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Li W, Ballard J, Zhao Y, Long Q. Knowledge-guided learning methods for integrative analysis of multi-omics data. Comput Struct Biotechnol J 2024; 23:1945-1950. [PMID: 38736693 PMCID: PMC11087912 DOI: 10.1016/j.csbj.2024.04.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.
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Affiliation(s)
- Wenrui Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
| | - Jenna Ballard
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, 19104, PA, USA
| | - Yize Zhao
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, 06510, CT, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
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Singh A, Verma A, Dutta G, Gowane GR, Ludri A, Alex R. Functional transcriptome analysis revealed major changes in pathways affecting systems biology of Tharparkar cattle under seasonal heat stress. 3 Biotech 2024; 14:177. [PMID: 38855148 PMCID: PMC11156831 DOI: 10.1007/s13205-024-04018-2] [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: 12/11/2023] [Accepted: 05/26/2024] [Indexed: 06/11/2024] Open
Abstract
Heat stress significantly disturbs the production, reproduction, and systems biology of dairy cattle. A complex interaction among biological systems helps to combat and overcome heat stress. Indicine cattle breed Tharparkar has been well known for its thermal adaptability. Therefore, present investigation considered RNA-seq technology to explore the functional transcriptomics of Tharparkar cattle with the help of samples collected in spring and summer season. Among differentially expressed genes, about 3280 genes were highly dysregulated, in which 1207 gene were upregulated and 2073 genes were downregulated (|log2fold change|≥ 1 and p ≤ 0.05). Upregulated genes were related to insulin activation, interferons, and potassium ion transport. In contrast, downregulated genes were related to RNA processing, translation, and ubiquitination. Functional annotation revealed that the pathways associated with nervous system (NPFFR1, ROBO3) and metal ion transport (KCNG2, ATP1A2) were highly activated while mRNA processing and translation (EIF4A, EIF4B) and protein processing pathway (VPS4B, PEX13) were highly downregulated. Protein-protein interactions identified hub genes such as ATP13A3, IFNGR2, UBXN7, EIF4A2, SLC12A8 found to play an important role in immune, ubiquitination, translation and transport function. Co-expression network includes LYZ, PNRC1, SQSTM1, EIF4AB and DDX17 genes which are involved in lysosomal activity, tumor inhibition, ubiquitination, and translation initiation. Chemokine signaling pathway associated with immune response was highly upregulated in cluster analysis. The findings of this study provide insights into transcriptome expression and regulation which may better explain complex thermal resilience mechanism of Tharparkar cattle in heat stress under natural conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-024-04018-2.
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Affiliation(s)
- Ayushi Singh
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
| | - Archana Verma
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
| | - Gaurav Dutta
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
| | - Gopal R. Gowane
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
| | - Ashutosh Ludri
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
| | - Rani Alex
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001 India
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Teppert K, Yonezawa Ogusuku IE, Brandes C, Herbel V, Winter N, Werchau N, Khorkova S, Wöhle C, Jelveh N, Bisdorf K, Engels B, Schaser T, Anders K, Künkele A, Lock D. CAR'TCR-T cells co-expressing CD33-CAR and dNPM1-TCR as superior dual-targeting approach for AML treatment. MOLECULAR THERAPY. ONCOLOGY 2024; 32:200797. [PMID: 38601972 PMCID: PMC11004219 DOI: 10.1016/j.omton.2024.200797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024]
Abstract
Acute myeloid leukemia (AML), a fast-progressing hematological malignancy affecting myeloid cells, is typically treated with chemotherapy or hematopoietic stem cell transplantation. However, approximately half of the patients face relapses and 5-year survival rates are poor. With the goal to facilitate dual-specificity, boosting anti-tumor activity, and minimizing the risk for antigen escape, this study focused on combining chimeric antigen receptor (CAR) and T cell receptor (TCR) technologies. CAR'TCR-T cells, co-expressing a CD33-CAR and a transgenic dNPM1-TCR, revealed increased and prolonged anti-tumor activity in vitro, particularly in case of low target antigen expression. The distinct transcriptomic profile suggested enhanced formation of immunological synapses, activation, and signaling. Complete elimination of AML xenografts in vivo was only achieved with a cell product containing CAR'TCR-T, CAR-T, and TCR-T cells, representing the outcome of co-transduction with two lentiviral vectors encoding either CAR or TCR. A mixture of CAR-T and TCR-T cells, without CAR'TCR-T cells, did not prevent progressive tumor outgrowth and was comparable to treatment with CAR-T and TCR-T cells individually. Overall, our data underscore the efficacy of co-expressing CAR and transgenic TCR in one T cell, and might open a novel therapeutic avenue not only for AML but also other malignancies.
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Affiliation(s)
- Karin Teppert
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | | | | | - Vera Herbel
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Nora Winter
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Niels Werchau
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | | | - Christian Wöhle
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Nojan Jelveh
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Kevin Bisdorf
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Boris Engels
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Thomas Schaser
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
| | - Kathleen Anders
- Department of Pediatric Oncology and Hematology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10178 Berlin, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Annette Künkele
- Department of Pediatric Oncology and Hematology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10178 Berlin, Germany
- German Cancer Consortium (DKTK), 10117 Berlin, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Dominik Lock
- Miltenyi Biotec B.V. & Co. KG, 51429 Bergisch Gladbach, Germany
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4
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Robertson H, Kim HJ, Li J, Robertson N, Robertson P, Jimenez-Vera E, Ameen F, Tran A, Trinh K, O'Connell PJ, Yang JYH, Rogers NM, Patrick E. Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas. Nat Med 2024:10.1038/s41591-024-03030-6. [PMID: 38890530 DOI: 10.1038/s41591-024-03030-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 04/29/2024] [Indexed: 06/20/2024]
Abstract
The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction.
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Affiliation(s)
- Harry Robertson
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
| | - Hani Jieun Kim
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
- Kinghorn Cancer Centre and Cancer Research Theme, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Jennifer Li
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia
| | - Nicholas Robertson
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Paul Robertson
- Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia
| | - Elvira Jimenez-Vera
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Farhan Ameen
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
| | - Andy Tran
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
| | - Katie Trinh
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Philip J O'Connell
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Jean Y H Yang
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Natasha M Rogers
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
- Centre for Cancer Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.
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Cossarini F, Shang J, Krek A, Al-Taie Z, Hou R, Canales-Herrerias P, Tokuyama M, Tankelevich M, Tillowiz A, Jha D, Livanos AE, Leyre L, Uzzan M, Martinez-Delgado G, Tylor M, Sharma K, Bourgonje AR, Cruz M, Ioannou G, Dawson T, D'Souza D, Kim-Schulze S, Akm A, Aberg JA, Chen BK, Gnjatic S, Polydorides AD, Cerutti A, Argmann C, Vujkovic-Cvijin I, Suarez-Farinas M, Petralia F, Faith JJ, Mehandru S. HIV-1 infection is associated with depletion of germinal center B cells and a decrease in IgA + plasma cells in the GI tract. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.590425. [PMID: 38826293 PMCID: PMC11142040 DOI: 10.1101/2024.05.17.590425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Gastrointestinal (GI) B cells and plasma cells (PCs), critical to mucosal homeostasis, play an important role in the host response to HIV-1 infection. Here, high resolution mapping of human B cells and PCs from colon and ileum during both viremic and suppressed HIV-1 infection identified a significant reduction in germinal center (GC) B cells and Follicular Dendritic Cells (FDCs) during HIV-1 viremia. Further, IgA + PCs, the major cellular output of intestinal GCs were significantly reduced during viremic HIV-1 infection. PC-associated transcriptional perturbations, including type I interferon signaling persisted in antiretroviral therapy (ART) treated individuals, suggesting ongoing disruption of the intestinal immune milieu during ART. GI humoral immune perturbations associated with changes in intestinal microbiome composition and systemic inflammation. Herein, we highlight a key immune defect in the GI mucosa due to HIV-1 viremia, with major implications. One Sentence Summary Major perturbations in intestinal GC dynamics in viremic HIV-1 infection relate to reduced IgA + plasma cells, systemic inflammation and microbiota changes.
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Shlapakova PS, Dobrynina LA, Kalashnikova LA, Gubanova MV, Danilova MS, Gnedovskaya EV, Grigorenko AP, Gusev FE, Manakhov AD, Rogaev EI. Peripheral Blood Gene Expression Profiling Reveals Molecular Pathways Associated with Cervical Artery Dissection. Int J Mol Sci 2024; 25:5205. [PMID: 38791244 PMCID: PMC11121660 DOI: 10.3390/ijms25105205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/01/2024] [Accepted: 05/05/2024] [Indexed: 05/26/2024] Open
Abstract
Cervical artery dissection (CeAD) is the primary cause of ischemic stroke in young adults. Monogenic heritable connective tissue diseases account for fewer than 5% of cases of CeAD. The remaining sporadic cases have known risk factors. The clinical, radiological, and histological characteristics of systemic vasculopathy and undifferentiated connective tissue dysplasia are present in up to 70% of individuals with sporadic CeAD. Genome-wide association studies identified CeAD-associated genetic variants in the non-coding genomic regions that may impact the gene transcription and RNA processing. However, global gene expression profile analysis has not yet been carried out for CeAD patients. We conducted bulk RNA sequencing and differential gene expression analysis to investigate the expression profile of protein-coding genes in the peripheral blood of 19 CeAD patients and 18 healthy volunteers. This was followed by functional annotation, heatmap clustering, reports on gene-disease associations and protein-protein interactions, as well as gene set enrichment analysis. We found potential correlations between CeAD and the dysregulation of genes linked to nucleolar stress, senescence-associated secretory phenotype, mitochondrial malfunction, and epithelial-mesenchymal plasticity.
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Affiliation(s)
- Polina S. Shlapakova
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Larisa A. Dobrynina
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Ludmila A. Kalashnikova
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Mariia V. Gubanova
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Maria S. Danilova
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Elena V. Gnedovskaya
- Third Neurological Department, Research Center of Neurology, Moscow 125367, Russia; (P.S.S.); (L.A.K.); (M.V.G.); (E.V.G.)
| | - Anastasia P. Grigorenko
- Department of Genomics and Human Genetics, Laboratory of Evolutionary Genomics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia (F.E.G.)
| | - Fedor E. Gusev
- Department of Genomics and Human Genetics, Laboratory of Evolutionary Genomics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia (F.E.G.)
