Biology subjects

Rychkov, D.

Publications and source records attributed to Rychkov, D..

6 recordsLinked to original sources

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2022

Perturbations in podocyte transcriptome and biological pathways induced by FSGS associated circulating factors.

Focal segmental glomerulosclerosis (FSGS) is frequently associated with heavy proteinuria and progressive renal failure requiring dialysis or kidney transplantation. However, primary FSGS also has 40-80% risk of recurrence of disease in the transplanted kidney (rFSGS). Multiple circulating factors have been proposed to contribute to the pathogenesis of primary and rFSGS. However, neither the factors nor the downstream effectors specific to individual factors have been identified. 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. The proposed circulating factors include soluble urokinase-type plasminogen activator receptor (suPAR) and patient derived CD40 autoantibody (CD40autoAb) in the development and recurrence of FSGS. In a human in vitro model, using two novel human antibodies-anti uPAR (2G10) and anti CD40 antibody, we show that the podocyte injury caused by sera from FSGS patients is at least in part mediated by CD40 and suPAR. Additionally, we employ gene expression studies to compare the molecules and pathways activated in response to CD40 autoantibody from rFSGS patients (rFSGS/CD40autoAb) and suPAR, and delineate the unique pathways associated with FSGS injury and transcriptional podocyte alterations with targeted blockade of suPAR and CD40 pathways. Clinical ImpactFocal Segmental Glomerulosclerosis remains a disease without specific therapy for primary disease and high rate of recurrence after kidney transplantation. Circulating factors are implicated in the pathogenesis of FSGS but targeting them for therapy has remained elusive. We propose two potential therapeutic molecules for rFSGS treatment-a human anti-uPAR antibody (2G10) and a humanized anti-CD40 blocking antibody (Bristol Meyer Squibb, 986090) that reverse podocyte injury associated with FSGS in cultured podocytes and can be further tested in pre-clinical and clinical models. Furthermore, we use microarray profiling to identify transcriptional pathways specific to podocyte injury from patient-derived CD40 autoantibodies (rFSGS/CD40autoAb) and suPAR and selective blockade of these pathways to abrogate podocyte injury.

immunology↗

bioRxiv2022

Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses

Systems biology approaches have been used to define molecular signatures and mechanisms of immunity to vaccination. However, most such studies have been done with single vaccines, and comparative analysis of the response to different vaccines is lacking. We integrated temporal transcriptional data of over 3,000 samples, obtained from 820 healthy adults across 28 studies of 13 different vaccines and analyzed vaccination-induced signatures associated with the antibody response. Most vaccines induced similar kinetics of shared transcriptional signatures, including signatures of innate immunity occurring 1-3 days post-vaccination, as well as the canonical plasmablast and cell cycle signatures appearing 7 days post-vaccination. However, the yellow fever vaccine YF-17D uniquely induced an early transient signature of T and B cell activation at Day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at Days 7 and 14-21, respectively. Thus, despite the shared transcriptional response to most vaccines, at any given time point there was no evidence for a "universal signature" that could be used to predict the antibody response to all vaccines. However, accounting for the asynchronous nature of responses led to the identification of a time-adjusted signature that improved prediction antibody of responses across vaccines. These results provide a transcriptional atlas of the human immune response to vaccination and define a common, time-adjusted signature of antibody responses to vaccination.

immunology↗

bioRxiv2021

An innate immune activation state prior to vaccination predicts responsiveness to multiple vaccines

Many factors determine whether an individual responding to vaccination will generate an immune response that can lead to protection. Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccines. 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 a wide variety of vaccines. We leveraged the "Immune Signatures Data Resource" created by the NIH Human Immunology Project Consortium (HIPC) to integrate data from 28 studies involving 13 different vaccines and associate the blood transcriptional status of 820 healthy young adults with their responses. An unsupervised analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination states, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations downstream of NF{kappa}B and IRF7. Innate and adaptive immune cell subset-specific genes were also associated with the three pre-vaccination states. Importantly, individuals whose pre-vaccination state was enriched in pro-inflammatory response genes known to be downstream of NF{kappa}B tended to have higher serum antibody responses one month after vaccination. A supervised analysis of the same data resulted in a single classifier, also enriched for NF{kappa}B regulated genes, that predicted the antibody response across most of the vaccines. Projection into single-cell RNA-sequencing data suggested that this pre-vaccination state was attributable to the signature of activation of non-classical monocytes and myeloid dendritic cells. Transcriptional signatures of acute responses to bacterial and not viral infections were enriched in the high pro-inflammatory pre-vaccination state and also included NF{kappa}B regulated genes. The pro-inflammatory pre-vaccination state was highly reminiscent of the innate activation state triggered by TLR 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. They also define a transcriptional signature NF{kappa}B activation at baseline, that is associated with a greater magnitude of antibody response to multiple vaccines, and suggest that modulation of the innate immune system by next-generation adjuvants targeting NF{kappa}B before vaccine administration may improve vaccine responsiveness.

