncover potential upstream genes or transcription factors that exhibited activation or inhibition across the four conditions. In summary, our comprehensive analysis of the SAMP8 mouse brain through whole-genome transcriptomics not only deepens our understanding of age-related changes but also lays the groundwork for a predictive model to facilitate drug screening for cognitive aging.
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Affiliation(s)
- Michitaka Fujiwara
- Graduate School of Environmental Science Program, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan
- Open Innovation Laboratory for Food and Medicinal Resource Engineering, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Tennodai, Tsukuba 305-8572, Japan
| | - Farhana Ferdousi
- Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
| | - Hiroko Isoda
- Open Innovation Laboratory for Food and Medicinal Resource Engineering, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Tennodai, Tsukuba 305-8572, Japan
- Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
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23
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Dai Y, Yu-Chun H, Fernandes BS, Zhang K, Xiaoyang L, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach. RESEARCH SQUARE 2023:rs.3.rs-3328861. [PMID: 37720047 PMCID: PMC10503860 DOI: 10.21203/rs.3.rs-3328861/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Background The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points. Methods We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS. Conclusion Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.
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Affiliation(s)
- Yulin Dai
- The University of Texas Health Science Center at Houston
| | - Hsu Yu-Chun
- The University of Texas Health Science Center at Houston
| | | | - Kai Zhang
- The University of Texas Health Science Center at Houston
| | - Li Xiaoyang
- The University of Texas Health Science Center at Houston
| | - Nitesh Enduru
- The University of Texas Health Science Center at Houston
| | - Andi Liu
- The University of Texas Health Science Center at Houston
| | | | - Xiaoqian Jiang
- The University of Texas Health Science Center at Houston
| | - Zhongming Zhao
- The University of Texas Health Science Center at Houston
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Luckett ES, Zielonka M, Kordjani A, Schaeverbeke J, Adamczuk K, De Meyer S, Van Laere K, Dupont P, Cleynen I, Vandenberghe R. Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults. Alzheimers Res Ther 2023; 15:121. [PMID: 37438770 PMCID: PMC10337180 DOI: 10.1186/s13195-023-01242-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/06/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND Gene expression is dysregulated in Alzheimer's disease (AD) patients, both in peripheral blood and post mortem brain. We investigated peripheral whole-blood gene (co)expression to determine molecular changes prior to symptom onset. METHODS RNA was extracted and sequenced for 65 cognitively healthy F-PACK participants (65 (56-80) years, 34 APOE4 non-carriers, 31 APOE4 carriers), at baseline and follow-up (interval: 5.0 (3.4-8.6) years). Participants received amyloid PET at both time points and amyloid rate of change derived. Accumulators were defined with rate of change ≥ 2.19 Centiloids. We performed differential gene expression and weighted gene co-expression network analysis to identify differentially expressed genes and networks of co-expressed genes, respectively, with respect to traits of interest (APOE4 status, amyloid accumulation (binary/continuous)), and amyloid positivity status, followed by Gene Ontology annotation. RESULTS There were 166 significant differentially expressed genes at follow-up compared to baseline in APOE4 carriers only, whereas 12 significant differentially expressed genes were found only in APOE4 non-carriers, over time. Among the significant genes in APOE4 carriers, several had strong evidence for a pathogenic role in AD based on direct association scores generated from the DISQOVER platform: NGRN, IGF2, GMPR, CLDN5, SMIM24. Top enrichment terms showed upregulated mitochondrial and metabolic pathways, and an exacerbated upregulation of ribosomal pathways in APOE4 carriers compared to non-carriers. Similarly, there were 33 unique significant differentially expressed genes at follow-up compared to baseline in individuals classified as amyloid negative at baseline and positive at follow-up or amyloid positive at both time points and 32 unique significant differentially expressed genes over time in individuals amyloid negative at both time points. Among the significant genes in the first group, the top five with the highest direct association scores were as follows: RPL17-C18orf32, HSP90AA1, MBP, SIRPB1, and GRINA. Top enrichment terms included upregulated metabolism and focal adhesion pathways. Baseline and follow-up gene co-expression networks were separately built. Seventeen baseline co-expression modules were derived, with one significantly negatively associated with amyloid accumulator status (r2 = - 0.25, p = 0.046). This was enriched for proteasomal protein catabolic process and myeloid cell development. Thirty-two follow-up modules were derived, with two significantly associated with APOE4 status: one downregulated (r2 = - 0.27, p = 0.035) and one upregulated (r2 = 0.26, p = 0.039) module. Top enrichment processes for the downregulated module included proteasomal protein catabolic process and myeloid cell homeostasis. Top enrichment processes for the upregulated module included cytoplasmic translation and rRNA processing. CONCLUSIONS We show that there are longitudinal gene expression changes that implicate a disrupted immune system, protein removal, and metabolism in cognitively intact individuals who carry APOE4 or who accumulate in cortical amyloid. This provides insight into the pathophysiology of AD, whilst providing novel targets for drug and therapeutic development.
