Biology subjects

Mukherjee, S.

Publications and source records attributed to Mukherjee, S..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

A basic framework governing splice-site choice in eukaryotes

Changes in splicing are observed between cells, tissues, organs, individuals, and species. These changes can mediate phenotypic variation ranging from flowering time differences in plants to genetic diseases in humans. However, the genomic determinants of splicing variation are largely unknown. Here, we quantified the usage of individual splice-sites and uncover extensive variation between individuals (genotypes) in Arabidopsis, Drosophila and Humans. We used this robust quantitative measure as a phenotype and mapped variation in splice-site usage using Genome-Wide Association Studies (GWAS). By carrying out more than 130,000 GWAS with splice-site usage phenotypes, we reveal genetic variants associated with differential usage of specific splice-sites. Our analysis conclusively shows that most of the common, genetically controlled variation in splicing is cis and there are no major trans hotspots in any of the three analyzed species. High-resolution mapping allowed us to determine genome-wide patterns that govern splice-site choice. We reveal that the variability in the intronic hexamer sequence (GT[N]4 or [N]4AG) differentiates intrinsic splice-site strength and is among the primary determinants of splice-site choice. Experimental analysis validates the primary role for intronic hexamer sequences in conferring splice-site decisions. Transcriptome analyses in diverse species across the tree of life reveals that hexamer rankings explains splice-site choices from yeast to plants to humans, forming the basic framework of the splicing code in eukaryotes.

genetics↗

bioRxiv2024

Development of an orally bioavailable mSWI/SNF ATPase degrader and acquired mechanisms of resistance in prostate cancer

Mammalian switch/sucrose non-fermentable (mSWI/SNF) ATPase degraders have been shown to be effective in enhancer-driven cancers by functioning to impede oncogenic transcription factor chromatin accessibility. Here, we developed AU-24118, a first-in-class, orally bioavailable proteolysis targeting chimera (PROTAC) degrader of mSWI/SNF ATPases (SMARCA2 and SMARCA4) and PBRM1. AU-24118 demonstrated tumor regression in a model of castration-resistant prostate cancer (CRPC) which was further enhanced with combination enzalutamide treatment, a standard of care androgen receptor (AR) antagonist used in CRPC patients. Importantly, AU-24118 exhibited favorable pharmacokinetic profiles in preclinical analyses in mice and rats, and further toxicity testing in mice showed a favorable safety profile. As acquired resistance is common with targeted cancer therapeutics, experiments were designed to explore potential mechanisms of resistance that may arise with long-term mSWI/SNF ATPase PROTAC treatment. Prostate cancer cell lines exposed to long-term treatment with high doses of a mSWI/SNF ATPase degrader developed SMARCA4 bromodomain mutations and ABCB1 overexpression as acquired mechanisms of resistance. Intriguingly, while SMARCA4 mutations provided specific resistance to mSWI/SNF degraders, ABCB1 overexpression provided broader resistance to other potent PROTAC degraders targeting bromodomain-containing protein 4 (BRD4) and AR. The ABCB1 inhibitor, zosuquidar, reversed resistance to all three PROTAC degraders tested. Combined, these findings position mSWI/SNF degraders for clinical translation for patients with enhancer-driven cancers and define strategies to overcome resistance mechanisms that may arise. Significance StatementThe mSWI/SNF complex is a promising therapeutic target for enhancer-driven cancers. PROTACs, which enable the targeting of "undruggable" proteins, often face the challenge of achieving oral bioavailability. Here, we present AU-24118, a first-in-class, orally bioavailable mSWI/SNF ATPase dual degrader with remarkable efficacy in in vitro and in vivo models. Additionally, our study describes two distinct mechanisms of resistance to PROTAC degraders, providing crucial insights into potential challenges facing their clinical application. These findings are critical for advancing PROTAC-based therapies to clinical settings as targeted therapies for cancers.

cancer biology↗

bioRxiv2024

Adoptive transfer of mitochondrial antigen-specific CD8+ T-cells in mice causes parkinsonism and compromises the dopamine system

The progressive degeneration of dopamine (DA) neurons drives motor symptoms in Parkinsons disease (PD). Whether this neuronal degeneration is due to cell-autonomous dysfunctions in DA neurons or to death signals generated by other cell types is a key problem to address. Recent evidence suggests that loss of function of the protein PINK1, linked to early-onset forms of PD, enhances the presentation of self-derived mitochondrial antigens, which induces the response of autoreactive CD8+ T cells. Whether mitochondrial antigen-specific CD8+ T cells alone are sufficient to induce nigrostriatal dysfunction has not been directly tested. Here we performed adoptive transfer of mitochondrial antigen-specific CD8+ T cells into wild-type or PINK1-deficient mice. We provide evidence for the entry and persistence of such cells in the brain and show that this leads to levodopa-reversible motor dysfunctions and partial degeneration of the nigrostriatal DA system in both genotypes. These findings establish that brain entry of autoreactive CD8+ T cells is sufficient to drive nigrostriatal degeneration and parkinsonian motor deficits, providing the most direct support to date for the hypothesis that an adaptive immune attack plays a key role in PD-like neurodegeneration.

