Biology subjects

Sarkar, H.

Publications and source records attributed to Sarkar, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Engaging the host adaptive response to nutrient limitation delays cancer cachexia

Cancer cachexia is a debilitating wasting syndrome associated with involuntary weight loss, reduced appetite and food intake, adipose and muscle atrophy, and systemic inflammation with induction of a myriad of candidate cachexia factors. Using a multi-omic approach in a genetically engineered mouse model of lung cancer cachexia, we found that early circulating metabolic alterations preceded overt weight loss. Although both cachexia and fasting are characterized by a reduction in food intake, we show that the systemic metabolic changes in cachexia are distinct, indicating a lack of induction in the normal protective response to negative energy balance during cachexia. Cancer cachexia causes profound hepatic reprogramming, and activation of inflammatory programs, alongside muscle atrophy and elevation of circulating cytokines. However, targeting individual candidate cachectic factors such as IL-6, TNF-, GDF15, CCL2, LIF, and IL17A failed to rescue weight loss or prolong survival. In contrast, caloric restriction initiated prior to tumor induction elicited a program of protection during tumor progression that attenuated multi-tissue inflammatory and catabolic transcriptional responses, preserved lean mass, and prolonged survival. Together, these findings suggest that metabolic preconditioning broadly dampens systemic inflammation and delays the onset and progression of cancer cachexia.

cancer biology↗

bioRxiv2026

An Indole Dimer Antifungal Metabolite from a Rice Seed Endophyte Inhibits Ergosterol Biosynthesis in Fungal Pathogens

The increasing prevalence of fungal phytopathogens and the widespread emergence of fungicide resistance necessitate alternative antifungal strategies with reduced environmental impact. Here, we report the isolation and characterization of a novel antifungal metabolite, SM06, produced by the rice seed-associated endophytic bacterium Phytobacter sp. RSE02. SM06 exhibited broad-spectrum antifungal activity against plant and human pathogenic fungi, including Curvularia lunata, Fusarium oxysporum, and Candida albicans. In vitro assays and micromorphological analyses revealed that SM06, an indole dimer, disrupts fungal cell membrane integrity, while in planta experiments demonstrated significant suppression of brown leaf spot disease in tomato and rice. Molecular docking suggested that SM06 binds to lanosterol 14-demethylase (ERG11), a key enzyme in fungal sterol biosynthesis. Consistent with this prediction, LC-MS-based analyses confirmed a significant reduction in ergosterol content in SM06-treated fungal cells. Together, these findings identify SM06 as a biologically active antifungal metabolite produced by a plant-associated bacterium and highlight its potential application in sustainable fungal disease management. IMPORTANCEFungal diseases cause major losses in crop-production and contribute to the growing challenge of antifungal resistance, underscoring the need for sustainable alternatives to chemical fungicides. This study identifies SM06, a novel indole dimer produced by the rice seed endophyte Phytobacter sp. RSE02, with strong antifungal activity against economically important plant pathogens and clinically relevant fungi. Through integrated chemical, cellular, and in planta analyses, we demonstrate that SM06 disrupts fungal membrane integrity by inhibiting ergosterol biosynthesis. The compound is biocompatible, stable, and effective in plant disease suppression, highlighting its translational potential for crop protection. These findings reveal seed endophytes as an important yet underexplored sources of antifungal metabolites and provide a mechanistic foundation for developing eco-friendly biocontrol strategies with implications beyond agriculture. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/697688v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@c7da68org.highwire.dtl.DTLVardef@d3f1a1org.highwire.dtl.DTLVardef@9cab82org.highwire.dtl.DTLVardef@1c01141_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation

