Biology subjects

Raychaudhuri, S.

Publications and source records attributed to Raychaudhuri, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2020

Improved Supra-Organization Is A Conformational And Functional Adaptation Of Respiratory Complexes

Multiple surveillance mechanisms accelerate proteasome mediated degradation of misfolded proteins to prevent protein aggregation inside and outside mitochondria. But how cells safeguard mitochondrial function despite increased protein aggregation during proteasome inactivation? Here, using two-dimensional complexome profiling, we extensively characterize the dynamic states of respiratory complexes (RCs) in proteasome-inhibited cells. We report that RC-subunits are increasingly integrated into supra-organizations to optimize catalytic activity simultaneous to their aggregation inside mitochondria. Complex-II (CII) and CV are incorporated into oligomers. CI, CIII, and CIV subcomplexes are associated into holocomplexes followed by integration into supercomplexes. Time-course experiments reveal that the core (CI+CIII2) stoichiometry of supercomplex (I+III2+IV) is preserved during early-stress while CIV composition varies. Simultaneously, increased CI-activity suggests conformational optimization of supercomplexes for better function. Re-establishment of steady-state stoichiometry and relative increase in supercomplex-quantity consolidates functional adaptation during prolonged proteasome-inhibition. Together, we name this pre-emptive adaptive mechanism as improved Supra-organization of Respiratory Complexes (iSRC). We find that iSRC is active in multiple protein-unfolding stresses, in multiple cell-types that differ in proteostatic and metabolic demands, and reversible upon stress-withdrawal.

biochemistry↗

bioRxiv2019

Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic

Slow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patients first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.\n\nWe developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records: - 1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623);\n- 245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604);\n- 243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604).\nThe calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Probs discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinicians initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.\n\nIn conclusion, by converting genotypes into an interpretable probability value for five different inflammatory arthritides, we can better discriminate and diagnose rheumatic diseases. Genotypes available prior to a clinical visit could be considered part of patients medical history and potentially used to improve precision and diagnostic efficiency in clinical practice.

genetics↗

bioRxiv2019

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Many diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82x (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

genetics↗

bioRxiv2019

Presto scales Wilcoxon and auROC analyses to millions of observations

SummaryThe related Wilcoxon rank sum test and area under the receiver operator curve are ubiquitous in high dimensional biological data analysis. Current implementations do not scale readily to the increasingly large datasets generated by novel high-throughput technologies, such as single cell RNAseq. We introduce a simple and scalable implementation of both analyses, available through the R package Presto. Presto scales to big datasets, with functions optimized for both dense and sparse matrices. On a sparse dataset of 1 million observations, 10 groups, and 1,000 features, Presto performed both rank-sum and auROC analyses in only 17 seconds, compared to 6.4 hours with base R functions. Presto also includes functions to seamlessly integrate with the Seurat single cell analysis pipeline and the Bioconductor SingleCellExperiment class. Presto enables the use of robust classical analyses on big data with a simple interface and optimized implementation.\n\nAvailability and ImplementationPresto is available as an R package at https://github.com/immunogenomics/presto.\n\nContactsoumya@broadinstitute.org\n\nSupplementary InformationVignettes are available with the Presto package.

bioinformatics↗

bioRxiv2019

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

Understanding how genetic regulatory variation affects gene expression in different T cell states is essential to deciphering autoimmunity. We conducted a high-resolution RNA-seq time course analysis of stimulated memory CD4+ T cells from 24 healthy individuals. We identified 186 genes with dynamic allele-specific expression, where the balance of alleles changes over time. These genes were four fold enriched in autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes, particularly for a major autoimmune risk gene, HLA-DQB1. Each HLA-DQB1 allele had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a single nucleotide variant at the promoter is causal for T cell-specific control of HLA-DQB1 expression. Our study in CD4+ T cells shows that genetic variation in cis regulatory elements may affect gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

genomics↗

bioRxiv2019

CUX1 and IκBζ mediate the synergistic inflammatory response to TNF and IL-17A in stromal fibroblasts

The role of stromal fibroblasts in chronic inflammation is unfolding. In rheumatoid arthritis (RA), leukocyte-derived cytokines tumor necrosis factor (TNF) and IL-17A work together, activating fibroblasts to become a dominant source of the hallmark cytokine IL-6. However, IL-17A alone has minimal effect on fibroblasts. To identify key mediators of the synergistic response to TNF and IL-17A in human synovial fibroblasts, we performed time series, dose response, and gene silencing transcriptomics experiments. Here we show that in combination with TNF, IL-17A selectively induces a specific set of genes mediated by factors including CUX1 and I{kappa}B{zeta}. In the promoters of CXCL1, CXCL2, and CXCL3, we found a putative CUX1-NF-{kappa}B binding motif not found elsewhere in the genome. CUX1 and NF-{kappa}B p65 mediate transcription of these genes independent of LIFR, STAT3, STAT4, and ELF3. Transcription of NFKBIZ, encoding the atypical I{kappa}B factor I{kappa}B{zeta}, is IL-17A dose-dependent, and I{kappa}B{zeta} only mediates the transcriptional response to TNF and IL-17A, but not to TNF alone. In fibroblasts, IL-17A response depends on CUX1 and I{kappa}B{zeta} to engage the NF-{kappa}B complex to produce chemoattractants for neutrophil and monocyte recruitment.

