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Rashmi P, Sigdel TK, Rychkov D, Damm I, Da Silva AA, Vincenti F, Lourenco AL, Craik CS, Reiser J, Sarwal MM. Perturbations in podocyte transcriptome and biological pathways induced by FSGS associated circulating factors. Ann Transl Med 2023; 11:315. [PMID: 37404982 PMCID: PMC10316099 DOI: 10.21037/atm-22-3670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/03/2022] [Indexed: 07/06/2023]
Abstract
Background Focal segmental glomerulosclerosis (FSGS) is frequently associated with heavy proteinuria and progressive renal failure requiring dialysis or kidney transplantation. However, primary FSGS also has a ~40% risk of recurrence of disease in the transplanted kidney (rFSGS). Multiple circulating factors have been identified to contribute to the pathogenesis of primary and rFSGS including soluble urokinase-type plasminogen activator receptor (suPAR) and patient-derived CD40 autoantibody (CD40autoAb). However, the downstream effector pathways specific to individual factors require further study. The tumor necrosis factor, TNF pathway activation by one or more circulating factors present in the sera of patients with FSGS has been supported by multiple studies. Methods A human in vitro model was used to study podocyte injury measured as the loss of actin stress fibers. Anti-CD40 autoantibody was isolated from FSGS patients (recurrent and non-recurrent) and control patients with ESRD due to non-FSGS related causes. Two novel human antibodies-anti-uPAR (2G10) and anti-CD40 antibody (Bristol Meyer Squibb, 986090) were tested for their ability to rescue podocyte injury. Podocytes treated with patient derived antibody were transcriptionally profiled using whole human genome microarray. Results Here we show that podocyte injury caused by sera from FSGS patients is mediated by CD40 and suPAR and can be blocked by human anti-uPAR and anti-CD40 antibodies. Transcriptomic studies to compare the molecules and pathways activated in response to CD40 autoantibody from rFSGS patients (rFSGS/CD40autoAb) and suPAR, identified unique inflammatory pathways associated with FSGS injury. Conclusions We identified several novel and previously described genes associated with FSGS progression. Targeted blockade of suPAR and CD40 pathways with novel human antibodies showed inhibition of podocyte injury in FSGS.
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Affiliation(s)
- Priyanka Rashmi
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Tara K. Sigdel
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Dmitry Rychkov
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Izabella Damm
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Andrea Alice Da Silva
- Department of Immunology, Laboratory of Autoimmunity and Immunoregulation, Fluminense Federal University, Niteroi, Brazil
| | - Flavio Vincenti
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Andre L. Lourenco
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Charles S. Craik
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Jochen Reiser
- Department of Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Minnie M. Sarwal
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
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Fourati S, Tomalin LE, Mulè MP, Chawla DG, Gerritsen B, Rychkov D, Henrich E, Miller HER, Hagan T, Diray-Arce J, Dunn P, Levy O, Gottardo R, Sarwal MM, Tsang JS, Suárez-Fariñas M, Pulendran B, Kleinstein SH, Sékaly RP. Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination. Nat Immunol 2022; 23:1777-1787. [PMID: 36316476 PMCID: PMC9747610 DOI: 10.1038/s41590-022-01329-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/05/2022]
Abstract
Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccination. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across 13 different vaccines. Analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination endotypes, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations. Importantly, individuals whose pre-vaccination endotype was enriched in pro-inflammatory response genes known to be downstream of nuclear factor-kappa B showed significantly higher serum antibody responses 1 month after vaccination. This pro-inflammatory pre-vaccination endotype showed gene expression characteristic of the innate activation state triggered by Toll-like receptor ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination.