- Department of Genetics, Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi 354340, Russia; (A.D.M.)
| | - Andrey D. Manakhov
- Department of Genetics, Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi 354340, Russia; (A.D.M.)
- Center for Genetics and Genetic Technologies, Faculty of Biology, Lomonosov Moscow State University, Moscow 119192, Russia
| | - Evgeny I. Rogaev
- Department of Genetics, Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi 354340, Russia; (A.D.M.)
- Department of Psychiatry, UMass Chan Medical School, 222 Maple Ave, Reed-Rose-Gordon Building, Shrewsbury, MA 01545, USA
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Liu X, Tao Y, Cai Z, Bao P, Ma H, Li K, Li M, Zhu Y, Lu ZJ. Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data. Bioinformatics 2024; 40:btae316. [PMID: 38741230 PMCID: PMC11139513 DOI: 10.1093/bioinformatics/btae316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/29/2024] [Accepted: 05/11/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. RESULTS To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. AVAILABILITY AND IMPLEMENTATION Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.
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Affiliation(s)
- Xiaofan Liu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Yuhuan Tao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Zilin Cai
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Pengfei Bao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Hongli Ma
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Kexing Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Mengtao Li
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), MST State Key Laboratory of Complex Severe and Rare Diseases, MOE Key Laboratory of Rheumatology and Clinical Immunology, Beijing 100730, China
| | - Yunping Zhu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zhi John Lu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
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8
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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9
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James DW, Quintela M, Lucini L, Alkafri NK, Healey GD, Younas K, Bunkheila A, Margarit L, Francis LW, Gonzalez D, Conlan RS. Homeobox regulator Wilms Tumour 1 is displaced by androgen receptor at cis-regulatory elements in the endometrium of PCOS patients. Front Endocrinol (Lausanne) 2024; 15:1368494. [PMID: 38745948 PMCID: PMC11091321 DOI: 10.3389/fendo.2024.1368494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/08/2024] [Indexed: 05/16/2024] Open
Abstract
Decidualisation, the process whereby endometrial stromal cells undergo morphological and functional transformation in preparation for trophoblast invasion, is often disrupted in women with polycystic ovary syndrome (PCOS) resulting in complications with pregnancy and/or infertility. The transcription factor Wilms tumour suppressor 1 (WT1) is a key regulator of the decidualization process, which is reduced in patients with PCOS, a complex condition characterized by increased expression of androgen receptor in endometrial cells and high presence of circulating androgens. Using genome-wide chromatin immunoprecipitation approaches on primary human endometrial stromal cells, we identify key genes regulated by WT1 during decidualization, including homeobox transcription factors which are important for regulating cell differentiation. Furthermore, we found that AR in PCOS patients binds to the same DNA regions as WT1 in samples from healthy endometrium, suggesting dysregulation of genes important to decidualisation pathways in PCOS endometrium due to competitive binding between WT1 and AR. Integrating RNA-seq and H3K4me3 and H3K27ac ChIP-seq metadata with our WT1/AR data, we identified a number of key genes involved in immune response and angiogenesis pathways that are dysregulated in PCOS patients. This is likely due to epigenetic alterations at distal enhancer regions allowing AR to recruit cofactors such as MAGEA11, and demonstrates the consequences of AR disruption of WT1 in PCOS endometrium.
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Affiliation(s)
- David W. James
- Swansea University Medical School, Swansea, United Kingdom
| | | | - Lisa Lucini
- Swansea University Medical School, Swansea, United Kingdom
| | | | | | - Kinza Younas
- Swansea University Medical School, Swansea, United Kingdom
- Swansea Bay University Health Board, Swansea, United Kingdom
| | - Adnan Bunkheila
- Swansea University Medical School, Swansea, United Kingdom
- Swansea Bay University Health Board, Swansea, United Kingdom
| | - Lavinia Margarit
- Swansea University Medical School, Swansea, United Kingdom
- Cwm Taf Morgannwg University Health Board, Bridgend, United Kingdom
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10
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Xia Y, Sun M, Huang H, Jin WL. Drug repurposing for cancer therapy. Signal Transduct Target Ther 2024; 9:92. [PMID: 38637540 PMCID: PMC11026526 DOI: 10.1038/s41392-024-01808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.
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Affiliation(s)
- Ying Xia
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
- Division of Gastroenterology and Hepatology, Department of Medicine and, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ming Sun
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
| | - Hai Huang
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China.
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China.
| | - Wei-Lin Jin
- Institute of Cancer Neuroscience, Medical Frontier Innovation Research Center, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, PR China.
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11
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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12
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Hart M, Kern F, Fecher-Trost C, Krammes L, Aparicio E, Engel A, Hirsch P, Wagner V, Keller V, Schmartz GP, Rheinheimer S, Diener C, Fischer U, Mayer J, Meyer MR, Flockerzi V, Keller A, Meese E. Experimental capture of miRNA targetomes: disease-specific 3'UTR library-based miRNA targetomics for Parkinson's disease. Exp Mol Med 2024; 56:935-945. [PMID: 38556547 PMCID: PMC11059366 DOI: 10.1038/s12276-024-01202-5] [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: 08/21/2023] [Revised: 01/12/2024] [Accepted: 01/30/2024] [Indexed: 04/02/2024] Open
Abstract
The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA-target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson's disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online ( https://ccb-web.cs.uni-saarland.de/utr-seremato ), and all the data have been added to the miRATBase database ( https://ccb-web.cs.uni-saarland.de/miratbase ).
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Affiliation(s)
- Martin Hart
- Human Genetics, Saarland University, 66421, Homburg, Germany.
| | - Fabian Kern
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)-Helmholtz Centre for Infection Research (HZI), Saarland University Campus, Saarbrücken, Germany
| | - Claudia Fecher-Trost
- Department of Experimental and Clinical Pharmacology & Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, 66421, Homburg, Germany
| | - Lena Krammes
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Ernesto Aparicio
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Annika Engel
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Pascal Hirsch
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Viktoria Wagner
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Verena Keller
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
- Department for Internal Medicine II, Saarland University Hospital, 66421, Homburg, Germany
| | | | | | - Caroline Diener
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Ulrike Fischer
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Jens Mayer
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Markus R Meyer
- Department of Experimental and Clinical Pharmacology & Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, 66421, Homburg, Germany
| | - Veit Flockerzi
- Department of Experimental and Clinical Pharmacology & Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, 66421, Homburg, Germany
| | - Andreas Keller
- Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)-Helmholtz Centre for Infection Research (HZI), Saarland University Campus, Saarbrücken, Germany
| | - Eckart Meese
- Human Genetics, Saarland University, 66421, Homburg, Germany
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13
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Xu B, Hwangbo DS, Saurabh S, Rosensweig C, Allada R, Kath WL, Braun R. Temperature-driven coordination of circadian transcriptional regulation. PLoS Comput Biol 2024; 20:e1012029. [PMID: 38648221 PMCID: PMC11108206 DOI: 10.1371/journal.pcbi.1012029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 05/21/2024] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
The circadian clock is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce circadian oscillations in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While previous studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome: genes are cycling in a temperature-specific manner, and the distributions of their phases also differ between the two conditions. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. We report that the phase differences between cycling genes increase as a function of geodesic distance in the low temperature condition, suggesting increased coordination of cycling on the gene regulatory network. Our results suggest a potential mechanism whereby the circadian clock mediates the fly's response to seasonal changes in temperature.
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Affiliation(s)
- Bingxian Xu
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, United States of America
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, United States of America
| | - Dae-Sung Hwangbo
- Department of Biology, University of Louisville, Louisville, Kentucky, United States of America
- Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Sumit Saurabh
- Department of Biology, Loyola University, Chicago, Illinois, United States of America
| | - Clark Rosensweig
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, United States of America
- Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Ravi Allada
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, United States of America
- Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - William L. Kath
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, United States of America
- Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, United States of America
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
| | - Rosemary Braun
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, United States of America
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, United States of America
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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14
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Colombo PC, Castagna F, Onat D, Wong KY, Harxhi A, Hayashi Y, Friedman RA, Pinsino A, Ladanyi A, Mebazaa A, Jelic S, Arrigo M, Lejemtel TH, Papapanou P, Sabbah HN, Schmidt AM, Yuzefpolskaya M, Demmer RT. Experimentally Induced Peripheral Venous Congestion Exacerbates Inflammation, Oxidative Stress, and Neurohormonal and Endothelial Cell Activation in Patients With Systolic Heart Failure. J Card Fail 2024; 30:580-591. [PMID: 37625581 PMCID: PMC10884348 DOI: 10.1016/j.cardfail.2023.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023]
Abstract
BACKGROUND Venous congestion (VC) is a hallmark of symptomatic heart failure (HF) requiring hospitalization; however, its role in the pathogenesis of HF progression remains unclear. We investigated whether peripheral VC exacerbates inflammation, oxidative stress and neurohormonal and endothelial cell (EC) activation in patients with HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS Two matched groups of patients with HFrEF and with no peripheral VC vs without recent HF hospitalization were studied. We modeled peripheral VC by inflating a cuff around the dominant arm, targeting ∼ 30 mmHg increase in venous pressure (venous stress test [VST]). Blood and ECs were sampled before and after 90 minutes of VST. We studied 44 patients (age 53 ± 12 years, 32% female). Circulating endothelin-1, tumor necrosis factor-α, interleukin-6, isoprostane, angiotensin II (ang-2), angiopoietin-2, vascular cell adhesion molecule-1, and CD146 significantly increased after the VST. Enhanced endothelin-1 and angiopoietin-2 responses to the VST were present in patients with vs without recent hospitalization and were prospectively associated with incident HF-related events; 6698 messenger ribonucleic acid (mRNA probe sets were differentially expressed in ECs after VST. CONCLUSIONS Experimental VC exacerbates inflammation, oxidative stress, neurohormonal and EC activation and promotes unfavorable transcriptome remodeling in ECs of patients with HFrEF. A distinct biological sensitivity to VC appears to be associated with high risk for HF progression.