immunology↗

bioRxiv2019

A positively selected, common, missense variant in FBN1 confers a 2.2 centimeter reduction of height in the Peruvian population

Peruvians are among the shortest people in the world. To understand the genetic basis of short stature in Peru, we examined an ethnically diverse group of Peruvians and identified a novel, population-specific, missense variant in FBN1 (E1297G) that is significantly associated with lower height in the Peruvian population. Each copy of the minor allele (frequency = 4.7%) reduces height by 2.2 cm (4.4 cm in homozygous individuals). This is the largest effect size known for a common height-associated variant. This variant shows strong evidence of positive selection within the Peruvian population and is significantly more frequent in Native American populations from coastal regions of Peru compared to populations from the Andes or the Amazon, suggesting that short stature in Peruvians is the result of adaptation to the coastal environment. One Sentence SummaryA mutation found in Peruvians has the largest known effect on height for a common variant. This variant is specific to Native American ancestry.

genomics↗

bioRxiv2019

HBEGF+ macrophages identified in rheumatoid arthritis promote joint tissue invasiveness and are reshaped differentially by medications

Macrophages tailor their function to the signals found in tissue microenvironments, taking on a wide spectrum of phenotypes. In human tissues, a detailed understanding of macrophage phenotypes is limited. Using single-cell RNA-sequencing, we define distinct macrophage subsets in the joints of patients with the autoimmune disease rheumatoid arthritis (RA), which affects ~1% of the population. The subset we refer to as HBEGF+ inflammatory macrophages is enriched in RA tissues and shaped by resident fibroblasts and the cytokine TNF. These macrophages promote fibroblast invasiveness in an EGF receptor dependent manner, indicating that inflammatory intercellular crosstalk reshapes both cell types and contributes to fibroblast-mediated joint destruction. In an ex vivo tissue assay, the HBEGF+ inflammatory macrophage is targeted by several anti-inflammatory RA medications, however, COX inhibition redirects it towards a different inflammatory phenotype that is also expected to perpetuate pathology. These data highlight advances in understanding the pathophysiology and drug mechanisms in chronic inflammatory disorders can be achieved by focusing on macrophage phenotypes in the context of complex interactions in human tissues. One Sentence SummaryA newly identified human macrophage phenotype from patients with the autoimmune condition RA is found to promote joint tissue invasiveness and demonstrates variable sensitivities to anti-inflammatory medications used to treat the disease.

immunology↗

bioRxiv2018

Estimating heritability of complex traits in admixed populations with summary statistics

The increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary among diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability [Formula] and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates [Formula] by 10% - 60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate [Formula] and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of [Formula] and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of [Formula]. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size{tau} * is 0.16 {+/-} 0.04, 0.28 {+/-} 0.11 and 0.18 {+/-} 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations. Author summaryAdmixed populations such as African Americans and Hispanic Americans bear a disproportionately high burden of disease but remain underrepresented in current genetic studies. It is important to extend current methodological advancements for understanding the genetic basis of complex traits in homogeneous populations to individuals with admixed genetic backgrounds. Here, we develop a computationally efficient method to answer two specific questions. First, does genetic variation contribute to the same amount of phenotypic variation (heritability) across diverse populations? Second, are the genetic mechanisms shared among different populations? To answer these questions, we use our novel method to conduct the first comprehensive heritability-based analysis of a large number of admixed individuals. We show that there is a high degree of concordance in total heritability and tissue-specific enrichment between different ancestral groups. However, traits such as age at menarche show a noticeable differences among populations. Our work provides a powerful way to analyze genetic data in admixed populations and may contribute to the applicability of genomic medicine to admixed population groups.

genetics↗

bioRxiv2018

Benchmarker: an unbiased, association-data-driven strategy to evaluate gene prioritization algorithms