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Affiliation(s)
- Emma S Luckett
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Magdalena Zielonka
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, VIB-KU Leuven, KU Leuven, Leuven, 3000, Belgium
| | - Amine Kordjani
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory of Neuropathology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
| | | | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory of Molecular Neurobiomarker Research, KU Leuven, Leuven, 3000, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, 3000, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, 3000, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
| | - Isabelle Cleynen
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium.
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium.
- Neurology Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
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25
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Zucker R, Kovalerchik M, Linial M. Gene-based association study reveals a distinct female genetic signal in primary hypertension. Hum Genet 2023:10.1007/s00439-023-02567-9. [PMID: 37133573 DOI: 10.1007/s00439-023-02567-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023]
Abstract
Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.
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Affiliation(s)
- Roei Zucker
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michael Kovalerchik
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
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26
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Itai T, Jia P, Dai Y, Chen J, Chen X, Zhao Z. De novo mutations disturb early brain development more frequently than common variants in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2023; 192:62-70. [PMID: 36863698 PMCID: PMC11270591 DOI: 10.1002/ajmg.b.32932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/08/2022] [Accepted: 01/29/2023] [Indexed: 03/04/2023]
Abstract
Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.
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Affiliation(s)
- Toshiyuki Itai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, Nevada, USA
| | - Xiangning Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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27
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Yao X, Yang H, Han H, Kou X, Jiang Y, Luo M, Zhou Y, Wang J, Fan X, Wang X, Li MJ, Yan H. Genome-wide analysis of genetic pleiotropy and causal genes across three age-related ocular disorders. Hum Genet 2023; 142:507-522. [PMID: 36917350 DOI: 10.1007/s00439-023-02542-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/04/2023] [Indexed: 03/16/2023]
Abstract
Age-related macular degeneration (AMD), cataract, and glaucoma are leading causes of blindness worldwide. Previous genome-wide association studies (GWASs) have revealed a variety of susceptible loci associated with age-related ocular disorders, yet the genetic pleiotropy and causal genes across these diseases remain poorly understood. By leveraging large-scale genetic and observational data from ocular disease GWASs and UK Biobank (UKBB), we found significant pairwise genetic correlations and consistent epidemiological associations among these ocular disorders. Cross-disease meta-analysis uncovered seven pleiotropic loci, three of which were replicated in an additional cohort. Integration of variants in pleiotropic loci and multiple single-cell omics data identified that Müller cells and astrocytes were likely trait-related cell types underlying ocular comorbidity. In addition, we comprehensively integrated eye-specific gene expression quantitative loci (eQTLs), epigenomic profiling, and 3D genome data to prioritize causal pleiotropic genes. We found that pleiotropic genes were essential in nerve development and eye pigmentation, and targetable by aflibercept and pilocarpine for the treatment of AMD and glaucoma. These findings will not only facilitate the mechanistic research of ocular comorbidities but also benefit the therapeutic optimization of age-related ocular diseases.
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Affiliation(s)
- Xueming Yao
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hongxi Yang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Han Han
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xuejing Kou
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuhan Jiang
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Menghan Luo
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaohong Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China. .,Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
| | - Hua Yan
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China. .,Laboratory of Molecular Ophthalmology, Tianjin Medical University, Tianjin, 300070, China. .,School of Medicine, Nankai University, Tianjin, 300071, China.
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28
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Manuel AM, Dai Y, Jia P, Freeman LA, Zhao Z. A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis. Hum Mol Genet 2023; 32:998-1009. [PMID: 36282535 PMCID: PMC9991005 DOI: 10.1093/hmg/ddac265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 02/02/2023] Open
Abstract
Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have pinpointed differentially methylated CpG sites in MS patients. However, the interplay between genetic risk factors and epigenetic regulation remains elusive. Here, we employed a network model to integrate GWAS summary statistics of 14 802 MS cases and 26 703 controls with DNA methylation profiles from 140 MS cases and 139 controls and the human interactome. We identified differentially methylated genes by aggregating additive effects of differentially methylated CpG sites within promoter regions. We reconstructed a gene regulatory network (GRN) using literature-curated transcription factor knowledge. Colocalization of the MS GWAS and methylation quantitative trait loci (mQTL) was performed to assess the GRN. The resultant MS-associated GRN highlighted several single nucleotide polymorphisms with GWAS-mQTL colocalization: rs6032663, rs6065926 and rs2024568 of CD40 locus, rs9913597 of STAT3 locus, and rs887864 and rs741175 of CIITA locus. Moreover, synergistic mQTL and expression QTL signals were identified in CD40, suggesting gene expression alteration was likely induced by epigenetic changes. Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA) indicated that the GRN was enriched in T follicular helper cells (P-value = 0.0016). Drug target enrichment analysis of annotations from the Therapeutic Target Database revealed the GRN was also enriched with drug target genes (P-value = 3.89 × 10-4), revealing repurposable candidates for MS treatment. These candidates included vorinostat (HDAC1 inhibitor) and sivelestat (ELANE inhibitor), which warrant further investigation.