Computational simulation and data augmentation of spatial transcriptomics (ST) are essential for quantitative benchmarking, reproducibility, and methodological innovation. Yet current models often lack flexibility in controlling spatial and transcriptional heterogeneity, fail to capture higher-order gene dependencies, and rarely extend to three-dimensional or alignment-aware contexts. Here we present FEAST, a computational infrastructure that models ST data within a parameter cloud - a latent manifold encoding gene-level mean, variance, and sparsity. By sampling and perturbing this manifold, FEAST generates high-fidelity synthetic slices with tunable spatial and transcriptional variation, enabling systematic evaluation of clustering, deconvolution, and spatial alignment algorithms. Beyond two dimensions, FEAST performs 3D parameter-cloud interpolation guided by optimal transport and benchmark alignment, reconstructing continuous tissue architectures while preserving molecular coherence. Together, these capabilities establish FEAST as a foundational platform for standardized benchmarking, data augmentation, and 3D reconstruction in spatial transcriptomics. The source code and tutorials for FEAST are publicly available at https://github.com/maiziezhoulab/FEAST, and can be installed via Pypi at https://pypi.org/project/FEAST-py/.

bioinformatics↗

bioRxiv2025

Niche Macrophages Recycle Iron to Tumor Cells and Foster Erythroblast Mimicry to Promote Bone Metastasis and Anemia

Bone marrow is both a primary site for blood cell production and a fertile niche for metastatic cancer cell growth, notably in breast cancer. Although anemia is common among patients with bone metastasis, the mechanistic link between metastatic colonization and disrupted erythropoiesis remains poorly understood. Using in vivo niche labeling and single-cell RNA sequencing, we identified a specialized population of VCAM1+CD163+CCR3+ macrophages enriched in the bone metastatic niche. These macrophages, typically essential for erythropoiesis in healthy bone marrow, are co- opted by tumor cells to support their growth through iron acquisition. The hijacking of these macrophages by tumor cells reduces iron availability for erythroblasts, impairing erythropoiesis and contributing to anemia. With increased iron supply, tumor cells further adapt by mimicking erythroblasts, producing hemoglobin under GATA1 regulation in response to hypoxic stress. Notably, macrophages with similar iron- regulating features were found in human bone metastases across multiple cancer types, and elevated HBB expression in breast cancer correlates with increased risk of bone metastasis. These findings establish iron-recycling macrophages as essential regulators within the metastatic bone niche, revealing novel insights into the interplay between immune modulation, metal metabolism and tumor cell plasticity in driving metastatic progression and anemia.

cancer biology↗

bioRxiv2025

NOR1 and Mitophagy: An Insight into Sertoli Cell Function Regulating Spermatogenesis

The development and differentiation of germ cell (Gc) is supported by the Sertoli cel (Sc). We previously reported that a developmental switch in Sc around 12 days of age onwards in rats. During the process of differentiation of Sc, the differential expression of mitophagy-related genes and its role in male fertility is poorly understood. To address this gap, we evaluated the microarray dataset GSE48795 to identify 12 mitophagy-related hub genes - Egfr, Bcl2, Ccl2, Mmp2, Igf1, Fgf7, Apoe, Fos, Cxcl12, Ocln, Dcn, and Snca. We identify NOR1 as a potential mitophagy-related gene of interest due to its strong regulatory association with two hub genes Bcl2 and Fos. To determine the importance of our analysis of finding key mitophagy-related genes, we generated a transgenic rat model with Sc-specific knockdown of NOR1 during puberty. Adult male transgenic rats were found to be infertile due to impaired spermatogenesis, indicating a critical role for NOR1 in Sc function. These findings suggest that mitophagy-related genes may play an important role in the differentiation of Sc and thereby, regulate male fertility.

