Biology subjects

Khurana, R.

Publications and source records attributed to Khurana, R..

8 recordsLinked to original sources

bioRxiv2025

Targeting CREB remodels the immune microenvironment to enhance immunotherapy responses in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a challenging disease in need of improved treatments. Cyclic adenosine monophosphate response element binding protein 1 (CREB) is an emerging therapeutic target whose oncogenic effects in PDAC have been largely attributed to a key molecular interplay between oncogenic KrasG12D/+ (Kras*) and chronic inflammation driving irreversible acinar to ductal reprogramming. Here, we demonstrate that CREB activation fosters tumor associated macrophage (TAM) mediated immunosuppression and promotes PDAC growth in an aggressive LSL-KrasG12D/+;Trp53R172H/+;Pdx1Cre/+(KPC) genetically engineered mouse model. Selective deletion of CREB (Crebfl/fl) in KPC(KPCC-/-) mice attenuates primary disease burden. Unbiased transcriptomic analysis and validation using diverse molecular, genetic and pharmacological approaches in vitro and in vivo identify CREB-mediated transcriptional regulation of leukemia inhibitory factor (Lif) as one of the potential mediators of tumor cell-macrophage crosstalk promoting a pro-tumor polarization of TAMs, thereby attenuating the infiltration of effector T cells. Mechanistically, cancer cell derived LIF facilitates an immunosuppressive, pro-tumorigenic state. Importantly, pharmacological targeting of the CREB-LIF signaling axis between cancer cells and macrophages, using a CREB-specific inhibitor (CREBi), significantly suppresses tumor growth and sensitizes PDAC to immunotherapy, highlighting the therapeutic potential of this treatment combination to improve outcomes in this aggressive disease.

cancer biology↗

bioRxiv2025

SigSpace: an LLM-based agent for drug responsesignature interpretation

Agent systems powered by large language models (LLMs) are increasingly applied in computational biology to automate analysis, integrate data, and accelerate discovery. Here, we investigate the capacity of LLM-driven agents to interpret transcriptional response signatures of drug perturbations in cancer cell lines, a task central to understanding drug mechanisms of action (MoAs) and supporting cancer drug discovery. Leveraging the Tahoe-100M dataset of 100 million transcriptomic profiles across 1,100 small-molecule perturbations and 50 cancer cell lines, we developed an LLM-based agent system, SigSpace, that processes differential gene expression signatures and generates concise, human-readable summaries of drug responses. We then tested whether blinded response signature summaries could be correctly matched to their corresponding drug identity or MoA. Our results show that LLM-generated summaries consistently outperform random baselines and that the choice of LLM model and signature score significantly influences performance. These findings highlight the potential of LLMs to enhance interpretation of complex transcriptional data to enable drug discovery. Future directions for improvement include exploring alternative response signature formats, improving summarization fidelity, benchmarking performance across different summary formats and lengths, and broadening applications to additional datasets and predictive tasks in drug discovery.

bioinformatics↗

bioRxiv2024

Multiparameter Quantitative Analyses of Diagnostic Cells in Brain Tissues from Tuberous Sclerosis Complex

The advent of high-dimensional imaging approaches offers innovative opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example of such disorders is tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine-learning workflow (Balloon Identifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom antibody panel and 36-parameter imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC markers and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. These findings comprise a toolbox and dataset for understanding the abundance, structure, and signaling activity of these histopathologically abnormal cells, and an example case of how such tools can be developed and applied within human tissues.

neuroscience↗

bioRxiv2023

Serial dilution shapes genetic variation and defines conservation units in Asian elephants

Asian elephants (Elephas maximus) are the largest extant terrestrial megaherbivores native to Asia, with 60% of their wild population found in India. Despite ecological and cultural importance, their population genetic structure and diversity, demographic history, and ensuing implications for management/conservation remain understudied. We analysed 34 whole genomes (between 11X - 32X) from most known elephant landscapes in India and identified five management/conservation units corresponding to elephants in Northern (Northwestern/Northeastern) India, Central India and three in Southern India. Our genetic data reveal signatures of serial colonisation, and a dilution of genetic diversity from north to south of India. The Northern populations diverged from other populations more than 70,000 years ago, have higher genetic diversity, and low inbreeding/high effective size (Pi = 0.0016{+/-}0.0001; FROH> 1MB= 0.09{+/-}0.03). Two of three populations in Southern India (South of Palghat Gap: SPG, and South of Shencottah Gap:SSG) have low diversity and are inbred, with very low effective population sizes compared to current census sizes (Pi = 0.0014{+/-}0.00009 and 0.0015{+/-}0.0001; FROH> 1MB= 0.25{+/-}0.09 and 0.17{+/-}0.02). Analyses of genetic load reveals purging of potentially high-effect insertion/deletion (indel) deleterious alleles in the Southern populations and potential dilution of all deleterious alleles from north to south in India. However, despite dilution and purging for the damaging mutation load in Southern India, the load that remains is homozygous. High homozygosity of deleterious alleles, coupled with low neutral genetic diversity make these populations (SPG and SSG) high priority for conservation attention. Most surprisingly, our study suggests that patterns of genetic diversity and genetic load can correspond to geographic signatures of serial founding events, even in large, highly mobile, endangered mammals.

