Biology subjects

Desai, J.

Publications and source records attributed to Desai, J..

8 recordsLinked to original sources

bioRxiv2026

Leucine Regulates Neuronal Health During Nutrient Deprivation in Caenorhabditis elegans

Alterations in nutrient availability induce changes in neuronal structure and connectivity across species, ultimately leading to nervous system plasticity and behavioural changes. However, it remains unclear how prolonged acute nutrient deprivation affects the integrity and function of the nervous system. Using the nematode Caenorhabditis elegans, we uncovered that prolonged macronutrient deprivation profoundly impacts neuronal health. Across developmental stages, multiple sensory neuron classes in the C. elegans nervous system, including polymodal IL2, volatile odor-sensing AWB and AWC, and CO2-sensing BAG neurons, display extensive dendritic and axonal blebbing, severely disorganized dendritic, axonal, and cell body morphologies, and severely disrupted chemical and electrical synaptic organization during prolonged nutrient deprivation. The severity of these defects increases progressively with the duration of nutrient deprivation, ultimately affecting animal behaviours. Our results show that the availability of a particular macronutrient, specifically the branched-chain amino acid leucine alone, in the absence of any other macronutrients, although insufficient to support animal growth, is sufficient to completely prevent nutrient-deprivation-mediated neuronal damage and the decline in animal behavioural responses. Moreover, leucine supplementation alone is sufficient to reverse nutrient-deprivation-mediated neuronal abnormalities. These findings suggest that the availability of leucine, rather than absolute caloric intake, is crucial for maintaining neuronal health and function under adverse conditions.

neuroscience↗

bioRxiv2026

An innate immune circuit involving the lectin pathway, complement and lung epithelial cells enforce barrier immunity to inhaled fungal disease

The lung mucosal barrier thwarts many inhaled pathogens, including Coccidioides, the causative agent of Valley Fever. Because the earliest stages of pathogen recognition in the lung remain obscure, we investigated the initial events of barrier immunity in a murine model involving inhalation of Coccidioides sp. arthroconidia. Neutrophils accumulated rapidly, within 24 hours, near fungal spores in the bronchiolar airways. This response was driven by pattern recognition via the lectin complement pathway. Bronchiolar club cells propagated C3a signals and amplified the response via convergent C3aR and P2X7 signaling. We identified several MBL2 and P2RX7 polymorphisms that correlated with progressive disease in humans. Our assays revealed that these mutations caused functional impairments in C3a generation and P2X7 responsiveness. Our findings establish how complement signaling and epithelial sensing coordinate early immune responses to fungal infection, offering insights into essential host defense mechanisms and risk factors for disease progression in coccidioidomycosis.

microbiology↗

bioRxiv2025

scExploreR: a flexible platform for democratized analysis of multimodal single-cell data by non-programmers

Single-cell sequencing has revolutionized biomedical research by uncovering cellular heterogeneity in disease mechanisms, with significant potential for advancing personalized medicine. However, participation in single-cell data analysis is limited by the programming experience required to access data. Several existing browsers allow the interrogation of single-cell data through a point-and-click interface accessible to non-programmers, but many of these browsers are limited in the depth of analysis that can be performed, or the flexibility of input data formats accepted. Thus, programming experience is still required for comprehensive data analysis. We developed scExploreR to address these limitations and extend the range of analysis tasks that can be performed by non-programmers. scExploreR is implemented as a packaged R Shiny app that can be run locally or easily deployed for multiple users on a server. scExploreR offers extensive customization options for plots, allowing users to generate publication quality figures. Leveraging our SCUBA package, scExploreR seamlessly handles multimodal data, providing identical plotting capabilities regardless of input format. By empowering researchers to directly explore and analyze single-cell data, scExploreR bridges communication gaps between biological and computational scientists, streamlining insight generation.

bioinformatics↗

bioRxiv2025

Integrative Multi-Omics Analysis Identifies Nuclear Factor I as a Key Driver of Dysregulated Purine Metabolism in DIPG

