Biology subjects

Kothapalli, N.

Publications and source records attributed to Kothapalli, N..

3 recordsLinked to original sources

bioRxiv2024

Characterization of Collaborative Cross mouse founder strain CAST/EiJ as a novel model for lethal COVID-19

Mutations in SARS-CoV-2 variants of concern (VOCs) have expanded the viral host range beyond primates, and a limited range of other mammals, to mice, affording the opportunity to exploit genetically diverse mouse panels to model the broad range of responses to infection in patient populations. Here we surveyed responses to VOC infection in genetically diverse Collaborative Cross (CC) founder strains. Infection of wild-derived CC founder strains produced a broad range of viral burden, disease susceptibility and survival, whereas most other strains were resistant to disease despite measurable lung viral titers. In particular, CAST/EiJ, a wild-derived strain, developed high lung viral burdens, more severe lung pathology than seen in other CC strains, and a dysregulated cytokine profile resulting in morbidity and mortality. These inbred mouse strains may serve as a valuable platform to evaluate therapeutic countermeasures against severe COVID-19 and other coronavirus pandemics in the future.

immunology↗

bioRxiv2023

Massively parallel base editing screens to map variant effects on anti-tumor hallmarks of primary human T cells

Base editing enables generation of single nucleotide variants, but large-scale screening in primary human T cells is limited due to low editing efficiency, among other challenges1. Here, we developed a high-throughput approach for high-efficiency and massively parallel adenine and cytosine base-editor screening in primary human T cells. We performed multiple large-scale screens editing 102 genes with central functions in T cells and full-length tiling mutagenesis of selected genes, and read out variant effects on hallmarks of T cell anti-tumor immunity, including activation, proliferation, and cytokine production. We discovered a broad landscape of gain- and loss-of-function mutations, including in PIK3CD and its regulatory subunit encoded by PIK3R1, LCK, AKT1, CTLA-4 and JAK1. We identified variants that affected several (e.g., PIK3CD C416R) or only selected (e.g. LCK Y505C) hallmarks of T cell activity, and functionally validated several hits by probing downstream signaling nodes and testing their impact on T cell polyfunctionality and proliferation. Using primary human T cells in which we engineered a T cell receptor (TCR) specific to a commonly presented tumor testis antigen as a model for cellular immunotherapy, we demonstrate that base edits identified in our screens can tune specific or broad T cell functions and ultimately improve tumor elimination while exerting minimal off-target activity. In summary, we present the first large-scale base editing screen in primary human T cells and provide a framework for scalable and targeted base editing at high efficiency. Coupled with multi-modal phenotypic mapping, we accurately nominate variants that produce a desirable T cell state and leverage these synthetic proteins to improve models of cellular cancer immunotherapies.

immunology↗

bioRxiv2022

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES

SummaryRecent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters, and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms and is compatible with existing single-cell packages and pseudotime techniques. NICHES is also a user friendly and extensible program, allowing rapid analysis of cell-cell signaling at single-cell resolution. Availability and implementationNICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. Contactmichasam.raredon@yale.edu; yuval.kluger@yale.edu

bioinformatics↗