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Affiliation(s)
- Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Leorah A Freeman
- Department of Neurology, Dell Medical School, The University of Texas, Austin, TX 78712, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
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29
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Sepehrinezhad A, Shahbazi A, Sahab Negah S, Stolze Larsen F. New Insight Into Mechanisms of Hepatic Encephalopathy: An Integrative Analysis Approach to Identify Molecular Markers and Therapeutic Targets. Bioinform Biol Insights 2023; 17:11779322231155068. [PMID: 36814683 PMCID: PMC9940182 DOI: 10.1177/11779322231155068] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/17/2023] [Indexed: 02/19/2023] Open
Abstract
Hepatic encephalopathy (HE) is a set of complex neurological complications that arise from advanced liver disease. The precise molecular and cellular mechanism of HE is not fully understood. Differentially expressed genes (DEGs) from microarray technologies are powerful approaches to obtain new insight into the pathophysiology of HE. We analyzed microarray data sets of cirrhotic patients with HE from Gene Expression Omnibus to identify DEGs in postmortem cerebral tissues. Consequently, we uploaded significant DEGs into the STRING to specify protein-protein interactions. Cytoscape was used to reconstruct the genetic network and identify hub genes. Target genes were uploaded to different databases to perform comprehensive enrichment analysis and repurpose new therapeutic options for HE. A total of 457 DEGs were identified in 2 data sets totally from 12 cirrhotic patients with HE compared with 12 healthy subjects. We found that 274 genes were upregulated and 183 genes were downregulated. Network analyses on significant DEGs indicated 12 hub genes associated with HE. Enrichment analysis identified fatty acid beta-oxidation, cerebral organic acidurias, and regulation of actin cytoskeleton as main involved pathways associated with upregulated genes; serotonin receptor 2 and ELK-SRF/GATA4 signaling, GPCRs, class A rhodopsin-like, and p38 MAPK signaling pathway were related to downregulated genes. Finally, we predicted 39 probable effective drugs/agents for HE. This study not only confirms main important involved mechanisms of HE but also reveals some yet unknown activated molecular and cellular pathways in human HE. In addition, new targets were identified that could be of value in the future study of HE.
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Affiliation(s)
- Ali Sepehrinezhad
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Neuroscience Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Shahbazi
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Sajad Sahab Negah
- Neuroscience Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Neuroscience, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fin Stolze Larsen
- Department of Hepatology CA-3163, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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30
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Habets PC, Kalafatakis K, Dzyubachyk O, van der Werff SJ, Keo A, Thakrar J, Mahfouz A, Pereira AM, Russell GM, Lightman SL, Meijer OC. Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation. Neurobiol Stress 2023; 22:100514. [PMID: 36660181 PMCID: PMC9842700 DOI: 10.1016/j.ynstr.2023.100514] [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: 09/14/2022] [Revised: 01/02/2023] [Accepted: 01/02/2023] [Indexed: 01/05/2023] Open
Abstract
The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pulsatility is crucial for normal emotional response in man. In this study, we investigate the anatomical transcriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show most significant differential expression (q = 3.99e-10) and enrichment (fold enrichment = 5.56, q = 9.09e-4). Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies.
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Affiliation(s)
- Philippe C. Habets
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands
- Amsterdam University Medical Centre, Department of Psychiatry, Department of Anatomy and Neurosciences, 1081 HZ, Amsterdam, the Netherlands
| | - Konstantinos Kalafatakis
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
- Institute of Health Science Education, Barts and the London School of Medicine & Dentistry, Queen Mary University of London Malta Campus, VCT 2520, Victoria Gozo, Malta
| | - Oleh Dzyubachyk
- Department of Radiology, Division of Medical Image Processing, Leiden University Medical Center, 2333 ZA, Leiden, the Netherlands
- Leiden University Medical Center, Department of Cell and Chemical Biology, Section Electron Microscopy, 2300 RC, Leiden, the Netherlands
| | - Steven J.A. van der Werff
- Department of Psychiatry, Leiden University Medical Center LUMC, Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | - Arlin Keo
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands
| | - Jamini Thakrar
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Alberto M. Pereira
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands
- Department of Endocrinology & Metabolism, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Georgina M. Russell
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Stafford L. Lightman
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Onno C. Meijer
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands
- Leiden Institute for Brain and Cognition, Leiden, the Netherlands
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31
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Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments. Genes (Basel) 2022; 13:genes13071210. [PMID: 35885993 PMCID: PMC9316565 DOI: 10.3390/genes13071210] [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: 06/15/2022] [Revised: 07/01/2022] [Accepted: 07/03/2022] [Indexed: 02/04/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein-protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.
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