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Affiliation(s)
- Paolo C Colombo
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA.
| | - Francesco Castagna
- Department of Medicine, Division of Cardiology, Montefiore Medical Center, New York, New York, USA
| | - Duygu Onat
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Ka Yuk Wong
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Ante Harxhi
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Yacki Hayashi
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Richard A Friedman
- Herbert Irving Comprehensive Cancer Center Columbia University, New York, New York, USA
| | - Alberto Pinsino
- Department of Anesthesia, Division of Critical Care, Montefiore Medical Center, New York, New York, USA
| | - Annamaria Ladanyi
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Alexander Mebazaa
- Department of Anesthesiology and Critical Care Medicine, AP-HP Saint Louis and Lariboisière University Hospitals, Paris, France
| | - Sanja Jelic
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Thierry H Lejemtel
- Section of Cardiology, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Panos Papapanou
- Department of Periodontology Columbia University Irving Medical Center, New York, New York, USA
| | - Hani N Sabbah
- Department of Medicine, Division of Cardiovascular Medicine, Henry Ford Hospital, Detroit, Michigan, USA
| | - Ann Marie Schmidt
- Department of Medicine, Division of Endocrinology, New York University, New York, New York, USA
| | - Melana Yuzefpolskaya
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Ryan T Demmer
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA; and Department of Epidemiology, Mailman School of Public Health Columbia University, New York, New York, USA
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15
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Leventhal MJ, Zanella CA, Kang B, Peng J, Gritsch D, Liao Z, Bukhari H, Wang T, Pao PC, Danquah S, Benetatos J, Nehme R, Farhi S, Tsai LH, Dong X, Scherzer CR, Feany MB, Fraenkel E. A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585262. [PMID: 38559190 PMCID: PMC10980014 DOI: 10.1101/2024.03.17.585262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Age is the strongest risk factor for developing Alzheimer's disease, the most common neurodegenerative disorder. However, the mechanisms connecting advancing age to neurodegeneration in Alzheimer's disease are incompletely understood. We conducted an unbiased, genome-scale, forward genetic screen for age-associated neurodegeneration in Drosophila to identify the underlying biological processes required for maintenance of aging neurons. To connect genetic screen hits to Alzheimer's disease pathways, we measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease. We further identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. Through multi-omic, multi-species network integration of these data, we identified relationships between screen hits and tau-mediated neurotoxicity. Furthermore, we computationally and experimentally identified relationships between screen hits and DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to attenuate the effects of age on neurodegeneration and Alzheimer's disease.
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Affiliation(s)
- Matthew J Leventhal
- MIT Ph.D. Program in Computational and Systems Biology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Camila A Zanella
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Byunguk Kang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Jiajie Peng
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - David Gritsch
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhixiang Liao
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Hassan Bukhari
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Tao Wang
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Present address: School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Ping-Chieh Pao
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Serwah Danquah
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Joseph Benetatos
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ralda Nehme
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Samouil Farhi
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Li-Huei Tsai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xianjun Dong
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Clemens R Scherzer
- Precision Neurology Program, Brigham and Women’s Hospital and Harvard Medical school, Boston, MA, USA
- APDA Center for Advanced Parkinson’s Disease Research, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Present address: Stephen and Denise Adams Center of Yale School of Medicine, CT, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ernest Fraenkel
- MIT Ph.D. Program in Computational and Systems Biology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Lead contact
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16
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Hwang AS, Kechter JA, Li X, Hughes A, Severson KJ, Boudreaux B, Bhullar P, Nassir S, Yousif M, Zhang N, Butterfield RJ, Nelson S, Xing X, Tsoi LC, Zunich S, Sekulic A, Pittelkow M, Gudjonsson JE, Mangold A. Topical Ruxolitinib in the Treatment of Necrobiosis Lipoidica: A Prospective, Open-Label Study. J Invest Dermatol 2024:S0022-202X(24)00159-3. [PMID: 38417541 DOI: 10.1016/j.jid.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
Necrobiosis lipoidica (NL) is a rare granulomatous disease. There are few effective treatments for NL. We sought to investigate the efficacy and safety of the Jak1/2 inhibitor, ruxolitnib, in the treatment of NL and identify the biomarkers associated with the disease and treatment response. We conducted an open-label, phase 2 study of ruxolitinib in 12 patients with NL. We performed transcriptomic analysis of tissue samples before and after treatment. At week 12, the mean NL lesion score decreased by 58.2% (SD = 28.7%, P = .003). Transcriptomic analysis demonstrated enrichment of type I and type II IFN pathways in baseline disease. Weighted gene coexpression network analysis demonstrated post-treatment changes in IFN pathways with key hub genes IFNG and signal transducer and activator of transcription 1 gene STAT1. Limitations include small sample size and a study group limited to patients with <10% body surface area. In conclusion, ruxolitinib is an effective treatment for NL and targets the key pathogenic mediators of the disease.
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Affiliation(s)
- Angelina S Hwang
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Jacob A Kechter
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Xing Li
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Alysia Hughes
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Kevin J Severson
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Blake Boudreaux
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Puneet Bhullar
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Shams Nassir
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Miranda Yousif
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Nan Zhang
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona, USA
| | | | - Steven Nelson
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Xianying Xing
- Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA
| | - Samantha Zunich
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | | | - Mark Pittelkow
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
| | | | - Aaron Mangold
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA.
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17
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Gobena S, Admassu B, Kinde MZ, Gessese AT. Proteomics and Its Current Application in Biomedical Area: Concise Review. ScientificWorldJournal 2024; 2024:4454744. [PMID: 38404932 PMCID: PMC10894052 DOI: 10.1155/2024/4454744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/27/2024] Open
Abstract
Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.
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Affiliation(s)
- Semira Gobena
- College of Veterinary Medicine and Animal Sciences, University of Gondar, Gondar, Ethiopia
| | - Bemrew Admassu
- Department of Veterinary Biomedical Sciences, College of Veterinary Medicine and Animal Sciences, University of Gondar, Gondar, Ethiopia
| | - Mebrie Zemene Kinde
- Department of Veterinary Biomedical Sciences, College of Veterinary Medicine and Animal Sciences, University of Gondar, Gondar, Ethiopia
| | - Abebe Tesfaye Gessese
- Department of Veterinary Biomedical Sciences, College of Veterinary Medicine and Animal Sciences, University of Gondar, Gondar, Ethiopia
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18
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Li Z, Liu G, Yang X, Shu M, Jin W, Tong Y, Liu X, Wang Y, Yuan J, Yang Y. An atlas of cell-type-specific interactome networks across 44 human tumor types. Genome Med 2024; 16:30. [PMID: 38347596 PMCID: PMC10860273 DOI: 10.1186/s13073-024-01303-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. METHODS We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions. RESULTS We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. CONCLUSIONS This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.
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Affiliation(s)
- Zekun Li
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Gerui Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaoxiao Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Meng Shu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Wen Jin
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yang Tong
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaochuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yuting Wang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Yang Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China.
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
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19
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Rawal P, Tripathi DM, Hemati H, Kumar J, Tyagi P, Sarin SK, Nain V, Kaur S. Targeted HBx gene editing by CRISPR/Cas9 system effectively reduces epithelial to mesenchymal transition and HBV replication in hepatoma cells. Liver Int 2024; 44:614-624. [PMID: 38105495 DOI: 10.1111/liv.15805] [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: 08/03/2022] [Revised: 10/28/2023] [Accepted: 11/12/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND AND AIMS Hepatitis B virus X protein (HBx) play a key role in pathogenesis of HBV-induced hepatocellular carcinoma (HCC) by promoting epithelial to mesenchymal transition (EMT). In this study, we hypothesized that inhibition of HBx is an effective strategy to combat HCC. METHODOLOGY AND RESULTS We designed and synthesized novel HBx gene specific single guide RNA (sgRNA) with CRISPR/Cas9 system and studied its in vitro effects on tumour properties of HepG2-2.15. Full length HBx gene was excised using HBx-CRISPR that resulted in significant knockdown of HBx expression in hepatoma cells. HBx-CRISPR also decreased levels of HBsAg and HBV cccDNA expression. A decreased expression of mesenchymal markers, proliferation and tumorigenic properties was observed in HBx-CRISPR treated cells as compared to controls in both two- and three- dimensional (2D and 3D) tumour models. Transcriptomics data showed that out of 1159 differentially expressed genes in HBx-CRISPR transfected cells as compared to controls, 70 genes were upregulated while 1089 genes associated with cell proliferation and EMT pathways were downregulated. CONCLUSION Thus, targeting of HBx by CRISPR/Cas9 gene editing system reduces covalently closed circular DNA (cccDNA) levels, HBsAg production and mesenchymal characteristics of HBV-HCC cells. We envision inhibition of HBx by CRISPR as a novel therapeutic approach for HBV-induced HCC.
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Affiliation(s)
- Preety Rawal
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Dinesh Mani Tripathi
- Department of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, Delhi, India
| | - Hamed Hemati
- Department of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, Delhi, India
| | - Jitendra Kumar
- Department of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, Delhi, India
| | - Purnima Tyagi
- Department of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, Delhi, India
| | - Shiv Kumar Sarin
- Department of Hepatology, Institute of Liver and Biliary Sciences, Delhi, India
| | - Vikrant Nain
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Savneet Kaur
- Department of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, Delhi, India
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20
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Liu PW, Lin J, Hou R, Cai Z, Gong Y, He PA, Yang J. Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer. Comput Biol Med 2024; 169:107926. [PMID: 38183706 DOI: 10.1016/j.compbiomed.2024.107926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/09/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.
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Affiliation(s)
- Pei-Wen Liu
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China; Geneis Beijing Co., Ltd., Beijing, China
| | - Jun Lin
- Depatment of Pathology, The People's Hospital of QuZhou City, ZheJiang, China
| | - Rui Hou
- Geneis Beijing Co., Ltd., Beijing, China
| | - Zhe Cai
- Extendcity (Shanghai) Co., Ltd., Shanghai, China
| | - Yue Gong
- Geneis Beijing Co., Ltd., Beijing, China
| | - Ping-An He
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China.
