Biology subjects

Chitalia, V. C.

Publications and source records attributed to Chitalia, V. C..

3 recordsLinked to original sources

bioRxiv2025

Natural and patient-derived mutations in BK polyomavirus VP1 reveal structural determinants of BC-loop dependent antibody escape

BK polyomavirus (BKPyV) reactivation poses a serious threat to both renal and hematopoietic transplant outcomes, with no approved antiviral therapies. Neutralizing monoclonal antibodies targeting the viral capsid protein VP1 have recently advanced into clinical development, driven by promising in vitro potency. Since naturally elicited neutralizing antibodies arise following infection, we examined whether host immune pressure may have shaped the VP1 surface, with implications for therapeutic application. To probe the impact of VP1 coevolution on therapeutic engagement, we evaluated two neutralizing antibody formats: the clinical-stage monoclonal antibody 319C07 and nanobodies VHH16 and VHH17. Our X-ray crystallographic analysis of the VP1 pentameric core in complex with antibody fragments showed a convergent receptor- mimetic engagement of the sialic acid-binding cleft, mapping to a shared epitope centered around the VP1 BC-loop. Analysis of over 900 BKPyV VP1 sequences from public databases, combined with longitudinal VP1 sequence data from transplant recipients, highlighted widespread pre-existing diversity at the BC-loop contact sites, underscoring the evolutionary adaptability of BKPyV at this antigenic surface that may compromise therapeutic recognition. Through integrated mutagenesis and binding analyses, we show that circulating BC-loop mutations, including single amino acid substitutions, are sufficient to abrogate 319C07 binding and neutralization. Nanobodies VHH16 and VHH17, selected for their compact size and potential to access intrarenal sites of BKPyV replication, also exhibited complete loss of binding across multiple clinically observed VP1 variants. Our findings may offer a mechanistic framework to interpret the translational gap between in vitro neutralization and clinical efficacy of these biologics. As part of ongoing efforts to target VP1, we demonstrate, for the first time, small molecules with nanomolar to sub-nanomolar affinities against BKPyV VP1 variants including potent binding to JCPyV VP1. Our work paves a path towards a new class of antiviral strategies that could block replication at intracellular replication sites, provide a higher genetic barrier to resistance and the potential to address both viral reactivation and persistent, high- burden viremia in transplant settings.

microbiology↗

bioRxiv2020

SARS-CoV-2 desensitizes host cells to interferon through inhibition of the JAK-STAT pathway

SARS-CoV-2 can infect multiple organs, including lung, intestine, kidney, heart, liver, and brain. The molecular details of how the virus navigates through diverse cellular environments and establishes replication are poorly defined. Here, we performed global proteomic analysis of the virus-host interface in a newly established panel of phenotypically diverse, SARS-CoV-2-infectable human cell lines representing different body organs. This revealed universal inhibition of interferon signaling across cell types following SARS-CoV-2 infection. We performed systematic analyses of the JAK-STAT pathway in a broad range of cellular systems, including immortalized cell lines and primary-like cardiomyocytes, and found that several pathway components were targeted by SARS-CoV-2 leading to cellular desensitization to interferon. These findings indicate that the suppression of interferon signaling is a mechanism widely used by SARS-CoV-2 in diverse tissues to evade antiviral innate immunity, and that targeting the viral mediators of immune evasion may help block virus replication in patients with COVID-19.

microbiology↗

bioRxiv2018

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning

IntroductionThe number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute as standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive and non-standardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. MethodsTrichrome-stained images (n=275) from renal biopsies of 171 chronic kidney disease patients treated at the Boston Medical Center from 2009-12 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by three experts into three categories: (a) No glomerulus, (b) Normal or partially sclerosed glomerulus and (c) Globally sclerosed glomerulus. This led to identification of 751 unique images representing non{-}glomerular regions, 611 images with either normal or partially sclerosed (NPS) glomeruli and 134 images with globally sclerosed (GS) glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test data images to segment the GS glomeruli. ResultsThe CNN model was able to accurately discriminate non-glomerular images from NPS and GS images (Performance on test data - Accuracy: 92.67{+/-}2.02% and Kappa: 0.8681{+/-}0.0392). The segmentation model that was based on the CNN multi-label classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). ConclusionThis work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

pathology↗