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Affiliation(s)
- Slim Fourati
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Lewis E Tomalin
- Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew P Mulè
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID and Center for Human Immunology (CHI), NIH, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program, Cambridge University, Cambridge, UK
| | | | | | - Dmitry Rychkov
- Division of Transplant Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Evan Henrich
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Thomas Hagan
- Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Joann Diray-Arce
- Precision Vaccines Program, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Patrick Dunn
- ImmPort Curation Team, NG Health Solutions, Rockville, MD, USA
| | - Ofer Levy
- Precision Vaccines Program, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Raphael Gottardo
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Biomedical Data Science Center, University of Lausanne and Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Minnie M Sarwal
- Division of Transplant Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - John S Tsang
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID and Center for Human Immunology (CHI), NIH, Bethesda, MD, USA
| | - Mayte Suárez-Fariñas
- Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bali Pulendran
- Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Rafick-Pierre Sékaly
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
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Patterson SL, Sun S, Rychkov D, Katz P, Tsitsiklis A, Nakamura MC, Serpa PH, Langelier CR, Sirota M. Physical Activity Associates With Lower Systemic Inflammatory Gene Expression in Rheumatoid Arthritis. J Rheumatol 2022; 49:1320-1327. [PMID: 35777820 PMCID: PMC9722583 DOI: 10.3899/jrheum.220050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVE While general population studies have shown inverse associations between physical activity and common inflammatory biomarkers, the effects of physical activity on inflammatory gene expression and signaling pathways in rheumatoid arthritis (RA) remain unknown. We aimed to determine whether physical activity independently associates with expression of inflammatory genes among people with RA. METHODS This was a prospective observational study of adults with RA. Physical activity was measured by quantitative actigraphy over 7 consecutive days, and peripheral blood collected during the same time period was used for RNA sequencing followed by differential gene expression, pathway, and network analyses. RESULTS Actigraphy and RNA sequencing data were evaluated in 35 patients. The cohort had a mean age of 56 (SD 12) years, and was 91% female, 31% White, 9% Black, 9% Asian, and 40% Hispanic. We found 767 genes differentially expressed (adjusted P < 0.1) between patients in the greatest vs lowest physical activity tertiles, after adjusting for sex, age, race, and ethnicity. The most active patients exhibited dose-dependent downregulation of several immune signaling pathways implicated in RA pathogenesis. These included CD40, STAT3, TREM-1, interleukin (IL)-17A, IL-8, Toll-like receptor, and interferon (IFN) signaling pathways. Upstream cytokine activation state analysis predicted reduced activation of tumor necrosis factor-α and IFN in the most active group. In sensitivity analyses, we adjusted for RA disease activity and physical function and found consistent results. CONCLUSION Patients with RA who were more physically active had lower expression of immune signaling pathways implicated in RA pathogenesis, even after adjusting for disease activity, suggesting that physical activity may confer a protective effect in RA.
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Affiliation(s)
- Sarah L Patterson
- S.L. Patterson, MS, P. Katz, PhD, Division of Rheumatology, University of California;
| | - Shenghuan Sun
- S. Sun, BS, D. Rychkov, PhD, M. Sirota, PhD, Department of Pediatrics, University of California
| | - Dmitry Rychkov
- S. Sun, BS, D. Rychkov, PhD, M. Sirota, PhD, Department of Pediatrics, University of California
| | - Patricia Katz
- S.L. Patterson, MS, P. Katz, PhD, Division of Rheumatology, University of California
| | - Alexandra Tsitsiklis
- A. Tsitsiklis, PhD, P. Hayakawa Serpa, BA, Division of Infectious Diseases, University of California
| | - Mary C Nakamura
- M.C. Nakamura, MD, PhD, Division of Rheumatology, University of California, and Department of Veterans Affairs Medical Center
| | - Paula Hayakawa Serpa
- A. Tsitsiklis, PhD, P. Hayakawa Serpa, BA, Division of Infectious Diseases, University of California
| | - Charles R Langelier
- C.R. Langelier, MD, PhD, Division of Infectious Diseases, University of California, and Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Marina Sirota
- S. Sun, BS, D. Rychkov, PhD, M. Sirota, PhD, Department of Pediatrics, University of California
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Diray-Arce J, Miller HER, Henrich E, Gerritsen B, Mulè MP, Fourati S, Gygi J, Hagan T, Tomalin L, Rychkov D, Kazmin D, Chawla DG, Meng H, Dunn P, Campbell J, Sarwal M, Tsang JS, Levy O, Pulendran B, Sekaly R, Floratos A, Gottardo R, Kleinstein SH, Suárez-Fariñas M. The Immune Signatures data resource, a compendium of systems vaccinology datasets. Sci Data 2022; 9:635. [PMID: 36266291 PMCID: PMC9584267 DOI: 10.1038/s41597-022-01714-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/22/2022] [Indexed: 01/04/2023] Open
Abstract
Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.