Diffuse intrinsic pontine glioma (DIPG) is a devastating brainstem cancer in children, with a median survival of under one year and limited treatment options. Over 80% of DIPGs possess a H3K27M mutation. To identify metabolic vulnerabilities linked to this mutation, we utilized a multi-omics approach in H3K27M-expressing cells, patient-derived cell lines, and mouse models. We show that by reprogramming chromatin landscape the mutation aberrantly induces NFI transcriptional activity, leading to misregulated purine metabolism. The mutation amplifies purine biosynthesis and degradation via the enzymes ATIC and PNP, respectively. Unregulated purine degradation relieves the negative feedback of purines on their own synthesis allowing continuous synthesis, use and degradation making DIPGs reliant on purine biosynthesis. Targeting ATIC reduced tumor progression and improved survival in mice. We propose ATIC as a potential novel target in DIPG.

cancer biology↗

bioRxiv2023

A multidimensional assessment of in-host fitness costs of drug resistance in the opportunistic fungal pathogen Candida glabrata

The global rise of antimicrobial resistance poses a serious threat to public health. Because drug-resistant (DR) pathogens typically carry mutations in genes involved in critical cellular functions, they may be less fit under drug-free conditions than their susceptible counterparts. As such, the limited use of antimicrobial drugs has been proposed as a practical strategy to diminish the prevalence of DR strains. However, in many cases the fitness of DR pathogens under host conditions is unknown. Candida (Nakaseomyces) glabrata is a prevalent opportunistic fungal pathogen notable for its high rate of fluconazole resistance (FLZR), echinocandin resistance (ECR), and multidrug resistance (MDR) relative to other Candida pathogens. Nonetheless, the fitness of C. glabrata MDR isolates is poorly characterized, and studies of FLZR isolate fitness have produced contradictory findings. Two important host niches for C. glabrata are macrophages, in which it can survive and proliferate, and the gut. Herein, by employing a comprehensive collection of clinical and isogenic C. glabrata isolates, we show that FLZR C. glabrata isolates are less fit inside macrophages than susceptible isolates and that this fitness cost is reversed by acquiring ECR mutations in FKS1/2 genes. Interestingly, dual-RNAseq revealed that macrophages infected with DR isolates mount an inflammatory response whereas the intracellular DR cells downregulate processes required for in-host adaptation. Consistently, DR isolates were outcompeted by their susceptible counterparts in the context of gut colonization and in the kidneys of systemically infected mice, whereas they showed comparable fitness in the spleen. Collectively, our study shows that macrophage-rich organs, such as the spleen, favor the retention of DR isolates, potentially reducing the utility of limited antifungal use to decrease the burden of DR C. glabrata in the context of candidemia. Author summaryThe rise of multidrug resistant (MDR) strains of fungal pathogens, notably Candida glabrata, poses a significant clinical challenge because of the limited number of antifungal drugs available for use. Thus, it is vital to minimize the prevalence of drug resistance in the clinic. Because in some bacterial and fungal species drug resistance is accompanied by a fitness cost, implementation of limited antibiotic or antifungal drug use in the clinic has been suggested as a practical way to favor the spread of susceptible isolates. However, it is not clear whether this strategy can work for MDR C. glabrata, as its fitness costs have not been systematically examined, particularly in the context of the host. Herein, we show that MDR C. glabrata isolates can replicate within macrophages as well as susceptible isolates, and this result was consistent with gene expression changes in the infected macrophages. In animal models, MDR strains were unfit in the context of the gastrointestinal tract and kidney, but their fitness in the spleen was comparable to that of susceptible strains. Accordingly, the potential of limited antifungal use to reduce the prevalence of MDR strains of C. glabrata strongly depends on the host reservoir of infection.

microbiology↗

bioRxiv2021

HES1 protein oscillations are necessary for neural stem cells to exit from quiescence