evolutionary biology↗

bioRxiv2022

Alignment, Segmentation and Neighborhood Analysis in Cyclic Immunohistochemistry Data Using CASSATT

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. In addition, cycIHC benefits from the speed and simplicity of brightfield microscopy for data collection, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets (greater than 50 GB) currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the logodds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact - or do not interact - at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT was first tested using a newly generated cycIHC dataset consisting of six GBM tissue sections processed through eight cycles of AEC based IHC staining. Further validation was completed on a previously published lung cancer tissue microarray dataset consisting of 107 cores processed through eighteen cycles of staining and imaging. CASSATT is a fully open-source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

bioinformatics↗

bioRxiv2022

Reversal of lactate and PD-1-mediated macrophage immunosuppression controls growth of PTEN/p53-deficient prostate cancer

PTEN loss-of-function occurs in approximately 50% of mCRPC patients, and is associated with a poor prognosis, therapeutic outcomes and resistance to immune-checkpoint inhibitors. Recent clinical studies demonstrated that dual PI3K/AKT pathway inhibition and androgen axis blockade led to a modest improvement in progression-free survival of PTEN-deficient mCRPC patients, but the mechanistic basis for this limited efficacy is unknown. To elucidate potential resistance mechanism(s), we performed co-clinical trials in a prostate-specific PTEN/p53-deficient genetically-engineered mouse model, and discovered that the recruitment of PD-1-expressing tumor-associated macrophages (TAM) thwarts the phagocytosis-mediated anti-tumor efficacy of androgen deprivation therapy (ADT)/PI3K inhibitor (PI3Ki) combination. Strikingly, we observed a TAM-dependent [~]3-fold enhancement in the overall response rate with the addition of PD-1 antibody (aPD-1) to ADT/PI3Ki combination therapy. Mechanistically, decreased lactate production from PI3Ki-treated tumor cells suppressed histone lactylation (H3K18lac) within TAM, resulting in their phagocytic activation, which was augmented by concurrent ADT/aPD-1 treatment. Consistent with our murine observations, single cell RNA-sequencing analysis of human metastatic PC samples revealed a direct correlation between high glycolytic activity and phagocytosis suppression. Critically, feedback activation of Wnt/{beta}-catenin signaling observed in non-responder mice following ADT/PI3Ki/aPD-1 combination treatment, restored lactate-mediated H3K18lac and suppressed phagocytosis within macrophages. Altogether, these data suggest that reversal of lactate and PD-1-mediated TAM immunosuppression by PI3Ki and aPD-1, respectively, controls tumor growth in combination with ADT, and warrants further clinical investigation in PTEN/p53-deficient mCRPC patients. One Sentence SummaryInhibition of tumor-cell intrinsic lactate production suppresses PTEN/p53-deficient prostate cancer growth via macrophage activation/phagocytosis

cancer biology↗

bioRxiv2020

Creation and validation of 3D-printed head molds for stereotaxic injections of neonatal mouse brains

BackgroundAn increasing number of rodent model systems use injection of DNA or viral constructs in the neonatal brain. However, approaches for reliable positioning and stereotaxic injection at this developmental stage are limited, typically relying on handheld positioning or molds that must be re-aligned for use in a given laboratory. New methodA complete protocol and open source software pipeline for generating 3D-printed head molds derived from a CT scan of a neonatal mouse head cast, together with a universal adapter that can be placed on a standard stereotaxic stage. ResultsA series of test injections with adenovirus encoding red fluorescent protein were conducted using original clay molds and newly generated 3D printed molds. Several metrics were used to compare spread and localization of targeted injections. Comparison with existing methodsThe new method of head mold generation gave comparable results to the field standard, but also allowed the rapid generation of additional copies of each head mold with standardized positioning of the head each time. ConclusionsThis 3D printing pipeline can be used to rapidly develop a series of head molds with standardized injection coordinates across multiple laboratories. More broadly, this pipeline can easily be adapted to other perinatal ages or species.

neuroscience↗

bioRxiv2020

The Clinical Kinase Index: Prioritizing Understudied Kinases as Targets for the Treatment of Cancer

The approval of the first kinase inhibitor, Gleevec, in 2001, ushered in a paradigm shift for oncological treatment--the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the established role deregulated kinase activity plays in cancer, only 8% of the entire kinome has been effectively "drugged". Moreover, a quarter of the 634 human kinases are vastly understudied. We have developed a comprehensive scoring system which utilizes differential gene expression, clinical and pathological parameters, overall survival and mutational hotspot analysis to rank and prioritize clinically-relevant kinase targets across 17 solid tumor cancers from The Cancer Genome Atlas (TCGA). Collectively, we report that dark kinases have potential clinical value as biomarkers or as new drug targets that warrant further study.

bioinformatics↗