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21
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Buzzao D, Castresana-Aguirre M, Guala D, Sonnhammer ELL. Benchmarking enrichment analysis methods with the disease pathway network. Brief Bioinform 2024; 25:bbae069. [PMID: 38436561 PMCID: PMC10939300 DOI: 10.1093/bib/bbae069] [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: 09/29/2023] [Revised: 01/10/2024] [Accepted: 02/03/2024] [Indexed: 03/05/2024] Open
Abstract
Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.
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Affiliation(s)
- Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
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22
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Iwata K, Ferdousi F, Arai Y, Isoda H. Modulation of mitochondrial activity by sugarcane (Saccharum officinarum L.) top extract and its bioactive polyphenols: a comprehensive transcriptomics analysis in C2C12 myotubes and HepG2 hepatocytes. NATURAL PRODUCTS AND BIOPROSPECTING 2024; 14:2. [PMID: 38177614 PMCID: PMC10766937 DOI: 10.1007/s13659-023-00423-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
Age-related mitochondrial dysfunction leads to defects in cellular energy metabolism and oxidative stress defense systems, which can contribute to tissue damage and disease development. Among the key regulators responsible for mitochondrial quality control, peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) is an important target for mitochondrial dysfunction. We have previously reported that bioactive polyphenols extracted from sugarcane top (ST) ethanol extract (STEE) could activate neuronal energy metabolism and increase astrocyte PGC-1α transcript levels. However, their potential impact on the mitochondria activity in muscle and liver cells has not yet been investigated. To address this gap, our current study examined the effects of STEE and its polyphenols on cultured myotubes and hepatocytes in vitro. Rhodamine 123 assay revealed that the treatment with STEE and its polyphenols resulted in an increase in mitochondrial membrane potential in C2C12 myotubes. Furthermore, a comprehensive examination of gene expression patterns through transcriptome-wide microarray analysis indicated that STEE altered gene expressions related to mitochondrial functions, fatty acid metabolism, inflammatory cytokines, mitogen-activated protein kinase (MAPK) signaling, and cAMP signaling in both C2C12 myotubes and HepG2 hepatocytes. Additionally, protein-protein interaction analysis identified the PGC-1α interactive-transcription factors-targeted regulatory network of the genes regulated by STEE, and the quantitative polymerase chain reaction results confirmed that STEE and its polyphenols upregulated the transcript levels of PGC-1α in both C2C12 and HepG2 cells. These findings collectively suggest the potential beneficial effects of STEE on muscle and liver tissues and offer novel insights into the potential nutraceutical applications of this material.
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Affiliation(s)
- Kengo Iwata
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
- Nippo Co., Ltd., Daito, Osaka, 574-0062, Japan
| | - Farhana Ferdousi
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
- Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
| | | | - Hiroko Isoda
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan.
- Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan.
- AIST-University of Tsukuba Open Innovation Laboratory for Food and Medicinal Resource Engineering (FoodMed-OIL), Tsukuba, Ibaraki, 305-8572, Japan.
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23
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Li Z, Guo Z, Xiao H, Chen X, Liu W, Zhou H. Simulating neuronal development: exploring potential mechanisms for central nervous system metastasis in acute lymphoblastic leukemia. Front Oncol 2024; 13:1331802. [PMID: 38239636 PMCID: PMC10794646 DOI: 10.3389/fonc.2023.1331802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/07/2023] [Indexed: 01/22/2024] Open
Abstract
Background Acute lymphoblastic leukemia (ALL) is prone to metastasize to the central nervous system (CNS), which is an important cause of poor treatment outcomes and unfavorable prognosis. However, the pathogenesis of CNS metastasis of ALL cells has not been fully illuminated. Recent reports have shed some light on the correlation between neural mechanisms and ALL CNS metastasis. These progressions prompt us to study the relationship between ALL central nervous system metastasis and neuronal development, exploring potential biomarkers and therapeutic targets of CNS metastasis. Materials and methods ALL central nervous system metastasis- and neuronal development-related differentially expressed genes (DEGs) were identified by analyzing gene expression datasets GSE60926 and GSE13715. Target prediction and network analysis methods were applied to assess protein-protein interaction networks. Gene Ontology (GO) terms and pathway enrichment for DEGs were assessed. Co-expressed differentially expressed genes (co-DEGs) coupled with corresponding predicted microRNAs (miRNAs) were studied as well. Reverse transcription-polymerase chain reaction (RT-PCR) and flow cytometry were employed for the validation of key co-DEGs in primary ALL cells. Furthermore, ALL cells were treated with a vascular endothelial growth factor (VEGF) inhibitor to block neuronal development and assess changes in the co-DEGs. Results We identified 216, 208, and 204 DEGs in ALL CNS metastasis specimens and neuronal development samples (GSE60926 and GSE13715). CD2, CD3G, CD3D, and LCK may be implicated in ALL CNS metastasis. LAMB1, MATN3, IGFBP3, LGALS1, and NEUROD1 may be associated with neuronal development. Specifically, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) may link ALL central nervous system metastasis and neuronal development process. The miRNAs for each co-DEG could be potential biomarkers or therapeutic targets for ALL central nervous system metastasis, especially hsa-miR-22-3p, hsa-miR-548t-5p, and hsa-miR-6134. Additionally, four co-DEGs (LGALS1, TMEM71, SHISA2, and S100A11) were validated in CNS-infiltrated ALL cells. The VEGF inhibitor demonstrated a suppressive effect on mRNA and protein expression of key co-DEGs. Conclusion The bioinformatic survey and key gene validation suggest a possible correlation between ALL CNS metastasis and the neuronal development process. Simulating the neuronal development process might be a possible strategy for CNS metastasis in ALL. LGALS1, TMEM71, SHISA2, and S100A11 genes are promising and novel biomarkers and targets in ALL CNS metastasis.
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Affiliation(s)
- Ziping Li
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhi Guo
- Department of Hematology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Haitao Xiao
- Department of Anatomy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Neurological Diseases of Ministry of Education, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuexing Chen
- Institute of Hematology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Wei Liu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Zhou
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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24
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Onarman Umu ÖC, Mydland LT, Chen C, de Nanclares MP, Shurson GC, Urriola PE, Sørum H, Øverland M. Integrated multi-omics approach reveals novel associations in the rapeseed diet-microbiota-host axis in pigs. ISME COMMUNICATIONS 2024; 4:ycae061. [PMID: 38800131 PMCID: PMC11128262 DOI: 10.1093/ismeco/ycae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024]
Abstract
Diet-mediated host-microbiota interplay is a key factor in optimizing the gut function and overall health of the host. Gaining insight into the biological mechanisms behind this relationship is fundamental to finding sustainable, environment-friendly feed solutions in livestock production systems. Here, we apply a multi-omics integration approach to elucidate sustainable diet-associated host-gut microbiota interactions in pigs and we demonstrate novel and biologically relevant host-microbe associations in the gut, driven by a rapeseed meal-based feed (RSF). Interestingly, RSF-diet promoted the abundance of segmented filamentous bacteria Candidatus Arthromitus that was associated with the maintenance of mucosal immunity in the ileum of pigs. In the colon, RSF diet affected host mRNA splicing functions, which may result in different host gene products, through host-microbiota associations, particularly with the Faecalibacterium population, and through the interaction of dietary components such as sinapic acid with the host cells. Moreover, telomere maintenance and organization functions that may determine the overall health of the host were upregulated and notably associated with Subdoligranulum population in the colon of RSF diet-fed pigs. This integrative multi-omics approach provides more insight into the diet-microbiota-host axis, and a better understanding of mechanisms and opportunities to find new strategies for modulating host health and potentially improving caloric and nutritional efficiency in animal production.
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Affiliation(s)
- Özgün C Onarman Umu
- Department of Paraclinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences, Ås N-1432, Norway
| | - Liv Torunn Mydland
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås N-1432, Norway
| | - Chi Chen
- Department of Food Science and Nutrition, University of Minnesota, St Paul, MN 55108, United States
| | - Marta Pérez de Nanclares
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås N-1432, Norway
| | - Gerald C Shurson
- Department of Animal Science, University of Minnesota, St Paul, MN 55108, United States
| | - Pedro E Urriola
- Department of Animal Science, University of Minnesota, St Paul, MN 55108, United States
| | - Henning Sørum
- Department of Paraclinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences, Ås N-1432, Norway
| | - Margareth Øverland
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås N-1432, Norway
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25
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Rohan P, dos Santos EC, Abdelhay E, Binato R. High Expression of THY1 in Intestinal Gastric Cancer as a Key Factor in Tumor Biology: A Poor Prognosis-Independent Marker Related to the Epithelial-Mesenchymal Transition Profile. Genes (Basel) 2023; 15:28. [PMID: 38254918 PMCID: PMC10815053 DOI: 10.3390/genes15010028] [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: 12/03/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Gastric cancer (GC) is an important cancer-related death worldwide. Among its histological subtypes, intestinal gastric cancer (IGC) is the most common. A previous work showed that increased expression of the THY1 gene was associated with poor overall survival in IGC. Furthermore, it was shown that IGC tumor cells with high expression of THY1 have a greater capacity for tumorigenesis and metastasis in vitro. This study aimed to identify molecular differences between IGC with high and low expression of THY1. Using a feature selection method, a group of 35 genes were found to be the most informative gene set for THY1high IGC tumors. Through a classification model, these genes differentiate THY1high from THY1low tumors with 100% of accuracy both in the test subset and the independent test set. Additionally, this group of 35 genes correctly clustered 100% of the samples. An extensive validation of this potential molecular signature in multiple cohorts successfully segregated between THY1high and THY1low IGC tumors (>95%), proving to be independent of the gene expression quantification methodology. These genes are involved in central processes to tumor biology, such as the epithelial-mesenchymal transition (EMT) and remodeling of the tumor tissue composition. Moreover, patients with THY1high IGC demonstrated poor survival and a more advanced clinicopathological staging. Our findings revealed a molecular signature for IGC with high THY1 expression. This signature showed EMT and remodeling of the tumor tissue composition potentially related to the biology of IGC. Altogether, our results indicate that THY1high IGC tumors are a particular subset of tumors with a specific molecular and prognosis profile.