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Affiliation(s)
- Joann Diray-Arce
- Precision Vaccines Program, Boston Children's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Helen E R Miller
- Harvard Medical School, Boston, MA, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Evan Henrich
- Harvard Medical School, Boston, MA, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Matthew P Mulè
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID NIH Center for Human Immunology, NIH, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program, Department of Medicine, Cambridge University, Atlanta, GA, USA
| | - Slim Fourati
- Emory University School of Medicine, Atlanta, GA, USA
| | - Jeremy Gygi
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Thomas Hagan
- Stanford University School of Medicine, Stanford University, Stanford, CA, USA
- Division of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lewis Tomalin
- Department of Population Health Sciences and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dmitry Rychkov
- University of California, San Francisco, San Francisco, CA, USA
| | - Dmitri Kazmin
- The Jackson Laboratory for Genomic Medicine, Farmington CT, Rockville, MD, USA
| | - Daniel G Chawla
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | - Patrick Dunn
- ImmPort Curation Team, NG Health Solutions, Rockville, MD, USA
| | - John Campbell
- ImmPort Curation Team, NG Health Solutions, Rockville, MD, USA
| | - Minnie Sarwal
- University of California, San Francisco, San Francisco, CA, USA
| | - John S Tsang
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID NIH Center for Human Immunology, NIH, Bethesda, MD, USA
| | - Ofer Levy
- Precision Vaccines Program, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - Bali Pulendran
- Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Rafick Sekaly
- Emory University School of Medicine, Atlanta, GA, USA
| | - Aris Floratos
- Columbia University Medical Center, New York, NY, USA
| | - Raphael Gottardo
- Harvard Medical School, Boston, MA, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- University of Lausanne and University Hospital of Lausanne, Lausanne, Switzerland
| | | | - Mayte Suárez-Fariñas
- Department of Population Health Sciences and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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5
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Rychkov D, Neely J, Oskotsky T, Yu S, Perlmutter N, Nititham J, Carvidi A, Krueger M, Gross A, Criswell LA, Ashouri JF, Sirota M. Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis. Front Immunol 2021; 12:638066. [PMID: 34177888 PMCID: PMC8223752 DOI: 10.3389/fimmu.2021.638066] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/17/2021] [Indexed: 01/20/2023] Open
Abstract
There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.
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Affiliation(s)
- Dmitry Rychkov
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
- Department of Surgery, University of California San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Jessica Neely
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
| | - Steven Yu
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, United States
| | - Noah Perlmutter
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Joanne Nititham
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Alexander Carvidi
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Melissa Krueger
- Department of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Andrew Gross
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Lindsey A. Criswell
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Institute for Human Genetics (IHG), University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Judith F. Ashouri
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
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6
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Zarinsefat A, Hartoularos G, Rychkov D, Rashmi P, Chandran S, Vincenti F, Yee CJ, Sarwal MM. Single-Cell RNA Sequencing of Tocilizumab-Treated Peripheral Blood Mononuclear Cells as an in vitro Model of Inflammation. Front Genet 2021; 11:610682. [PMID: 33469465 PMCID: PMC7813999 DOI: 10.3389/fgene.2020.610682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 12/02/2020] [Indexed: 12/27/2022] Open
Abstract
COVID-19 has posed a significant threat to global health. Early data has revealed that IL-6, a key regulatory cytokine, plays an important role in the cytokine storm of COVID-19. Multiple trials are therefore looking at the effects of Tocilizumab, an IL-6 receptor antibody that inhibits IL-6 activity, on treatment of COVID-19, with promising findings. As part of a clinical trial looking at the effects of Tocilizumab treatment on kidney transplant recipients with subclinical rejection, we performed single-cell RNA sequencing of comparing stimulated PBMCs before and after Tocilizumab treatment. We leveraged this data to create an in vitro cytokine storm model, to better understand the effects of Tocilizumab in the presence of inflammation. Tocilizumab-treated cells had reduced expression of inflammatory-mediated genes and biologic pathways, particularly amongst monocytes. These results support the hypothesis that Tocilizumab may hinder the cytokine storm of COVID-19, through a demonstration of biologic impact at the single-cell level.