Genome-wide association studies (GWAS) are valuable for understanding human biology, but associated loci typically contain multiple associated variants and genes. Thus, algorithms that prioritize likely causal genes and variants for a given phenotype can provide biological interpretations of association data. However, a critical, currently missing capability is to objectively compare performance of such algorithms. Typical comparisons rely on \"gold standard\" genes harboring causal coding variants, but such gold standards may be biased and incomplete. To address this issue, we developed Benchmarker, an unbiased, data-driven benchmarking method that compares performance of prioritization strategies to each other (and to random chance) by leave-one-chromosome-out cross-validation with stratified linkage disequilibrium (LD) score regression. We first applied Benchmarker to twenty well-powered GWAS and compared gene prioritization based on strategies employing three different data sources, including annotated gene sets and gene expression. No individual strategy clearly outperformed the others, but genes prioritized by multiple strategies had higher per-SNP heritability than those prioritized by one strategy only. We also compared two gene prioritization methods, DEPICT and MAGMA; genes prioritized by both methods strongly outperformed genes prioritized by only one. Our results suggest that combining data sources and algorithms should pinpoint higher quality genes for follow-up. Benchmarker provides an unbiased approach to evaluate any method that provides genome-wide prioritization of gene sets, genes, or variants, and can determine the best such method for any particular GWAS. Our method addresses an important unmet need for rigorous tool assessment and can assist in mapping genetic associations to causal function.

genomics↗

bioRxiv2018

Fast, sensitive, and flexible integration of single cell data with Harmony

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of[~] 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.

bioinformatics↗

bioRxiv2018

Genes with high network connectivity are enriched for disease heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. First, we constructed annotations from 18,119 biological pathways, including 100kb windows around each gene. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and on annotations from the baseline-LD model, a stringent step that greatly reduced the number of pathways detected; most of the significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity using closeness centrality, a measure of how close a gene is to other genes in the network. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.

genetics↗

bioRxiv2018

Progression of recent Mycobacterium tuberculosis exposure to active tuberculosis is a highly heritable complex trait driven by 3q23 in Peruvians

Among 1.8 billion people worldwide infected with Mycobacterium tuberculosis, 5-15% are expected to develop active tuberculosis (TB). Approximately half of these will progress to active TB within the first 18 months after infection, presumably because they fail to mount the initial immune response that contains the local bacterial spread. The other half will reactivate their latent infection later in life, likely triggered by a loss of immune competence due to factors such as HIV-associated immunosuppression or ageing. This natural history suggests that undiscovered host genetic factors may control early progression to active TB. Here, we report results from a large genome-wide genetic study of early TB progression. We genotyped a total of 4,002 active TB cases and their household contacts in Peru and quantified genetic heritability [Formula] of early TB progression to be 21.2% under the liability scale. Compared to the reported [Formula] of genome-wide TB susceptibility (15.5%), this result indicates early TB progression has a stronger genetic basis than population-wide TB susceptibility. We identified a novel association between early TB progression and variants located in an enhancer region on chromosome 3q23 (rs73226617, OR=1.19; P < 5x10-8). We used in silico and in vitro analyses to identify likely functional variants and target genes, highlighting new candidate mechanisms of host response in early TB progression.

genetics↗

bioRxiv2018

Insights from Comparison of the Renal and Skin Single Cell Transcriptomes in Lupus Nephritis

Lupus nephritis (LN) occurs in up to 50% of patients with systemic lupus erythematosus (SLE), and is a major contributor to mortality and morbidity. LN presents as a highly heterogeneous disease both in histopathology and response to therapy. The molecular and cellular processes leading to renal damage and to the heterogeneity of the disease are not well understood. To elucidate the processes underpinning the heterogeneity of LN, we applied singlecell RNA-sequencing (scRNA-seq) to renal biopsies from LN patients. Skin biopsies were evaluated as a source of biomarkers for monitoring kidney disease. Type-I interferon (IFN) response signatures were identified in tubular cells and keratinocytes, differentiating LN patients from healthy controls. Non-responders associated with higher IFN signatures in both tissue compartments. Moreover, non-response was also associated with a fibrotic signature in the tubular cells. Receptor-ligand interaction analysis indicated that the fibrotic process is likely mediated by FGF receptors with the initiating signal originating from infiltrating leukocytes. Differential expression analysis of tubular cells between proliferative and membranous LN pointed to several fibrosis-relevant pathways, which may offer insight into their histological differences. In summary, scRNA-seq was applied to LN to deconstruct its heterogeneity and provide novel targets for personalized approaches to therapy.

immunology↗

bioRxiv2018

Pathologically distinct fibroblast subsets drive inflammation and tissue damage in arthritis