cell biology↗

bioRxiv2025

Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms

Spatially resolved transcriptomics (SRT) technologies measure gene expression across thousands of spatial locations within a tissue slice. Multiple SRT technologies are currently available and others are in active development with each technology having varying spatial resolution (subcellular, single-cell, or multicellular regions), gene coverage (targeted vs. whole-transcriptome), and sequencing depth per location. For example, the widely used 10x Genomics Visium platform measures whole transcriptomes from multiple-cell-sized spots, while the 10x Genomics Xenium platform measures a few hundred genes at subcellular resolution. A number of studies apply multiple SRT technologies to slices that originate from the same biological tissue. Integration of data from different SRT technologies can overcome limitations of the individual technologies enabling the imputation of expression from unmeasured genes in targeted technologies and/or the deconvolution of ad-mixed expression from technologies with lower spatial resolution. We introduce Spatial Integration for Imputation and Deconvolution (SIID), an algorithm to reconstruct a latent spatial gene expression matrix from a pair of observations from different SRT technologies. SIID leverages a spatial alignment and uses a joint non-negative factorization model to accurately impute missing gene expression and infer gene expression signatures of cell types from ad-mixed SRT data. In simulations involving paired SRT datasets from different technologies (e.g., Xenium and Visium), SIID shows superior performance in reconstructing spot-to-cell-type assignments, recovering cell-type-specific gene expression, and imputing missing data compared to contemporary tools. When applied to real-world 10x Xenium-Visium pairs from human breast and colon cancer tissues, SIID achieves highest performance in imputing holdout gene expression. A PyTorch implementation of SIID is available at https://github.com/raphael-group/siid.

bioinformatics↗

bioRxiv2024

Dominant RDH12-retinitis pigmentosa impairs photoreceptor development and cone function in retinal organoids

Retinal dehydrogenase 12 (RDH12) is a photoreceptor NADPH-dependent retinal reductase enzyme, converting all-trans-retinal to all-trans-retinol. Heterozygous variants in RDH12 cause a rare autosomal dominant (AD) retinitis pigmentosa. As no disease models exist, we generated human induced pluripotent stem cell derived retinal organoids (RO) from a RDH12-AD patient (with pathogenic c.759del p.(Phe254Leufs*24) variant), alongside a healthy control (WT). RDH12-AD RO exhibited correct localisation of RDH12 to the photoreceptor inner segments up to week 44; transmission electron microscopy at week 37 showed photoreceptors were less abundant and shorter in length compared to WT. Visual cone function, retinol biosynthesis and the vitamin A pathway were also highly disrupted at week 44. Our study is the first to describe a RDH12-AD disease model with pathology at later stages of photoreceptor differentiation, in keeping with the milder disease course seen in humans. It provides insights into the aetiology and possible targets for future therapeutic development.

pathology↗

bioRxiv2024

Generation of a Transgenic Mouse Model for Investigating Mitochondria in Sperm

Mitochondria play a crucial role in the sperm development; however, the mechanisms regulating their function in sperm remain poorly understood. Developing a method to regulate the expression of a target gene within the mitochondria of sperm is a vital step in this area of research. In this study, we aimed to create a system for expressing a transgene in the mitochondria of sperm. As a proof of concept, we generated transgenic mice that express green fluorescent protein (GFP) fused with a mitochondrial localization signal (MLS) driven by the phosphoglycerate kinase 2 (PGK2) promoter, which facilitates the transgene expression in the sperm. Although the PGK2 promoter has previously shown to drive gene expression in spermatocytes and spermatids, the novelty of our approach lies in the combination of PGK2-driven MLS-GFP expression to study mitochondria in vivo. We established two founder lines of transgenic mice through pronuclear microinjection, and MLS-GFP expression was confirmed in the mitochondria of sperm cells using fluorescence microscopy and flow cytometry. Consequently, we provide a novel platform for investigating mitochondrial function in sperm, where GFP can be substituted with other genes of interest to examine their effects on mitochondria. This system specifically targets sperm mitochondria, offering an innovative approach for studying mitochondrial function in vivo.

cell biology↗

bioRxiv2023

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice--analogous to a map of elevation in a landscape--using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

bioinformatics↗

bioRxiv2023

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

MotivationAlthough transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. ResultsHere, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementationDifferentialRegulation is distributed as a Bioconductor R package.