neuroscience↗

bioRxiv2024

Long-range chemical signalling in vivo is regulated by mechanical signals

Biological processes are regulated by chemical and mechanical signals, yet the interaction between these signalling modalities remains poorly understood. Using the developing Xenopus laevis brain as a model system, we identified a critical crosstalk between tissue stiffness and long-range chemical signalling in vivo. Targeted knockdown of the mechanosensitive ion channel Piezo1 in retinal ganglion cells (RGCs) led to pathfinding errors in vivo. However, pathfinding errors were also observed in RGCs expressing Piezo1, when Piezo1 was downregulated in the surrounding brain tissue. Depleting Piezo1 in the brain parenchyma led to a decrease in the expression of the long-range chemical guidance cues Semaphorin3A (Sema3A) and Slit1, which instruct turning responses in distant cells. Furthermore, Piezo1 knockdown markedly reduced tissue stiffness. This tissue softening was independent of Sema3A depletion, and was caused by a decrease in the cell-cell adhesion proteins NCAM1 and N-Cadherin. Downregulating NCAM1 and N-Cadherin was sufficient to reduce tissue stiffness and Sema3A expression. Conversely, increasing environmental stiffness ex vivo resulted in enhanced tissue-level force generation and an increase in Slit1 and Sema3A expression. Moreover, stiffening soft brain regions in vivo induced ectopic Sema3A production via a Piezo1-dependent mechanism. Hence, tissue mechanics can locally modulate the availability of diffusive, long-range chemical signals, thus influencing cell function at sites distant from the mechanical cue. Such indirect regulatory mechanisms of cell function through mechanical signals are likely widespread across biological systems.

developmental biology↗

bioRxiv2024

Leucaena and Acaia leaf litter negatively affect the survivorship of tadpoles of the South Asian frog Microhyla ornata

Leaf litter from terrestrial plants forms an essential source of organic food matter for many freshwater aquatic organisms. However, leaves of some species are known to alter water quality, affecting the development and survivorship of amphibians. While amphibians from North and South America are affected by terrestrial leaf litter, similar studies are missing from Asia, a region with high amphibian diversity (mainly from the south and south-east Asia). At different tadpole densities, we tested the effect of different leaf litter (two non-native trees and a grass species) on the survivorship of ornate narrow-mouthed frog tadpoles (Microhyla ornata), a common frog in South Asia. We found the tadpole probability of survival extremely low in Leucaena but high in Themeda. While the odds of survivorship were nearly twelvefold higher in Acacia than Leucaena, it was not as high as in Themeda. A tadpole also had a lower odds of survival if raised in a high-density environment. In addition, Leucaena litter water had significantly higher pH levels than other litter types. Since both Leucaena and Acacia are non-native trees actively grown for agroforestry in South Asia, our results highlight another potential threat to amphibians in this region. Given the high amphibian diversity in this region, there is an urgent need for similar studies on other anurans and aquatic organisms.

ecology↗

bioRxiv2024

Structure of hepatitis B/D antiviral drug Bulevirtide bound to its receptor protein NTCP

Cellular entry of the hepatitis B and D viruses (HBV/HDV) require binding of the viral surface polypeptide preS1 to the hepatobiliary transporter NTCP. This interaction can be blocked by bulevirtide (BLV, formerly Myrcludex B), a preS1 derivative and approved drug for treating HDV infection. To elucidate the basis of this inhibitory function, we determined a cryo-EM structure of BLV-bound human NTCP. BLV forms two domains, a plug lodged in the bile salt transport tunnel of NTCP and a string that covers the receptors extracellular surface. The N-terminally attached myristoyl group of BLV interacts with the lipid-exposed surface of NTCP. Our structure reveals how BLV inhibits bile salt transport, rationalizes NTCP mutations that decrease the risk of HBV/HDV infection, and provides a basis for understanding the host specificity of HBV/HDV. Our results provide opportunities for structure-guided development of inhibitors that target HBV/HDV docking to NTCP.

biochemistry↗

bioRxiv2023

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

SummaryThe emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours. Availability and ImplementationThe ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.