The identification of lymphocyte subsets with non-overlapping effector functions has been pivotal to the development of targeted therapies in immune mediated inflammatory diseases (IMIDs). However it remains unclear whether fibroblast subclasses with non-overlapping functions also exist and are responsible for the wide variety of tissue driven processes observed in IMIDs such as inflammation and damage. Here we identify and describe the biology of distinct subsets of fibroblasts responsible for mediating either inflammation or tissue damage in arthritis. We show that deletion of FAP+ synovial cells suppressed both inflammation and bone erosions in murine models of resolving and persistent arthritis. Single cell transcriptional analysis identified two distinct fibroblast subsets: FAP+ THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAP+ THY1- destructive fibroblasts restricted to the synovial lining. When adoptively transferred into the joint, FAP + THY1- fibroblasts selectively mediate bone and cartilage damage with little effect on inflammation whereas transfer of FAP + THY1+ fibroblasts resulted in a more severe and persistent inflammatory arthritis, with minimal effect on bone and cartilage. Our findings describing anatomically discrete, functionally distinct fibroblast subsets with non-overlapping functions have important implications for cell based therapies aimed at modulating inflammation and tissue damage.

immunology↗

bioRxiv2018

Rheumatoid arthritis heritability is concentrated in regulatory elements with CD4+ T cell-state-specific transcription factor binding profiles

Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures at sites where specific transcription factors (TFs) are bound. To link these two identifying features, we introduce IMPACT, a genome annotation strategy which identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT predicts TF motif binding with high accuracy (average AUC 0.92, s.e. 0.03; across 8 TFs), a significant improvement (all p<6.9e-15) over intersecting motifs with open chromatin (average AUC 0.66, s.e. 0.11). Second, an IMPACT annotation trained on RNA polymerase II is more enriched for peripheral blood cis-eQTL variation (N=3,754) than sequence based annotations, such as promoters and regions around the TSS, (permutation p<1e-3, 25% average increase in enrichment). Third, integration with rheumatoid arthritis (RA) summary statistics from European (N=38,242) and East Asian (N=22,515) populations revealed that the top 5% of CD4+ Treg IMPACT regulatory elements capture 85.7% (s.e. 19.4%) of RA h2 (p<1.6e-5) and that the top 9.8% of Treg IMPACT regulatory elements, consisting of all SNPs with a non-zero annotation value, capture 97.3% (s.e. 18.2%) of RA h2 (p<7.6e-7), the most comprehensive explanation for RA h2 to date. In comparison, the average RA h2 captured by compared CD4+ T histone marks is 42.3% and by CD4+ T specifically expressed gene sets is 36.4%. Finally, integration with RA fine-mapping data (N=27,345) revealed a significant enrichment (2.87, p<8.6e-3) of putatively causal variants across 20 RA associated loci in the top 1% of CD4+ Treg IMPACT regulatory regions. Overall, we find that IMPACT generalizes well to other cell types in identifying complex trait associated regulatory elements.

genetics↗

bioRxiv2018

Defining Inflammatory Cell States in Rheumatoid Arthritis Joint Synovial Tissues by Integrating Single-cell Transcriptomics and Mass Cytometry

To define the cell populations in rheumatoid arthritis (RA) driving joint inflammation, we applied single-cell RNA-seq (scRNA-seq), mass cytometry, bulk RNA-seq, and flow cytometry to sorted T cells, B cells, monocytes, and fibroblasts from 51 synovial tissue RA and osteoarthritis (OA) patient samples. Utilizing an integrated computational strategy based on canonical correlation analysis to 5,452 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics together revealed cell states expanded in RA synovia: THY1+HLAhigh sublining fibroblasts (OR=33.8), IL1B+ pro-inflammatory monocytes (OR=7.8), CD11c+T-bet+ autoimmune-associated B cells (OR=5.7), and PD-1+Tph/Tfh (OR=3.0). We also defined CD8+ T cell subsets characterized by GZMK+, GZMB+, and GNLY+ expression. Using bulk and single-cell data, we mapped inflammatory mediators to source cell populations, for example attributing IL6 production to THY1+HLAhigh fibroblasts and naive B cells, and IL1B to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

immunology↗

bioRxiv2018

Proteostasis perturbation destabilizes respiratory complex assembly-intermediates via aggregation of subunits

Proteostasis is maintained by optimum expression, folding, transport, and clearance of proteins. Deregulation of any of these processes triggers widespread protein aggregation and loss of function. Here, we perturbed proteostasis by blocking proteasome-mediated protein degradation and investigated proteome partitioning from soluble to insoluble fraction. Aggregation of Respiratory Chain Complex (RCC) subunits highlights the early destabilization event as revealed by proteome redistribution. Sequence analyses followed by microscopy suggest that low complexity regions at the N-terminus are capable to facilitate aggregation of RCC subunits. As a result, respiratory complex assembly process is impaired due to destabilization of sub-complexes marking the onset of mitochondrial dysfunction and ROS accumulation. Redistribution of Histone proteins and their modifications indicated reprogramming of transcription as adaptive response. Together, we demonstrate susceptibility of RCC subunits to aggregation under multiple proteotoxic stresses providing an explanation for the simultaneous deregulation of proteostasis and bioenergetics in age-related degenerative conditions.

molecular biology↗