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Affiliation(s)
| | | | | | - Renata Binato
- Correspondence: ; Tel.: +55-21-3207-1874; Fax: +55-21-2509-2121
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26
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Rade M, Böhlen S, Neuhaus V, Löffler D, Blumert C, Merz M, Köhl U, Dehmel S, Sewald K, Reiche K. A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation. Genome Biol 2023; 24:287. [PMID: 38098113 PMCID: PMC10722659 DOI: 10.1186/s13059-023-03120-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND The coordinated transcriptional regulation of activated T-cells is based on a complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyze the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation. RESULTS Here, we identify and verify general biomarker signatures robustly evaluating T-cell activation in a time-resolved manner. We identify time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover sustained repressed, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. In addition, intermediate and late response activation signatures in CAR T-cell infusion products are correlated to immune effector cell-associated neurotoxicity syndrome. CONCLUSION This study is the first to describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source, e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies, or assessing dysregulated functions of human T-cell immunity.
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Affiliation(s)
- Michael Rade
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany.
| | - Sebastian Böhlen
- Department of Preclinical Pharmacology and In-Vitro Toxicology, Fraunhofer Institute for Toxicology and Experimental Medicine, ITEM, Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany
| | - Vanessa Neuhaus
- Department of Preclinical Pharmacology and In-Vitro Toxicology, Fraunhofer Institute for Toxicology and Experimental Medicine, ITEM, Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany
| | - Dennis Löffler
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany
| | - Conny Blumert
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany
| | - Maximilian Merz
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectiology, University Hospital of Leipzig, Leipzig, Germany
| | - Ulrike Köhl
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany
- Institute for Clinical Immunology, Leipzig University, Johannisallee 30, 04103, Leipzig, Germany
| | - Susann Dehmel
- Department of Preclinical Pharmacology and In-Vitro Toxicology, Fraunhofer Institute for Toxicology and Experimental Medicine, ITEM, Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany
| | - Katherina Sewald
- Department of Preclinical Pharmacology and In-Vitro Toxicology, Fraunhofer Institute for Toxicology and Experimental Medicine, ITEM, Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany
- Institute for Clinical Immunology, Leipzig University, Johannisallee 30, 04103, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Leipzig, Germany
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Soleymani S, Gravel N, Huang LC, Yeung W, Bozorgi E, Bendzunas NG, Kochut KJ, Kannan N. Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology. PeerJ 2023; 11:e16087. [PMID: 38077442 PMCID: PMC10704995 DOI: 10.7717/peerj.16087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/22/2023] [Indexed: 12/18/2023] Open
Abstract
The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.
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Affiliation(s)
- Saber Soleymani
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Wayland Yeung
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Elika Bozorgi
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathaniel G. Bendzunas
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
| | - Krzysztof J. Kochut
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Natarajan Kannan
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
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Comertpay B, Gov E. Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches. Biosystems 2023; 234:105063. [PMID: 37852410 DOI: 10.1016/j.biosystems.2023.105063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 10/20/2023]
Abstract
Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.
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Affiliation(s)
- Betul Comertpay
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
| | - Esra Gov
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.
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29
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Denis AA, Toledo D, Hakim QA, Quintana AA, Escobar CR, Oluwole SA, Costa A, Garcia EG, Hill AR, Agatemor C. Ligand-Independent Activation of Aryl Hydrocarbon Receptor and Attenuation of Glutamine Levels by Natural Deep Eutectic Solvent. Chembiochem 2023; 24:e202300540. [PMID: 37615422 DOI: 10.1002/cbic.202300540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 08/25/2023]
Abstract
Natural deep eutectic solvents (NADESs) are emerging sustainable alternatives to conventional organic solvents. Beyond their role as laboratory solvents, NADESs are increasingly explored in drug delivery and as therapeutics. Their increasing applications notwithstanding, our understanding of how they interact with biomolecules at multiple levels - metabolome, proteome, and transcriptome - within human cell remain poor. Here, we deploy integrated metabolomics, proteomics, and transcriptomics to probe how NADESs perturb the molecular landscape of human cells. In a human cell line model, we found that an archetypal NADES derived from choline and geranic acid (CAGE) significantly altered the metabolome, proteome, and transcriptome. CAGE upregulated indole-3-lactic acid and 4-hydroxyphenyllactic acid levels, resulting in ligand-independent activation of aryl hydrocarbon receptor to signal the transcription of genes with implications for inflammation, immunomodulation, cell development, and chemical detoxification. Further, treating the cell line with CAGE downregulated glutamine biosynthesis, a nutrient rapidly proliferating cancer cells require. CAGE's ability to attenuate glutamine levels is potentially relevant for cancer treatment. These findings suggest that NADESs, even when derived from natural components like choline, can indirectly modulate cell biology at multiple levels, expanding their applications beyond chemistry to biomedicine and biotechnology.
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Affiliation(s)
| | - Daniela Toledo
- Department of Chemistry, University of Miami, Miami, FL-33146, USA
| | | | | | | | | | - Arthur Costa
- Department of Chemistry, University of Miami, Miami, FL-33146, USA
| | | | - Anaya Rose Hill
- Department of Biology, University of Miami, Miami, FL-33146, USA
| | - Christian Agatemor
- Department of Chemistry, University of Miami, Miami, FL-33146, USA
- Department of Biology, University of Miami, Miami, FL-33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami Health System, University of Miami, Miami, FL-33136, USA
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Bowley TY, Merkley SD, Lagutina IV, Ortiz MC, Lee M, Tawfik B, Marchetti D. Targeting Translation and the Cell Cycle Inversely Affects CTC Metabolism but Not Metastasis. Cancers (Basel) 2023; 15:5263. [PMID: 37958436 PMCID: PMC10650766 DOI: 10.3390/cancers15215263] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Melanoma brain metastasis (MBM) is significantly associated with poor prognosis and is diagnosed in 80% of patients at autopsy. Circulating tumor cells (CTCs) are "seeds" of metastasis and the smallest functional units of cancer. Our multilevel approach has previously identified a CTC RPL/RPS gene signature directly linked to MBM onset. We hypothesized that targeting ribogenesis prevents MBM/metastasis in CTC-derived xenografts. We treated parallel cohorts of MBM mice with FDA-approved protein translation inhibitor omacetaxine with or without CDK4/CDK6 inhibitor palbociclib, and monitored metastatic development and cell proliferation. Necropsies and IVIS imaging showed decreased MBM/extracranial metastasis in drug-treated mice, and RNA-Seq on mouse-blood-derived CTCs revealed downregulation of four RPL/RPS genes. However, mitochondrial stress tests and RT-qPCR showed that omacetaxine and palbociclib inversely affected glycolytic metabolism, demonstrating that dual targeting of cell translation/proliferation is critical to suppress plasticity in metastasis-competent CTCs. Equally relevant, we provide the first-ever functional metabolic characterization of patient-derived circulating neoplastic cells/CTCs.
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Affiliation(s)
- Tetiana Y. Bowley
- Division of Molecular Medicine, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; (T.Y.B.); (S.D.M.); (M.C.O.); (M.L.)
| | - Seth D. Merkley
- Division of Molecular Medicine, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; (T.Y.B.); (S.D.M.); (M.C.O.); (M.L.)
| | - Irina V. Lagutina
- Animal Models Shared Resource, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM 87120, USA;
| | - Mireya C. Ortiz
- Division of Molecular Medicine, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; (T.Y.B.); (S.D.M.); (M.C.O.); (M.L.)
| | - Margaret Lee
- Division of Molecular Medicine, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; (T.Y.B.); (S.D.M.); (M.C.O.); (M.L.)
| | - Bernard Tawfik
- Division of Hematology and Oncology, Department of Internal Medicine, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM 87120, USA;
| | - Dario Marchetti
- Division of Molecular Medicine, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA; (T.Y.B.); (S.D.M.); (M.C.O.); (M.L.)
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Xu B, Hwangbo DS, Saurabh S, Rosensweig C, Allada R, Kath WL, Braun R. Temperature-driven coordination of circadian transcriptome regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.563979. [PMID: 37961403 PMCID: PMC10634908 DOI: 10.1101/2023.10.27.563979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The circadian rhythm is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce a circadian oscillation in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. Our results point to the mechanisms by which the circadian clock mediates the fly's response to seasonal changes in temperature.
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Affiliation(s)
- Bingxian Xu
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
| | - Dae-Sung Hwangbo
- Department of Biology, University of Louisville, Louisville, KY 40292, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Sumit Saurabh
- Department of Biology, Loyola University, Chicago, IL 60660, USA
| | - Clark Rosensweig
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Ravi Allada
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - William L Kath
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
| | - Rosemary Braun
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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Fu L, Li M, Lv J, Yang C, Zhang Z, Qin S, Li W, Wang X, Chen L. Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma. Front Endocrinol (Lausanne) 2023; 14:1270772. [PMID: 37955007 PMCID: PMC10634586 DOI: 10.3389/fendo.2023.1270772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. Methods This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Results Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. Conclusion To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.
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Affiliation(s)
- Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Manshi Li
- Department of Radiation Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chengcheng Yang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zihan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xinyan Wang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Gao J, Yi X, Wang Z. The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises. Comput Struct Biotechnol J 2023; 21:4933-4943. [PMID: 37867968 PMCID: PMC10585227 DOI: 10.1016/j.csbj.2023.10.016] [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: 06/13/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023] Open
Abstract
The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.
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Affiliation(s)
- Jingyuan Gao
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Xinzhu Yi
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Zhang Wang
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
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Jain SS, McNamara ME, Varghese RS, Ressom HW. Deconvolution of immune cell composition and biological age of hepatocellular carcinoma using DNA methylation. Methods 2023; 218:125-132. [PMID: 37574160 PMCID: PMC10529003 DOI: 10.1016/j.ymeth.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/15/2023] Open
Abstract
Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.