bioinformatics↗

bioRxiv2023

Loss of REP1 impacts choroidal melanogenesis in choroideremia

Choroideremia (CHM) is a rare X-linked chorioretinal dystrophy affecting the photoreceptors, retinal pigment epithelium (RPE) and choroid, however, the involvement of the choroid in disease progression is not fully understood. CHM is caused by mutations in the CHM gene, encoding the ubiquitously expressed Rab escort protein 1 (REP1). REP1 plays an important role in intracellular trafficking of vesicles, including melanosomes. In this study, we examined ultrastructure of the choroid in chmru848 fish and Chmnull/WT mouse models using transmission electron microscopy. Significant pigmentary disruptions were observed, with a lack of melanosomes in the choroid of chmru848fish from 4 days post fertilisation (4dpf). Total melanin and expression of melanogenesis genes tyr, tryp1a, mitf, dct and pmel were also reduced from 4dpf. In Chmnull/WT mice, choroidal melanosomes were significantly smaller at 1 month and at 1 year, eumelanin was reduced, and the choroid was thicker. The choroid in CHM patients was also examined using optical coherence tomography (OCT) and OCT- angiography (OCT-A) and the area of preserved choriocapillaris (CC) was found to be smaller than that of overlying photoreceptors, indicating that the choroid is degenerating at a faster rate. Histopathology of an enucleated eye from a 74-year-old CHM male patient revealed isolated areas of RPE but no associated underlying CC. Significant degenerative changes in the choroid of CHM patients and animal models are seen, highlighting the importance of administrative routes involving the choroid, such as suprachoroidal delivery. Pigmentary disruptions in CHM animal models reveal an important role for REP1 in melanogenesis, and drugs that improve melanin production represent a potential novel therapeutic avenue.

molecular biology↗

bioRxiv2022

Integrated single-cell and spatial transcriptomic analyses unravel the heterogeneity of the prostate tumor microenvironment

The treatment of primary prostate cancer delicately balances an active surveillance approach for low-risk disease with multimodal treatment including surgery, radiation therapy, and hormonal therapy for high-risk disease. Recurrence and development of metastatic disease remains a clinical problem, without a clear understanding of what drives immune escape and tumor progression. Here, we sought to comprehensively describe the tumor microenvironment of localized prostate cancer contrasting this with adjacent normal samples and healthy controls. We performed single-cell RNA sequencing and high-resolution spatial transcriptomic analysis. This revealed tumor context dependent changes in gene expression. Our data point towards an immune suppressive tumor microenvironment associated with suppressive myeloid populations and exhausted T-cells, in addition to high stromal angiogenic activity. We inferred cell-to-cell relationships at an unprecedented scale for ligand-receptor interactions within undissociated tissue sections. Our work provides a highly detailed and comprehensive resource of the prostate tumor microenvironment as well as tumor-stromal cell interactions. HighlightsO_LICharacterization of prostate cancer by combined scRNA-seq and spatial transcriptomic analysis C_LIO_LIPrimary prostate cancer establishes a suppressive immune microenvironment C_LIO_LIThe prostate tumor microenvironment exhibits a high angiogenic gene expression pattern C_LIO_LIA new computational analysis pipeline to deconvolute context-specific differential gene expression C_LI

cancer biology↗

bioRxiv2022

Haplotype-enhanced inference of somatic copy number profiles from single-cell transcriptomes

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of CNVs from scRNA-seq data. To resolve tumor clonal architecture, Numbat exploits the evolutionary relationships between subclones to iteratively infer the single-cell copy number profiles and tumor clonal phylogeny. Analyzing 21 tumor samples composed of multiple myeloma, breast, and thyroid cancers, we show that Numbat can accurately reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We uncover additional subclonal complexity contributed by allele-specific alterations, and identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. We hope that the increased power to characterize genomic aberrations and tumor subclonal phylogenies provided by Numbat will help delineate contributions of genetic and non-genetic mechanisms in cancer.