bioinformatics↗

bioRxiv2023

G6PD deficiency mediated impairment of iNOS and lysosomal acidification affecting phagocytotic clearance in microglia in response to SARS-CoV-2

The glucose-6-phosphate dehydrogenase (G6PD) deficiency is X-linked and is the most common enzymatic deficiency disorder globally. It is a crucial enzyme for the pentose phosphate pathway and produces NADPH, which plays a vital role in the regulation of oxidative stress of many cell types. The deficiency of G6PD causes hemolytic anemia, diabetes, cardiovascular and neurological disorders. Notably, the patient with G6PD deficiency was severely affected by SARS-CoV-2 and showed prolonged COVID-19 symptoms, neurological impacts, and high mortality. However, the mechanism of COVID-19 severity in G6PD deficient patients is still ambiguous. Here, using a CRISPR-edited G6PD deficient human microglia cell culture model, we observed a significant reduction in NADPH and an increase in basal reactive oxygen species (ROS) in microglia. Interestingly, the deficiency of the G6PD-NAPDH axis impairs induced nitric oxide synthase (iNOS) mediated nitric oxide (NO) production which plays a fundamental role in inhibiting viral replication. Surprisingly, we also observed that the deficiency of the G6PD-NADPH axis reduced lysosomal acidification, which further abrogates the lysosomal clearance of viral particles. Thus, impairment of NO production and lysosomal acidification as well as redox dysregulation in G6PD deficient microglia altered innate immune response, promoting the severity of SARS-CoV-2 pathogenesis.

cell biology↗

bioRxiv2023

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

Machine learning (ML)-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the mean{chi} 2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits, and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEMs utility by evaluating embeddings and multivariate traits in two separate datasets: i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance, and ii) a real data from the UK Biobank including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

bioinformatics↗

bioRxiv2023

Next-Gen Profiling of Tumor-resident Stem Cells using Machine learning

Tumor-resident stem cells, also known as cancer stem cells (CSCs), constitute a subgroup within tumors, play a crucial role in fostering resistance to treatment and the recurrence of tumors, and pose significant challenges for conventional therapeutic methods. Existing approaches for identifying CSCs face notable hurdles related to scalability, reproducibility, and technical consistency across different cancer types due to the adaptable nature of CSCs. In this context, we introduce OSCORP, an innovative machine-learning-driven approach. This methodology quantifies and identifies CSCs, achieving almost 99% accuracy using biopsy bulk RNAseq data. OSCORP leverages genetic similarities between normal and cancer stem cells. By categorizing CSCs into four distinct yet dynamic potency states, this approach provides insights into the differentiation landscape of CSCs, unveiling previously undisclosed facets of tumor heterogeneity. In evaluations conducted on patient samples across 22 cancer types, OSCORP revealed clinical, transcriptomic, and immunological signatures associated with each CSC state. It has emerged as a comprehensive tool for understanding and addressing the complexities of cancer stem cells. Ultimately, OSCORP opens up new possibilities for more effective personalized cancer therapies and holds the potential to serve as a clinical tool for monitoring patient-specific CSC changes during treatment or follow-up care.

cancer biology↗

bioRxiv2023

Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification

The Integrative Clusters (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct genomic subtypes based on DNA copy number and gene expression. Current classifiers achieve only low accuracy without gene expression data, warranting the development of new approaches to copy-number-only-based IntClust classification. A novel XGBoost-driven classification algorithm, CopyClust, was trained using genomic features from METABRIC and validated on TCGA achieving a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy.

genomics↗

bioRxiv2023

Pitfalls in performing genome-wide association studies on ratio traits

Genome-wide association studies (GWAS) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator-driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

genetics↗

bioRxiv2023

Tissue-resident NK cells support survival in pancreatic cancer through promotion of cDC1-CD8T activity

The immunosuppressive microenvironment in pancreatic ductal adenocarcinoma (PDAC) prevents tumor control and strategies to restore anti-cancer immunity (i.e. by increasing CD8 T cell activity) have had limited success. Here we demonstrate how inducing localized physical damage using ionizing radiation (IR) unmasks the benefit of immunotherapy by increasing tissue-resident NK (trNK) cells that support CD8 T activity. Our data confirms that targeting mouse orthotopic PDAC tumors with IR together with CCR5 inhibition and PD1 blockade reduces E-cadherin positive tumor cells by recruiting a hypoactive NKG2D-ve NK population, phenotypically reminiscent of trNK cells, that supports CD8 T cell involvement. We show an equivalent population in human PDAC cohorts that represents immunomodulatory trNK cells that could similarly support CD8 T cell levels in a cDC1-dependent manner. Importantly, a trNK signature associates with survival in PDAC and solid malignancies revealing a potential beneficial role for trNK in improving adaptive anti-tumor responses and supporting CCR5i/PD1 and IR-induced damage as a novel therapeutic approach.