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Affiliation(s)
- Sidharth S Jain
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Megan E McNamara
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Rency S Varghese
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Habtom W Ressom
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
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Yoshimura Y, Muto Y, Omachi K, Miner JH, Humphreys BD. Elucidating the Proximal Tubule HNF4A Gene Regulatory Network in Human Kidney Organoids. J Am Soc Nephrol 2023; 34:1672-1686. [PMID: 37488681 PMCID: PMC10561821 DOI: 10.1681/asn.0000000000000197] [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: 03/07/2023] [Accepted: 07/08/2023] [Indexed: 07/26/2023] Open
Abstract
SIGNIFICANCE STATEMENT HNF4 genes promote proximal tubule differentiation in mice, but their function in human nephrogenesis is not fully defined. This study uses human pluripotent stem cell (PSC)-derived kidney organoids as a model to investigate HNF4A and HNF4G functions. The loss of HNF4A , but not HNF4G , impaired reabsorption-related molecule expression and microvilli formation in human proximal tubules. Cleavage under targets and release using nuclease (CUT&RUN) sequencing and CRISPR-mediated transcriptional activation (CRISPRa) further confirm that HNF4A directly regulates its target genes. Human kidney organoids provide a good model for studying transcriptional regulation in human kidney development. BACKGROUND The proximal tubule plays a major role in electrolyte homeostasis. Previous studies have shown that HNF4A regulates reabsorption-related genes and promotes proximal tubule differentiation during murine kidney development. However, the functions and gene regulatory mechanisms of HNF4 family genes in human nephrogenesis have not yet been investigated. METHODS We generated HNF4A -knock out (KO), HNF4G -KO, and HNF4A/4G -double KO human pluripotent stem cell lines, differentiated each into kidney organoids, and used immunofluorescence analysis, electron microscopy, and RNA-seq to analyze them. We probed HNF4A-binding sites genome-wide by cleavage under targets and release using nuclease sequencing in both human adult kidneys and kidney organoid-derived proximal tubular cells. Clustered Regularly Interspaced Short Palindromic Repeats-mediated transcriptional activation validated HNF4A and HNF4G function in proximal tubules during kidney organoid differentiation. RESULTS Organoids lacking HNF4A , but not HNF4G , showed reduced expression of transport-related, endocytosis-related, and brush border-related genes, as well as disorganized brush border structure in the apical lumen of the organoid proximal tubule. Cleavage under targets and release using nuclease revealed that HNF4A primarily bound promoters and enhancers of genes that were downregulated in HNF4A -KO, suggesting direct regulation. Induced expression of HNF4A or HNF4G by CRISPR-mediated transcriptional activation drove increased expression of selected target genes during kidney organoid differentiation. CONCLUSIONS This study reveals regulatory mechanisms of HNF4A and HNF4G during human proximal tubule differentiation. The experimental strategy can be applied more broadly to investigate transcriptional regulation in human kidney development.
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Affiliation(s)
- Yasuhiro Yoshimura
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Yoshiharu Muto
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Kohei Omachi
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Jeffrey H. Miner
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Benjamin D. Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
- Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri
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Sharma A, Kumar R, Varadwaj P. Developing human olfactory network and exploring olfactory receptor-odorant interaction. J Biomol Struct Dyn 2023; 41:8941-8960. [PMID: 36310099 DOI: 10.1080/07391102.2022.2138976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
The Olfactory receptor (OR)-odorant interactions are perplexed. ORs can bind to structurally diverse odorants associated with one or more odor percepts. Various attempts have been made to understand the intricacies of OR-odorant interaction. In this study, experimentally documented OR-odorant interactions are investigated comprehensively to; (a) suggest potential odor percepts for ORs based on the OR-OR network; (b) determine how odorants interacting with specific ORs differ in terms of inherent pharmacophoric features and molecular properties, (c) identify molecular interactions that explained OR-odorant interactions of selective ORs; and (d) predict the probable role of ORs other than olfaction. Human olfactory receptor network (hORnet) is developed to study possible odor percepts for ORs. We identified six molecular properties which showed variation and significant patterns to differentiate odorants binding with five ORs. The pharmacophore analysis revealed that odorants subset of five ORs follow similar pharmacophore hypothesis, (one hydrogen acceptor and two hydrophobic regions) but differ in terms of distance and orientation of pharmacophoric features. To ascertain the binding site residues and key interactions between the selected ORs and their interacting odorants, 3D-structure modelling, docking and molecular dynamics studies were carried out. Lastly, the potential role of ORs beyond olfaction is explored. A human OR-OR network was developed to suggest possible odor percepts for ORs using empirically proven OR-odorant interactions. We sought to find out significant characteristics, molecular properties, and molecular interactions that could explain OR-odorant interactions and add to the understanding of the complex issue of odor perception.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Anju Sharma
- Department of Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, Uttar Pradesh, India
| | - Pritish Varadwaj
- Department of Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
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Kunes RZ, Walle T, Land M, Nawy T, Pe'er D. Supervised discovery of interpretable gene programs from single-cell data. Nat Biotechnol 2023:10.1038/s41587-023-01940-3. [PMID: 37735262 PMCID: PMC10958532 DOI: 10.1038/s41587-023-01940-3] [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: 12/21/2022] [Accepted: 08/09/2023] [Indexed: 09/23/2023]
Abstract
Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene-gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.
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Affiliation(s)
- Russell Z Kunes
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Thomas Walle
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Cooperation Unit Virotherapy, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Max Land
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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Jamal M, Lei Y, He H, Zeng X, Bangash HI, Xiao D, Shao L, Zhou F, Zhang Q. CCR9 overexpression promotes T-ALL progression by enhancing cholesterol biosynthesis. Front Pharmacol 2023; 14:1257289. [PMID: 37745085 PMCID: PMC10512069 DOI: 10.3389/fphar.2023.1257289] [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: 07/12/2023] [Accepted: 08/22/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of the lymphoid progenitor cells, contributing to ∼ 20% of the total ALL cases, with a higher prevalence in adults than children. Despite the important role of human T-ALL cell lines in understanding the pathobiology of the disease, a detailed comparison of the tumorigenic potentials of two commonly used T-ALL cell lines, MOLT4 and JURKAT cells, is still lacking. Methodology: In the present study, NOD-Prkdc scid IL2rgd ull (NTG) mice were intravenously injected with MOLT4, JURKAT cells, and PBS as a control. The leukemiac cell homing/infiltration into the bone marrow, blood, liver and spleen was investigated for bioluminescence imaging, flow cytometry, and immunohistochemistry staining. Gene expression profiling of the two cell lines was performed via RNA-seq to identify the differentially expressed genes (DEGs). CCR9 identified as a DEG, was further screened for its role in invasion and metastasis in both cell lines in vitro. Moreover, a JURKAT cell line with overexpressed CCR9 (Jurkat-OeCCR9) was investigated for T-ALL formation in the NTG mice as compared to the GFP control. Jurkat-OeCCR9 cells were then subjected to transcriptome analysis to identify the genes and pathways associated with the upregulation of CCR9 leading to enhanced tumirogenesis. The DEGs of the CCR9-associated upregulation were validated both at mRNA and protein levels. Simvastatin was used to assess the effect of cholesterol biosynthesis inhibition on the aggressiveness of T-ALL cells. Results: Comparison of the leukemogenic potentials of the two T-ALL cell lines showed the relatively higher leukemogenic potential of MOLT4 cells, characterized by their enhanced tissue infiltration in NOD-PrkdcscidIL2rgdull (NTG) mice. Transcriptmoe analysis of the two cell lines revealed numerous DEGs, including CCR9, enriched in vital signaling pathways associated with growth and proliferation. Notably, the upregulation of CCR9 also promoted the tissue infiltration of JURKAT cells in vitro and in NTG mice. Transcriptome analysis revealed that CCR9 overexpression facilitated cholesterol production by upregulating the expression of the transcriptional factor SREBF2, and the downstream genes: MSMO1, MVD, HMGCS1, and HMGCR, which was then corroborated at the protein levels. Notably, simvastatin treatment reduced the migration of the CCR9-overexpressing JURKAT cells, suggesting the importance of cholesterol in T-ALL progression. Conclusions: This study highlights the distinct tumorigenic potentials of two T-ALL cell lines and reveals CCR9-regulated enhanced cholesterol biosynthesis in T-ALL.
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Affiliation(s)
- Muhammad Jamal
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Yufei Lei
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Hengjing He
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Xingruo Zeng
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Hina Iqbal Bangash
- State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Di Xiao
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Liang Shao
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Quiping Zhang
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
- Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan University, Wuhan, China
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Guthrie J, Ko¨stel Bal S, Lombardo SD, Mu¨ller F, Sin C, Hu¨tter CV, Menche J, Boztug K. AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation. SCIENCE ADVANCES 2023; 9:eadg6375. [PMID: 37656781 PMCID: PMC10848965 DOI: 10.1126/sciadv.adg6375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/01/2023] [Indexed: 09/03/2023]
Abstract
Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.
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Affiliation(s)
- Julia Guthrie
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Sevgi Ko¨stel Bal
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
| | - Salvo Danilo Lombardo
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Felix Mu¨ller
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Celine Sin
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Christiane V. R. Hu¨tter
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna BioCenter, A-1030 Vienna, Austria
| | - Jo¨rg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
- St. Anna Children’s Hospital, Kinderspitalgasse 6, A-1090, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics and Adolescent Medicine, Währinger Gürtel 18-20, A-1090 Vienna, Austria
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40
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Zhang H, Steele JR, Kahrood HV, Lucas DD, Shah AD, Schittenhelm RB. Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data. J Proteome Res 2023; 22:2890-2899. [PMID: 37584946 DOI: 10.1021/acs.jproteome.3c00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.