systems biology↗

bioRxiv2020

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

Background/PurposeThere is an urgent need to identify effective biomarkers for early diagnosis of rheumatoid arthritis (RA) and to accurately monitor disease activity. Here we define an RA meta-profile using publicly available cross-tissue gene expression data and apply machine learning to identify putative biomarkers, which we further validate on independent datasets. MethodsWe carried out a comprehensive search for publicly available microarray gene expression data in the NCBI Gene Expression Omnibus database for whole blood and synovial tissues from RA patients and healthy controls. The raw data from 13 synovium datasets with 284 samples and 14 blood datasets with 1,885 samples were downloaded and processed. The datasets for each tissue were merged, batch corrected and split into training and test sets. We then developed and applied a robust feature selection pipeline to identify genes dysregulated in both tissues and highly associated with RA. From the training data, we identified a set of overlapping differentially expressed genes following the condition of co-directionality. The classification performance of each gene in the resulting set was evaluated on the testing sets using the area under a receiver operating characteristic curve. Five independent datasets were used to validate and threshold the feature selected (FS) genes. Finally, we defined the RA Score, composed of the geometric mean of the selected RA Score Panel genes, and demonstrated its clinical utility. ResultsThis feature selection pipeline resulted in a set of 25 upregulated and 28 downregulated genes. To assess the robustness of these FS genes, we trained a Random Forest machine learning model with this set of 53 genes and then with the set of 33 overlapping genes differentially expressed in both tissues and tested on the validation cohorts. The model with FS genes outperformed the model with common DE genes with AUC 0.89 {+/-} 0.04 vs 0.87 {+/-} 0.04. The FS genes were further validated on the 5 independent datasets resulting in 10 upregulated genes, TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, and ATP6V0E1, which are involved in innate immune system pathways, including neutrophil degranulation and apoptosis. There were also three downregulated genes, HSP90AB1, NCL, and CIRBP, that are involved in metabolic processes and T-cell receptor regulation of apoptosis. To investigate the clinical utility of the 13 validated genes, the RA Score was developed and found to be highly correlated with the disease activity score based on the 28 examined joints (DAS28) (r = 0.33 {+/-} 0.03, p = 7e-9) and able to distinguish osteoarthritis (OA) from RA samples (OR 0.57, 95% CI [0.34, 0.80], p = 8e-10). Moreover, the RA Score was not significantly different for rheumatoid factor (RF) positive and RF-negative RA sub-phenotypes (p = 0.9) and also distinguished polyarticular juvenile idiopathic arthritis (polyJIA) from healthy individuals in 10 independent pediatric cohorts (OR 1.15, 95% CI [1.01, 1.3], p = 2e-4) suggesting the generalizability of this score in clinical applications. The RA Score was also able to monitor the treatment effect among RA patients (t-test of treated vs untreated, p = 2e-4). Finally, we performed immunoblotting analysis of 6 proteins in unstimulated PBMC lysates from an independent cohort of 8 newly diagnosed RA patients and 7 healthy controls, where two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90, were validated and the S100A8 protein showed near significant up-regulation. ConclusionThe RA Score, consisting of 13 putative biomarkers identified through a robust feature selection procedure on public data and validated using multiple independent data sets, could be useful in the diagnosis and treatment monitoring of RA.

bioinformatics↗

bioRxiv2019

Precision Sub-phenotyping of Histologically Stable Kidney Transplants by a Composite Molecular and Cellular Instability Score

Acute Rejection (AR) is the main cause of the graft dysfunction and premature graft loss, and diagnosis of rejection before advanced histological injury is crucial to salvage graft function. However, recent molecular studies have highlighted the unrecognized burden of sub-clinical graft rejection when graft function is preserved, and a dichotomy exists, of a histologically normal biopsy with molecular signatures of AR. Conversely, significant variation also exists in the definition of a stable allograft, defined as a transplant with absence of clinical AR, with absence of histological inflammation, though published studies have highlighted that some of these stable samples will not have stable immune quiescence, as they may be molecularly similar to AR. Thus, refining the definition of a stable allograft as one that is clinically, histologically and molecularly quiescent is critical, as the inclusion of stable allografts in mechanistic and clinical studies are vital to provide a normal, non-injured comparative group for all interrogative studies on understanding allograft injury. With this goal in mind, we analyzed publicly available transcriptional data across 4,845 human kidney tissue samples from 38 Gene Expression Omnibus (GEO) datasets, inclusive of 510 allograft biopsy samples with AR, 1,154 renal allograft biopsies classified in each dataset as histological stable (hSTA), and 609 normal kidney (donor) samples. By applying a machine learning model, a substantive number of hSTA samples were found to be molecularly similar to AR (mAR); these have been reclassified in this study as clinical and histological stable samples with transcriptional signatures overlapping with AR (hSTA/mAR), with the predominant expression of a subset of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3). To understand the cellular sources of these molecular signals, we utilized xCell, a cell type enrichment tool and interrogated 64 specific cell types to identify 5 (CD4+ Tcm, CD4+ Tem, CD8+ Tem, NK cells, and Th1 cells) that were also highly predictive for classification of the AR phenotype in these studies. A combined gene and cell-type specific InstaScore (AUC 0.99) was developed using gene and cell subtype data to re-phenotype all hSTA allografts. This clearly defined two disparate hSTA biopsies: those that are both histologically and molecularly quiescent (hSTA/mSTA) or those that are histologically quiescent but molecularly similar to AR (hSTA/mAR). The clinical utility of the Instability Score was subsequently assessed by independent validation on a serial set of post-transplant hSTA biopsies, where strong significant correlation was observed between the score on 6 month post-transplant hSTA graft biopsies, where hSTA/mAR samples had a significant change in graft function and graft loss at 5 year follow-up. In conclusion, our computational approach of precision sub-phenotyping of hSTA allografts by the InstaScore identifies discrepancies in the current recognition of a stable allograft by histology alone. Precision molecular sub-typing of the hSTA allograft into the hSTA/mSTA group is an important deliverable for selection of "true" STA samples for mechanistic studies, and into the hSTA/mAR group, for accurate prediction of subsequent patient clinical outcomes, and real time treatment stratification for hSTA/mAR allografts to positively impact long-term graft survival.

bioinformatics↗