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Affiliation(s)
- Arya Zarinsefat
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - George Hartoularos
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Dmitry Rychkov
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Priyanka Rashmi
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Sindhu Chandran
- Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Flavio Vincenti
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Chun J. Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Minnie M. Sarwal
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
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7
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Abstract
IMPORTANCE Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury. OBJECTIVE To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data. DESIGN, SETTING, AND PARTICIPANTS In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction. MAIN OUTCOMES AND MEASURES The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue. RESULTS Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4+ T-cell central memory [Tcm], CD4+ T-cell effector memory [Tem], CD8+ Tem, natural killer [NK] cells, and Type 1 T helper [TH1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m2 in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR. CONCLUSIONS AND RELEVANCE The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.
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Affiliation(s)
- Dmitry Rychkov
- Division of Multi-Organ Transplantation, Department of Surgery, University of California, San Francisco
- Bakar Computational Health Sciences Institute, University of California, San Francisco
| | - Swastika Sur
- Division of Multi-Organ Transplantation, Department of Surgery, University of California, San Francisco
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco
- Department of Pediatrics, University of California, San Francisco
| | - Minnie M. Sarwal
- Division of Multi-Organ Transplantation, Department of Surgery, University of California, San Francisco
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8
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Neely J, Rychkov D, Paranjpe M, Waterfield M, Kim S, Sirota M. Gene Expression Meta-Analysis Reveals Concordance in Gene Activation, Pathway, and Cell-Type Enrichment in Dermatomyositis Target Tissues. ACR Open Rheumatol 2019; 1:657-666. [PMID: 31872188 PMCID: PMC6917332 DOI: 10.1002/acr2.11081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 08/23/2019] [Indexed: 01/21/2023] Open
Abstract
Objective We conducted a comprehensive gene expression meta‐analysis in dermatomyositis (DM) muscle and skin tissues to identify shared disease‐relevant genes and pathways across tissues. Methods Six publicly available data sets from DM muscle and two from skin were identified. Meta‐analysis was performed by first processing data sets individually then cross‐study normalization and merging creating tissue‐specific gene expression matrices for subsequent analysis. Complementary single‐gene and network analyses using Significance Analysis of Microarrays (SAM) and Weighted Gene Co‐expression Network Analysis (WGCNA) were conducted to identify genes significantly associated with DM. Cell‐type enrichment was performed using xCell. Results There were 544 differentially expressed genes (FC ≥ 1.3, q < 0.05) in muscle and 300 in skin. There were 94 shared upregulated genes across tissues enriched in type I and II interferon (IFN) signaling and major histocompatibility complex (MHC) class I antigen‐processing pathways. In a network analysis, we identified eight significant gene modules in muscle and seven in skin. The most highly correlated modules were enriched in pathways consistent with the single‐gene analysis. Additional pathways uncovered by WGCNA included T‐cell activation and T‐cell receptor signaling. In the cell‐type enrichment analysis, both tissues were highly enriched in activated dendritic cells and M1 macrophages. Conclusion There is striking similarity in gene expression across DM target tissues with enrichment of type I and II IFN pathways, MHC class I antigen‐processing, T‐cell activation, and antigen‐presenting cells. These results suggest IFN‐γ may contribute to the global IFN signature in DM, and altered auto‐antigen presentation through the class I MHC pathway may be important in disease pathogenesis.
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Affiliation(s)
- Jessica Neely
- Department of Pediatrics, University of California, San Francisco
| | - Dmitry Rychkov
- Department of Pediatrics, University of California, San Francisco
| | - Manish Paranjpe
- Department of Pediatrics, University of California, San Francisco
| | | | - Susan Kim
- Department of Pediatrics, University of California, San Francisco
| | - Marina Sirota
- Department of Pediatrics, University of California, San Francisco
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