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Affiliation(s)
- Haijian Zhang
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Joel R Steele
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Hossein Valipour Kahrood
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Deanna Deveson Lucas
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anup D Shah
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
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Shin E, Han SH, Park IS, Wee JH, Lee JS, Kim H. Does the Necrotic Portion of Metastatic Lymphadenopathy from Squamous Cell Carcinoma Still Have Tumoral Oncologic Information? Differential Diagnosis of Benign Necrotic Lymphadenopathy Using microRNA. Biomedicines 2023; 11:2407. [PMID: 37760848 PMCID: PMC10525664 DOI: 10.3390/biomedicines11092407] [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: 07/28/2023] [Revised: 08/14/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Neck necrotic lymph nodes commonly correspond to metastasis or benign inflammatory conditions such as Kikuchi disease and tuberculosis. Ultrasound-guided biopsy can be used for differential diagnosis, but results may be unclear. Therefore, this study aimed to identify target microRNAs (miRNAs) and genes for the differential diagnosis of inflammatory and malignant necrotic lymph nodes. We selected six inflammatory lymphadenitis formalin-fixed paraffin-embedded (FFPE) samples that showed internal necrosis and five cancer necrotic FFPE samples. Tissue microarray (TMA) was performed to separate the necrotic and cancerous portions. Total RNA was extracted from six pairs of separated inflammatory necrosis, five pairs of cancer necrosis, and cancer portions. Differentially expressed miRNAs were analyzed by comparing inflammatory necrosis, cancer, and cancer necrosis. Seventeen miRNAs were upregulated in cancer necrosis compared to inflammatory necrosis, and two miRNAs (hsa-miR-155-5p and hsa-miR-146b-5p) showed lower expression in cancer necrotic cells. Nineteen miRNAs that were differentially expressed between inflammatory and cancer necrosis were analyzed for target gene expression; these transcripts demonstrated a clear relationship with cancer. The differentially expressed miRNAs in inflammatory and tumor necrosis were associated with cancer-related pathways. These preliminary results might help in the differential diagnosis of cervical metastatic necrotic lymphadenopathy and avoiding unnecessary excisional biopsies.
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Affiliation(s)
- Eun Shin
- Department of Pathology, Dongtan Sacred Heart Hospital, Hwaseong 18450, Republic of Korea;
| | - Seung Hoon Han
- Department of Otorhinolaryngology-Head and Neck Surgery, Dongtan Sacred Heart Hospital, Hwaseong 18450, Republic of Korea; (S.H.H.); (I.-S.P.)
| | - Il-Seok Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Dongtan Sacred Heart Hospital, Hwaseong 18450, Republic of Korea; (S.H.H.); (I.-S.P.)
| | - Jee Hye Wee
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (J.H.W.); (J.S.L.)
| | - Joong Seob Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (J.H.W.); (J.S.L.)
| | - Heejin Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (J.H.W.); (J.S.L.)
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Wang Y, Yu Y, Li L, Zheng M, Zhou J, Gong H, Feng B, Wang X, Meng X, Cui Y, Xia Y, Chu S, Lin L, Chang H, Zhou R, Ma M, Li Z, Ji R, Lu M, Yang X, Zuo X, Li S, Li Y. Bile acid-dependent transcription factors and chromatin accessibility determine regional heterogeneity of intestinal antimicrobial peptides. Nat Commun 2023; 14:5093. [PMID: 37607912 PMCID: PMC10444805 DOI: 10.1038/s41467-023-40565-7] [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: 10/26/2022] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Antimicrobial peptides (AMPs) are important mediators of intestinal immune surveillance. However, the regional heterogeneity of AMPs and its regulatory mechanisms remain obscure. Here, we clarified the regional heterogeneity of intestinal AMPs at the single-cell level, and revealed a cross-lineages AMP regulation mechanism that bile acid dependent transcription factors (BATFs), NR1H4, NR1H3 and VDR, regulate AMPs through a ligand-independent manner. Bile acids regulate AMPs by perturbing cell differentiation rather than activating BATFs signaling. Chromatin accessibility determines the potential of BATFs to regulate AMPs at the pre-transcriptional level, thus shaping the regional heterogeneity of AMPs. The BATFs-AMPs axis also participates in the establishment of intestinal antimicrobial barriers of fetuses and the defects of antibacterial ability during Crohn's disease. Overall, BATFs and chromatin accessibility play essential roles in shaping the regional heterogeneity of AMPs at pre- and postnatal stages, as well as in maintenance of antimicrobial immunity during homeostasis and disease.
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Affiliation(s)
- Yue Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Yanbo Yu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China
| | - Lixiang Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China
| | - Mengqi Zheng
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Jiawei Zhou
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Haifan Gong
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Bingcheng Feng
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Xiao Wang
- Department of Pathology, Qilu Hospital of Shandong University, Jinan, China
| | - Xuanlin Meng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yanyan Cui
- Advanced Medical Research Institute, Shandong University, Jinan, China
| | - Yanan Xia
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Shuzheng Chu
- Advanced Medical Research Institute, Shandong University, Jinan, China
| | - Lin Lin
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Huijun Chang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Ruchen Zhou
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Mingjun Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China
| | - Ming Lu
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoyun Yang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China.
| | - Shiyang Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.
- Advanced Medical Research Institute, Shandong University, Jinan, China.
- Key Laboratory for Experimental Teratology of Ministry of Education, Shandong University, Jinan, China.
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Clinical Research Center for digestive disease, Jinan, China.
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43
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Ribarski-Chorev I, Schudy G, Strauss C, Schlesinger S. Short heat shock has a long-term effect on mesenchymal stem cells' transcriptome. iScience 2023; 26:107305. [PMID: 37529103 PMCID: PMC10387575 DOI: 10.1016/j.isci.2023.107305] [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: 12/22/2022] [Revised: 05/23/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
The adverse effects of heat stress (HS) on physiological systems are well documented, yet the underlying molecular mechanisms behind it remain poorly understood. To address this knowledge gap, we conducted a comprehensive investigation into the impact of HS on mesenchymal stem cells (MSCs), focusing on their morphology, phenotype, proliferative capacity, and fate determination. Our in-depth analysis of the MSCs' transcriptome revealed a significant influence of HS on the transcriptional landscape. Notably, even after a short period of stress, we observed a persistent alteration in cell identity, potentially mediated by the activation of bivalent genes. Furthermore, by comparing the differentially expressed genes following short HS with their transcriptional state after recovery, we identified the transient upregulation of MLL and other histone modifiers, providing a potential mechanistic explanation for the stable activation of bivalent genes. This could be used to predict and modify the long-term effect of HS on cell identity.
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Affiliation(s)
- Ivana Ribarski-Chorev
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Gisele Schudy
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Carmit Strauss
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Sharon Schlesinger
- The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
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44
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Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, Fine RS, Miao J, Patwardhan TA, Kanai M, Nasser J, Fulco CP, Tashman KC, Aguet F, Li T, Ordovas-Montanes J, Smillie CS, Biton M, Shalek AK, Ananthakrishnan AN, Xavier RJ, Regev A, Gupta RM, Lage K, Ardlie KG, Hirschhorn JN, Lander ES, Engreitz JM, Finucane HK. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet 2023; 55:1267-1276. [PMID: 37443254 PMCID: PMC10836580 DOI: 10.1038/s41588-023-01443-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 06/09/2023] [Indexed: 07/15/2023]
Abstract
Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.
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Affiliation(s)
- Elle M Weeks
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA
| | | | - Brian L Trippe
- Program in Computational & Systems Biology, MIT, Cambridge, MA, USA
- Computer Science & Artificial Intelligence Lab, MIT, Cambridge, MA, USA
| | - Rebecca S Fine
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals Incorporated, Boston, MA, USA
| | - Jenkai Miao
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Tejal A Patwardhan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Masahiro Kanai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, MGH, Boston, MA, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Bristol Myers Squibb, Cambridge, MA, USA
| | | | | | - Taibo Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MD-PhD Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jose Ordovas-Montanes
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA, USA
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Christopher S Smillie
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Computational & Systems Biology, MIT, Cambridge, MA, USA
| | - Moshe Biton
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, MGH, Boston, MA, USA
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Alex K Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Ragon Institute of MGH, MMIT, Cambridge, MA, USA
| | - Ashwin N Ananthakrishnan
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, MGH, Boston, MA, USA
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, MIT, Cambridge, MA, USA
- Genentech, San Francisco, CA, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kasper Lage
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, MGH, Boston, MA, USA
| | - Kristin G Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, MGH, Boston, MA, USA.
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Yap JYY, Goh LSH, Lim AJW, Chong SS, Lim LJ, Lee CG. Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:3749. [PMID: 37509410 PMCID: PMC10377993 DOI: 10.3390/cancers15143749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/30/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.
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Affiliation(s)
- Josephine Yu Yan Yap
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- NUS Graduate School, National University of Singapore, Singapore 119077, Singapore
| | - Laura Shih Hui Goh
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Ashley Jun Wei Lim
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Samuel S Chong
- Department of Paediatrics and Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
| | - Lee Jin Lim
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Caroline G Lee
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- NUS Graduate School, National University of Singapore, Singapore 119077, Singapore
- Division of Cellular & Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore, Singapore 168583, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
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Roverso M, Dogra R, Visentin S, Pettenuzzo S, Cappellin L, Pastore P, Bogialli S. Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers. MASS SPECTROMETRY REVIEWS 2023; 42:1424-1461. [PMID: 35474466 DOI: 10.1002/mas.21777] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/15/2021] [Accepted: 04/04/2022] [Indexed: 06/07/2023]
Abstract
Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.
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Affiliation(s)
- Marco Roverso
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Raghav Dogra
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Silvia Visentin
- Department of Women's and Children's Health, University of Padova, Padova, Italy
| | - Silvia Pettenuzzo
- Department of Chemical Sciences, University of Padova, Padova, Italy
- Center Agriculture Food Environment (C3A), University of Trento, San Michele all'Adige, Italy
| | - Luca Cappellin
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Paolo Pastore
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Sara Bogialli
- Department of Chemical Sciences, University of Padova, Padova, Italy
- Institute of Condensed Matter Chemistry and Technologies for Energy (ICMATE), National Research Council-CNR, Padova, Italy
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Chatterjee B, Thakur SS. Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers. Proteomics 2023; 23:e2200257. [PMID: 36919629 DOI: 10.1002/pmic.202200257] [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: 06/14/2022] [Revised: 03/04/2023] [Accepted: 03/06/2023] [Indexed: 03/16/2023]
Abstract
Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.
