Biology subjects

Sur, S.

Publications and source records attributed to Sur, S..

22 records · Page 2Linked to original sources

bioRxiv2021

Modeling Substrate Coordination to Zn-Bound Angiotensin Converting Enzyme 2

The spike protein in the envelope of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interacts with the receptor Angiotensin Converting Enzyme 2 (ACE2) on the host cell to facilitate the viral uptake. Angiotensin II (Ang II) peptide, which has a naturally high affinity for ACE2, may be useful in inhibiting this interaction. In this study, we computationally designed several Ang II mutants to find a strong binding sequence to ACE2 receptor and examined the role of ligand substitution in the docking of native as well as mutant Ang II to the ACE2 receptor. The peptide in the ACE2-peptide complex was coordinated to zinc in the ACE2 cleft. Exploratory molecular dynamics (MD) simulations were used to measure the time-based stability of the native and mutant peptides and their receptor complexes. The MD-generated root-mean-square deviation (RMSD) values are mostly similar between the native and seven mutant peptides considered in this work, although the values for free peptides demonstrated higher variation, and often were higher in amplitude than peptides associated with the ACE2 complex. An observed lack of a strong secondary structure in the short peptides is attributed to the latters greater flexibility and movement. The strongest binding energies within the ACE2-peptide complexes were observed in the native Ang II and only one of its mutant variants, suggesting ACE2 cleft is designed to provide optimal binding to the native sequence. An examination of the S1 binding site on ACE2 suggests that complex formation alone with these peptides may not be sufficient to allosterically inhibit the binding of SARS-CoV-2 spike proteins. However, it opens up the potential for utilizing AngII-ACE2 binding in the future design of molecular and supramolecular structures to prevent spike protein interaction with the receptor through creation of steric hindrance.

biophysics↗

bioRxiv2021

Exosomes from COVID-19 patients carry tenascin-C and fibrinogen-β in triggering inflammatory signals in distant organ cells

SARS-CoV-2 infection causes cytokine storm and overshoot immunity in humans; however, it remains to be determined whether genetic material of SARS-CoV-2 and/or virus induced soluble mediators from lung epithelial cells as natural host are carried out by macrophages or other vehicles at distant organs causing tissue damage. We speculated that exosomes as extracellular vesicles are secreted from SARS-CoV-2 infected cells may transport messages to other cells of distant organs leading to pathogenic consequences. For this, we took an unbiased proteomic approach for analyses of exosomes isolated from plasma of healthy volunteers and SARS-CoV-2 infected patients. Our results revealed that tenascin-C (TNC) and fibrinogen-{beta} (FGB) are highly abundant in exosomes from SARS-CoV-2 infected patients plasma as compared to that of healthy normal controls. Since TNC and FGB stimulate pro-inflammatory cytokines via NF-{kappa}B pathway, we examined the status of TNF-, IL-6 and CCL5 expression upon exposure of hepatocytes to exosomes from COVID-19 patients and observed significant increase when compared with that from healthy subjects. Together, our results demonstrated that soluble mediators, like TNC and FGB, are transported through plasma exosomes in SARS-CoV-2 infected patients and trigger pro-inflammatory cytokine expression in cells of distant organs in COVID-19 patients. ImportanceExosomes play an important role in intercellular communication by inducing physiological changes in recipient cells by transferring bioactive proteins. Little is known about exosomes from SARS-CoV-2 infected cells and their role in pathogenesis. Here, we have carefully examined and analyzed this aspect of SARS-CoV-2 infection. Our results uncovered the potential mechanisms by which SARS-CoV-2 communicates with other cells of distant organs and promotes pathogenesis. We expect to detect whether other factors are modulated in the presence of COVID-19 exosomes. Our exosomes related proteomic experiments prioritize after initial verification to further examine their role in SARS-CoV-2 associated other pathogenic mechanisms to target for therapeutic modalities.

physiology↗

bioRxiv2020

Novel Human Kidney Cell Subsets Identified by Mux-Seq

BackgroundThe kidney is a highly complex organ that performs multiple functions necessary to maintain systemic homeostasis, with complex interplay from different kidney sub-structures and the coordinated response of diverse cell types, few known and likely many others, as yet undiscovered. Traditional global sequencing techniques are limited in their ability to identify unique and functionally diverse cell types in complex tissues. MethodsHerein we characterize over 45,000 cells from 10 normal human kidneys using unbiased single-cell RNA sequencing. We also apply, for the first time, an approach of multiplexing kidney samples (Mux-Seq), pooled from different individuals, to save input sample amount and cost. We applied the computational tool Demuxlet to assess differential expression across multiple individuals by pooling human kidney cells for scRNA sequencing, utilizing individual genetic variability to determine the identity of each cell. ResultsMultiplexed droplet single-cell RNA sequencing results were highly correlated with the singleplexed sample run data. One hundred distinct cell cluster populations in total were identified across the major cell types of the kidney, with varied functional states. Proximal tubular and collecting duct cells were the most heterogeneous, displaying multiple clusters with unique ontologies. Novel proximal tubular cell subsets were identified with regenerative potential. Trajectory analysis demonstrated evolution of cell states between intercalated and principal cells in the collecting duct. ConclusionsHealthy kidney tissue has been successfully analyzed to detect all known renal cell types, inclusive of resident and infiltrating immune cells in the kidney. Mux-Seq is a unique method that allows for rapid and cost-effective single cell, in depth, transcriptional analysis of human kidney tissue. Significance StatementUse of renal biopsies for single cell transcriptomics is limited by small tissue availability and batch effects. In this study, we have successfully employed the use of Mux-Seq for the first time in kidney. Mux-Seq allows the use of single cell technology at a much more cost-effective manner by pooling samples from multiple individuals for a single sequencing run. This is even more relevant in the case of patient biopsies where the input of tissue is significantly limited. We show that the data from overlapping tissue samples are highly correlated between Mux-Seq and traditional Singleplexed RNA seq. Furthermore, the results from Mux-Seq of 4 pooled samples are highly correlated with singleplexed data from 10 singleplex samples despite the inherent variability among individuals.

genomics↗

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↗