Quiescence is a dynamic process of reversible cell-cycle arrest. High-level sustained expression of the HES1 transcriptional repressor, which oscillates with an ultradian periodicity in proliferative neural stem cells (NSCs), is thought to mediate quiescence. However, it is not known whether this is due to a change in levels or in dynamics. Here, we induce quiescence in NSCs with BMP4, which does not increase HES1 level, and we find that HES1 continues to oscillate. To assess the role of HES1 dynamics, we express sustained HES1 under a moderate-strength promoter, which overrides the endogenous oscillations while maintaining the total HES1 level within physiological range. We find that sustained HES1 does not affect proliferation or entry into quiescence, however, exit from quiescence is impeded. Thus, oscillatory expression of HES1 is specifically required for NSCs to exit quiescence, a finding of potential importance for controlling reactivation of stem cells in tissue regeneration and cancer.

cell biology↗

bioRxiv2021

Dual MAPK and HDAC inhibition rewires the apoptotic rheostat to trigger colorectal cancer cell death

The EGFR/RAS/MEK/ERK signalling pathway (ERK/MAPK) is hyper-activated in most colorectal cancers (CRCs). A current limitation of inhibitors of this pathway is that they primarily induce cytostatic effects in CRC cells. Nevertheless, these drugs do induce expression of pro-apoptotic factors, suggesting they may prime CRC cells to undergo apoptosis. As histone deacetylase inhibitors (HDACi) induce expression of multiple pro-apoptotic proteins, we examined whether they could synergize with ERK/MAPK inhibitors to trigger CRC cell apoptosis. Combined MEK/ERK and HDAC inhibition synergistically induced apoptosis in CRC cell lines and patient-derived tumour organoids in vitro, and attenuated Apc-initiated adenoma formation in vivo. Mechanistically this effect was mediated through induction of the BH3-only pro-apoptotic proteins BIM and BMF. Importantly, we demonstrate that this treatment paradigm can be tailored to specific MAPK genotypes in CRCs, by combining HDACi with EGFR, KRASG12C or BRAFV600 inhibitors in KRAS/BRAFWT; KRASG12C, BRAFV600E CRC cell lines respectively. These findings identify a novel ERK/MAPK genotype-targeted treatment paradigm for colorectal cancer.

cancer biology↗

bioRxiv2016

Incorporating the Rate of Transcriptional Change Improves Construction of Transcription Factor Regulatory Networks

Transcriptional regulatory networks (TRNs) can be developed by computational approaches that infer regulator-target gene interactions from transcriptional assays. Successful algorithms that generate predictive, accurate TRNs enable the identification of regulator-target relationships in conditions where experimentally determining regulatory interactions is a challenge. Improving the ability of TRNs to successfully predict known regulator-target relationships in model species will enhance confidence in applying these approaches to determine regulator-target interactions in non-model species where experimental validation is challenging. Many transcriptional profiling experiments are performed across multiple time points; therefore we sought to improve regulator-target predictions by adjusting how time is incorporated into the network. We created ExRANGES, which incorporates Expression in a Rate-Normalized GEne Specific manner that adjusts how expression data is provided to the network algorithm. We tested this on a two different network construction approaches and found that ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. ExRANGES improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, we examined the performance of ExRANGES on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set.\n\nAuthor SummaryTo understand how organisms can turn a collection of genes into a physiological response, we need to understand how certain genes are turned on and off. In model organisms, the ability to identify direct targets of transcription factors via ChIP-Seq in a high-throughput manner has advanced our understanding of transcriptional regulatory networks and how organisms regulate gene expression. However, for non-model organisms, it remains a challenge to identify TF-target relationships through experimental approaches such as ChIP-Seq. Without this information, the ability to understand regulatory control is limited. Computational approaches to identify regulator-target relationships in silico from easily attainable transcriptional data offer a solution. Several approaches exist for identifying gene regulatory networks, including many that take advantage of time series data. Most of these approaches weigh the relationship between regulators and putative targets at all time points equally. However, many regulators may control a single target in response to different inputs. In our approach, we focus on the association between regulators and targets primarily at times when there is a significant change in expression. ExRANGES essentially weights the expression value of each time point by the slope change after that time point so that relationships between regulators and targets are emphasized at the time points when the transcript levels are changing. This change in input into network identification algorithms improves the ability to predict regulator-target interactions and could be applied to many different algorithms We hope this improvement will increase the ability to identify regulators of interest in non-model species.

bioinformatics↗