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Affiliation(s)
- Bhaswati Chatterjee
- National Institute of Pharmaceutical Education and Research, Hyderabad, India
- National Institute of Animal Biotechnology (NIAB), Hyderabad, India
| | - Suman S Thakur
- Centre for Cellular and Molecular Biology, Hyderabad, India
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Subedi P, Huber K, Sterr C, Dietz A, Strasser L, Kaestle F, Hauck SM, Duchrow L, Aldrian C, Monroy Ordonez EB, Luka B, Thomsen AR, Henke M, Gomolka M, Rößler U, Azimzadeh O, Moertl S, Hornhardt S. Towards unravelling biological mechanisms behind radiation-induced oral mucositis via mass spectrometry-based proteomics. Front Oncol 2023; 13:1180642. [PMID: 37384298 PMCID: PMC10298177 DOI: 10.3389/fonc.2023.1180642] [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: 03/06/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023] Open
Abstract
Objective Head and neck cancer (HNC) accounts for almost 890,000 new cases per year. Radiotherapy (RT) is used to treat the majority of these patients. A common side-effect of RT is the onset of oral mucositis, which decreases the quality of life and represents the major dose-limiting factor in RT. To understand the origin of oral mucositis, the biological mechanisms post-ionizing radiation (IR) need to be clarified. Such knowledge is valuable to develop new treatment targets for oral mucositis and markers for the early identification of "at-risk" patients. Methods Primary keratinocytes from healthy volunteers were biopsied, irradiated in vitro (0 and 6 Gy), and subjected to mass spectrometry-based analyses 96 h after irradiation. Web-based tools were used to predict triggered biological pathways. The results were validated in the OKF6 cell culture model. Immunoblotting and mRNA validation was performed and cytokines present in cell culture media post-IR were quantified. Results Mass spectrometry-based proteomics identified 5879 proteins in primary keratinocytes and 4597 proteins in OKF6 cells. Amongst them, 212 proteins in primary keratinocytes and 169 proteins in OKF6 cells were differentially abundant 96 h after 6 Gy irradiation compared to sham-irradiated controls. In silico pathway enrichment analysis predicted interferon (IFN) response and DNA strand elongation pathways as mostly affected pathways in both cell systems. Immunoblot validations showed a decrease in minichromosome maintenance (MCM) complex proteins 2-7 and an increase in IFN-associated proteins STAT1 and ISG15. In line with affected IFN signalling, mRNA levels of IFNβ and interleukin 6 (IL-6) increased significantly following irradiation and also levels of secreted IL-1β, IL-6, IP-10, and ISG15 were elevated. Conclusion This study has investigated biological mechanisms in keratinocytes post-in vitro ionizing radiation. A common radiation signature in keratinocytes was identified. The role of IFN response in keratinocytes along with increased levels of pro-inflammatory cytokines and proteins could hint towards a possible mechanism for oral mucositis.
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Affiliation(s)
- Prabal Subedi
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Katharina Huber
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Christoph Sterr
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Anne Dietz
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Lukas Strasser
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Felix Kaestle
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Stefanie M. Hauck
- Helmholtz Zentrum München, German Research Centre for Environmental Health, Metabolomics and Proteomics Core, Munich, Germany
| | - Lukas Duchrow
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Christine Aldrian
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) partner site Freiburg, Freiburg, Germany
| | - Elsa Beatriz Monroy Ordonez
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) partner site Freiburg, Freiburg, Germany
| | - Benedikt Luka
- Department of Conservative Dentistry Periodontology and Preventive Dentistry, Hannover Medical School (MHH), Hannover, Germany
| | - Andreas R. Thomsen
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) partner site Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (dkfz), Heidelberg, Germany
| | - Michael Henke
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) partner site Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (dkfz), Heidelberg, Germany
| | - Maria Gomolka
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Ute Rößler
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Omid Azimzadeh
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Simone Moertl
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
| | - Sabine Hornhardt
- Bundesamt für Strahlenschutz/Federal Office for Radiation Protection, Neuherberg, Germany
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Jha D, Al-Taie Z, Krek A, Eshghi ST, Fantou A, Laurent T, Tankelevich M, Cao X, Meringer H, Livanos AE, Tokuyama M, Cossarini F, Bourreille A, Josien R, Hou R, Canales-Herrerias P, Ungaro RC, Kayal M, Marion J, Polydorides AD, Ko HM, D’souza D, Merand R, Kim-Schulze S, Hackney JA, Nguyen A, McBride JM, Yuan GC, Colombel JF, Martin JC, Argmann C, Suárez-Fariñas M, Petralia F, Mehandru S. Myeloid cell influx into the colonic epithelium is associated with disease severity and non-response to anti-Tumor Necrosis Factor Therapy in patients with Ulcerative Colitis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.542863. [PMID: 37333091 PMCID: PMC10274630 DOI: 10.1101/2023.06.02.542863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Ulcerative colitis (UC) is an idiopathic chronic inflammatory disease of the colon with sharply rising global prevalence. Dysfunctional epithelial compartment (EC) dynamics are implicated in UC pathogenesis although EC-specific studies are sparse. Applying orthogonal high-dimensional EC profiling to a Primary Cohort (PC; n=222), we detail major epithelial and immune cell perturbations in active UC. Prominently, reduced frequencies of mature BEST4+OTOP2+ absorptive and BEST2+WFDC2+ secretory epithelial enterocytes were associated with the replacement of homeostatic, resident TRDC+KLRD1+HOPX+ γδ+ T cells with RORA+CCL20+S100A4+ TH17 cells and the influx of inflammatory myeloid cells. The EC transcriptome (exemplified by S100A8, HIF1A, TREM1, CXCR1) correlated with clinical, endoscopic, and histological severity of UC in an independent validation cohort (n=649). Furthermore, therapeutic relevance of the observed cellular and transcriptomic changes was investigated in 3 additional published UC cohorts (n=23, 48 and 204 respectively) to reveal that non-response to anti-Tumor Necrosis Factor (anti-TNF) therapy was associated with EC related myeloid cell perturbations. Altogether, these data provide high resolution mapping of the EC to facilitate therapeutic decision-making and personalization of therapy in patients with UC.
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Affiliation(s)
- Divya Jha
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zainab Al-Taie
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Shadi Toghi Eshghi
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Aurelie Fantou
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Thomas Laurent
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Michael Tankelevich
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xuan Cao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Hadar Meringer
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexandra E Livanos
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Minami Tokuyama
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Cossarini
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arnaud Bourreille
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Regis Josien
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Ruixue Hou
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Pablo Canales-Herrerias
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan C. Ungaro
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maia Kayal
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Marion
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Huaibin M. Ko
- Department of Pathology and Cell Biology, Columbia University Medical Center-New York Presbyterian Hospital, New York, New York
| | - Darwin D’souza
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raphael Merand
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jason A. Hackney
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Allen Nguyen
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Jacqueline M. McBride
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Jean Frederic Colombel
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerome C. Martin
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Carmen Argmann
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Mayte Suárez-Fariñas
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Saurabh Mehandru
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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50
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Tichauer JE, Arellano G, Acuña E, González LF, Kannaiyan NR, Murgas P, Panadero-Medianero C, Ibañez-Vega J, Burgos PI, Loda E, Miller SD, Rossner MJ, Gebicke-Haerter PJ, Naves R. Interferon-gamma ameliorates experimental autoimmune encephalomyelitis by inducing homeostatic adaptation of microglia. Front Immunol 2023; 14:1191838. [PMID: 37334380 PMCID: PMC10272814 DOI: 10.3389/fimmu.2023.1191838] [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: 03/22/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Compelling evidence has shown that interferon (IFN)-γ has dual effects in multiple sclerosis and in its animal model of experimental autoimmune encephalomyelitis (EAE), with results supporting both a pathogenic and beneficial function. However, the mechanisms whereby IFN-γ may promote neuroprotection in EAE and its effects on central nervous system (CNS)-resident cells have remained an enigma for more than 30 years. In this study, the impact of IFN-γ at the peak of EAE, its effects on CNS infiltrating myeloid cells (MC) and microglia (MG), and the underlying cellular and molecular mechanisms were investigated. IFN-γ administration resulted in disease amelioration and attenuation of neuroinflammation associated with significantly lower frequencies of CNS CD11b+ myeloid cells and less infiltration of inflammatory cells and demyelination. A significant reduction in activated MG and enhanced resting MG was determined by flow cytometry and immunohistrochemistry. Primary MC/MG cultures obtained from the spinal cord of IFN-γ-treated EAE mice that were ex vivo re-stimulated with a low dose (1 ng/ml) of IFN-γ and neuroantigen, promoted a significantly higher induction of CD4+ regulatory T (Treg) cells associated with increased transforming growth factor (TGF)-β secretion. Additionally, IFN-γ-treated primary MC/MG cultures produced significantly lower nitrite in response to LPS challenge than control MC/MG. IFN-γ-treated EAE mice had a significantly higher frequency of CX3CR1high MC/MG and expressed lower levels of program death ligand 1 (PD-L1) than PBS-treated mice. Most CX3CR1highPD-L1lowCD11b+Ly6G- cells expressed MG markers (Tmem119, Sall2, and P2ry12), indicating that they represented an enriched MG subset (CX3CR1highPD-L1low MG). Amelioration of clinical symptoms and induction of CX3CR1highPD-L1low MG by IFN-γ were dependent on STAT-1. RNA-seq analyses revealed that in vivo treatment with IFN-γ promoted the induction of homeostatic CX3CR1highPD-L1low MG, upregulating the expression of genes associated with tolerogenic and anti-inflammatory roles and down-regulating pro-inflammatory genes. These analyses highlight the master role that IFN-γ plays in regulating microglial activity and provide new insights into the cellular and molecular mechanisms involved in the therapeutic activity of IFN-γ in EAE.
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Affiliation(s)
- Juan E. Tichauer
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Gabriel Arellano
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
- Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Eric Acuña
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Luis F. González
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Nirmal R. Kannaiyan
- Molecular Neurobiology, Department of Psychiatry & Psychotherapy, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Paola Murgas
- Center for Integrative Biology, Faculty of Science, Universidad Mayor, Santiago, Chile
| | | | - Jorge Ibañez-Vega
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Paula I. Burgos
- Department of Clinical Immunology and Rheumatology , School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Eileah Loda
- Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Stephen D. Miller
- Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Moritz J. Rossner
- Molecular Neurobiology, Department of Psychiatry & Psychotherapy, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Peter J. Gebicke-Haerter
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
- Central Institute of Mental Health, Faculty of Medicine, University of Heidelberg, Mannheim, Germany
| | - Rodrigo Naves
- Program of Immunology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile
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