cancer biology↗

bioRxiv2023

Nucleo-cytoplasmic environment modulates spatio-temporal p53 phase separation

Phase separation of various transcription factors and nucleic acids into biomolecular condensates is known to play an essential role in the regulation of gene expression. Here, we show that p53, a tumor suppressor and transcription factor, phase separates and forms biomolecular condensates in the nucleus of cancer cells as well as when overexpressed in the various cell lines. Although the nuclear condensates of wild-type (WT) p53 maintain their liquid state and are able to bind DNA, cancer-associated mutations not only promote misfolding but also partially rigidify the p53 condensates, which are unable to bind the DNA. Irrespective of WT or mutant form, the cytoplasmic partitioning of p53 with time also results in biomolecular condensate formation, which eventually undergoes rigidification. In vitro, WT p53 core domain (p53C) forms biomolecular condensates, which rigidify with time and the process is further promoted by cancer-associated mutations. Both RNA and non-specific DNA promote LLPS of p53C, but specific DNA promotes the dissolution of p53C condensates. The result suggests that the cellular microenvironment regulates p53 LLPS, material property and its functions.

biophysics↗

bioRxiv2023

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease

Almost every recent Alzheimers disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

genetics↗

bioRxiv2023

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states. Author summarySimultaneous ensemble spiking is hypothesized to have important roles in neural encoding; however, neurons can also spike simultaneously by chance. In order to characterize the potentially time-varying higher-order correlational structure of ensemble spiking, we propose an adaptive greedy filtering algorithm that estimates the rate of all reliably-occurring simultaneous ensemble spiking events. Moreover, we propose an accompanying statistical inference framework to distinguish the chance occurrence of simultaneous spiking events from coordinated higher-order spiking. We demonstrate the proposed methods accurately differentiate coordinated simultaneous spiking from chance occurrences in simulated data. In application to human and rat cortical data, the proposed methods reveal time-varying dynamics in higher-order coordination that coincide with changing brain states.

neuroscience↗

bioRxiv2023

Calling site preference reduces masking interference of acoustic signals among sympatric bush frogs and facilitates coexistence

The degree of signal overlap among sympatric species strongly influences the efficiency of intraspecific communication. This phenomenon is particularly pronounced among species that rely heavily on acoustic communication for locating mates and defending territories. Signal interference presents the classic cocktail party problem and, hence, can pose challenges for these species as it can likely hinder the detection, recognition, and localisation of conspecifics. To overcome this challenge, sympatric species are expected to partition their acoustic space to minimise masking interference. We studied a group of sympatric and closely related endemic frogs in coffee plantations of the Western Ghats, India. This community is active for a limited period during the monsoon and, therefore, has a restricted breeding season. Acoustic communication during the breeding season is integral to these frog species, making them an ideal system to investigate possible strategies adopted by a community to minimise masking interference. Using observational data on the vertical height of calling sites and acoustics data, we show that the frog assemblage can partition in multidimensional trait axes, such as call frequency and space. In addition, a novel coexistence strategy emerges when combining these results, where frogs with similar acoustic parameters partition more in their space use. Our results broadly suggest that competition for acoustic space can drive signal and space-use partitioning and that vertical call site selection can enhance the minimisation of masking interference. Our study is among the few studies that test spatial stratification in conjunction with spectral stratification among coexisting species, and to the best of our knowledge, the first on amphibians.

ecology↗

bioRxiv2023

Thermodynamic Forces from Protein and Water Govern Condensate Formation of an Intrinsically Disordered Protein Domain

Liquid-liquid phase separation (LLPS) can drive a multitude of cellular processes by compartmentalizing biological cells via the formation of dense liquid biomolecular condensates, which can function as membraneless organelles. Despite its importance, the molecular-level understanding of the underlying thermodynamics of this process remains incomplete. In this study, we use atomistic molecular dynamics simulations of the low complexity domain (LCD) of human fused in sarcoma (FUS) protein to investigate the contributions of water and protein molecules to the free energy changes that govern LLPS. Both protein and water components are found to have comparably sizeable thermodynamic contributions to the formation of FUS condensates. Moreover, we quantify the counteracting effects of water molecules that are released into the bulk upon condensate formation and the waters retained within the protein droplets. Among the various factors considered, solvation entropy and protein interaction enthalpy are identified as the most important contributions, while solvation enthalpy and protein entropy changes are smaller. These insights provide detailed molecular insights on the intricate thermodynamic interplay between protein- and solvation-related forces underlying the formation of biomolecular condensates.

biophysics↗