bioinformatics↗

bioRxiv2021

Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets

MotivationAllelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial-, or time-dependent AI signals may be dampened or not detected. ResultsWe introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing (scRNA-seq) data, or other spatially- or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower RMSE of allelic ratio estimates than existing methods. In real data, airpart identified differential AI patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AvailabilityThe airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

bioinformatics↗

bioRxiv2021

Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data

The rapid growth of high-throughput single-cell and single-nucleus RNA sequencing technologies has produced a wealth of data over the past few years. The available technologies continue to evolve and experiments continue to increase in both number and scale. The size, volume, and distinctive characteristics of these data necessitate the development of new software and associated computational methods to accurately and efficiently quantify single-cell and single-nucleus RNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying single-cell and single-nucleus RNA-seq data. Despite being faster and more memory frugal than other accurate and scalable quantification approaches, alevin-fry does not suffer from the false positive expression or memory scalability issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify single-cell and single-nucleus RNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same pre-processed data used to generate regular gene expression count matrices.

bioinformatics↗

bioRxiv2021

A like-for-like comparison of lightweight-mapping pipelines for single-cell RNA-seq data pre-processing

Recently, Booeshaghi and Pachter (1) published a benchmark comparing the kallisto-bustools pipeline (2) for single-cell data pre-processing to the alevin-fry pipeline (3). Their benchmarking adopted drastically dissimilar configurations for these two tools, and overlooked the time- and space-frugal configurations of alevin-fry previously benchmarked by Sarkar et al. (3). In this manuscript, we provide a small set of modifications to the benchmarking scripts of Booeshaghi and Pachter that are necessary to perform a like-for-like comparison between kallisto-bustools and alevin-fry. We also address some misuses of the alevin-fry commands and include important data on the exact reference transcriptomes used for processing1. Using the same benchmarking scripts of Booeshaghi and Pachter (1), we demonstrate that, when configured to match the computational com-plexity of kallisto-bustools as closely as possible, alevin-fry processes data faster (~2.08 times as fast on average) and uses less peak memory (~ 0.34 times as much on average) compared to kallisto-bustools, while producing results that are similar when assessed in the manner done by Booeshaghi and Pachter (1). This is a notable inversion of the performance characteristics presented in the previous benchmark.

bioinformatics↗

bioRxiv2021

Accounting for fragments of unexpectedorigin improves transcript quantification inRNA-seq simulations focused on increased realism

Transcript and gene quantification is the first step in many RNA-seq analyses. While many factors and properties of experimental RNA-seq data likely contribute to differences in accuracy between various approaches to quantification, it has been demonstrated (1) that quantification accuracy generally benefits from considering, during alignment, potential genomic origins for sequenced fragments that reside outside of the annotated transcriptome. Recently, Varabyou et al. (2) demonstrated that the presence of transcriptional noise leads to systematic errors in the ability of tools -- particularly annotation-based ones -- to accurately estimate transcript expression. Here, we confirm the findings of Varabyou et al. (2) using the simulation framework they have provided. Using the same data, we also examine the methodology of Srivastava et al.(1) as implemented in recent versions of salmon (3), and show that it substantially enhances the accuracy of annotation-based transcript quantification in these data.

bioinformatics↗

bioRxiv2020

Compression of quantification uncertainty for scRNA-seq counts

Motivation Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of “inferential replicates”, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.Results We demonstrate that storing only the mean and variance from a set of inferential replicates (“compression”) is sufficient to capture gene-level quantification uncertainty. Using these values, we generate “pseudo-inferential” replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. We show reduced false positives when applying this procedure to trajectory-based differential expression analyses. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory consumption without any loss in performance. Lastly, we show that the removal of multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.Availability and implementation makeInfReps and splitSwish are implemented in the development branch of the R/Bioconductor fishpond package available at http://bioconductor.org/packages/devel/bioc/html/fishpond.html. Sample code to calculate the uncertainty-aware p-values can be found on GitHub at https://github.com/skvanburen/scUncertaintyPaperCode.Contact michaelisaiahlove{at}gmail.comCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