Biology subjects

Sarkar, A. K.

Publications and source records attributed to Sarkar, A. K..

8 recordsLinked to original sources

bioRxiv2025

A cytokinin-auxin antagonistic module regulates nitrogen-triggered tiller outgrowth in rice

Tillering is a key trait that shapes aboveground architecture and directly contributes to rice yield. However, integration of environmental cues to genetic regulators for controlling tiller outgrowth remains poorly understood. We investigated the effects of nitrogen, one of the critical environmental factors, on the early stages of tiller bud outgrowth. Comprehensive temporal transcriptomic analyses in response to two nitrogen forms, nitrate (NO -) and ammonium (NH +), identified gene-regulatory networks for nitrogen-triggered tiller outgrowth, with phytohormone signaling as the central interface. Pharmacological, molecular, and genetic experiments established cytokinin-auxin antagonism as a central regulator of the nitrogen-mediated tillering. Cytokinin promoted bud outgrowth by repressing the TCP transcription factor OsTB1, the key inhibitor of tillering, via Cytokinin Response Factors OsERF53/54. OsERF53/54 further reduced polar auxin transport by repressing OsPIN1a to promote bud activation. Conversely, auxin promoted bud dormancy by inducing OsTB1 and OsPIN1a via OsARF11/16. Both NO - and NH + triggered tiller outgrowth and induced largely overlapping transcriptional responses, with a temporal delay for NO -. Adequate nitrogen supplies induced cytokinin signaling while inhibiting auxin signaling and transport in tiller buds, thereby triggering bud outgrowth. Together, we identify a key regulatory role of auxin-cytokinin antagonistic module for integrating nitrogen signals to determine tiller bud fate.

plant biology↗

bioRxiv2024

m6A-mediated epi-transcriptomic dysregulation underlies synaptic dysfunction in fragile X syndrome

Fragile X syndrome (FXS), the leading genetic cause of intellectual disability, arises from FMR1 gene silencing and loss of the FMRP protein. N6-methyladenosine (m6A) is a prevalent mRNA modification essential for post-transcriptional regulation. FMRP is known to bind to and regulate the stability of m6A-containing transcripts. However, how loss of FMRP impacts on transcriptome-wide m6A modifications in FXS patients remains unknown. To answer this question, we generated cortical neurons differentiated from induced pluripotent stem cells (iPSC) derived from healthy subjects and FXS patients. In electrophysiology recordings, we validated that synaptic and neuronal network defects in iPSC-derived FXS neurons corresponded to the clinical EEG data of the patients from which the corresponding iPSC line was derived. In analysis of transcriptome-wide methylation, we show that FMRP deficiency led to increased translation of m6A writers, resulting in hypermethylation that primarily affecting synapse-associated transcripts and increased mRNA decay. Conversely, in the presence of an m6A writer inhibitor, synaptic defects in FXS neurons were rescued. Taken together, our findings uncover that an FMRP-dependent epi-transcriptomic mechanism contributes to FXS pathogenesis by disrupting m6A modifications in FXS, suggesting a promising avenue for m6A- targeted therapies.

neuroscience↗

bioRxiv2021

MicroRNA775 and target Galactosyltransferase (GALT9) module regulates recovery from submergence induced senescence by modulating SAGs in Arabidopsis thaliana

Submergence induced hypoxic condition is one of the abiotic stresses which negatively affects the plant growth and development, and causes early onset of senescence. Hypoxic conditions ateres the expression of a number of non-coding microRNAs (miRNAs), besides protein-coding genes. However, the molecular function of stress-induced miRNA in submergence induced physiological or developmental changes and recovery remains to be understood. The expression of miR775 is highly induced under hypoxic stress conditions. Here, we show that miR775 is a potential post-transcriptional regulator number of targets, including Galactosyltransferase (GALT9). The expression of miR775 and target GALT9 was significantly induced and reduced respectively at 24 hours of submergence. The overexpression of miR775 (miR775-Oe) confers enhanced recovery from submergence stress and reduced accumulation of ROS, in contrast to wild type and endogenous target mimic of miR775 (MIM775) Arabidopsis plants. We observed a similar recovery phenotype in case of target galt9 mutant plants, indicating the role of miR775-GALT9 module in recovery from submergence. Further, we showed that the expression of SENESCENCE ASSOCIATED GENES (SAGs), such as SAG12, SAG29, and ORE1. was increased in MIM775 and reduced in miR775-Oe and galt9 plants. Thus, our results suggest that miR775-GALT9 module plays a crucial role in the recovery from submergence by modulating the expression of SAGs through differential accumulation of ROS.

plant biology↗

bioRxiv2021

Electrophysiological maturation and increased excitability of human iPSC-derived neurons in HTR2A variant-related sleep bruxism

Sleep bruxism (SB) is a sleep-related movement disorder characterized by grinding and clenching of the teeth during sleep. We previously found a significant association between SB and a single nucleotide polymorphism (SNP), rs6313, in the neuronal serotonin 2A receptor gene (HTR2A), and established human induced pluripotent stem cell (hiPSC)-derived neurons from SB patients with a genetic variant. To elucidate the electrophysiological characteristics of SB iPSC-derived neural cells bearing a SB-related genetic variant, we generated ventral hindbrain neurons from two SB patients and two unaffected controls and explored the intrinsic membrane properties of these neurons by patch-clamp technique. We found that the electrophysiological properties of the iPSC-derived neurons from the control line mature in a time-dependent manner in long-term cultures. In the early stage of neurogenesis, neurons from two SB lines tended to display shorter action potential (AP) half durations, which led to an increased cell capability of evoked firing. This is the first in vitro modelling of SB using disease-specific hiPSCs. The revealed electrophysiological characteristics may serve as a benchmark for further investigation of pathogenic mechanisms underlying SB. Summary StatementSleep bruxism patient-specific iPSC-derived neurons with the HTR2A variant show altered electrophysiological characteristics, providing the foremost narration of sleep bruxism neurological phenotypes in vitro from any species.

neuroscience↗

bioRxiv2020

Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis

AO_SCPLOWBSTRACTC_SCPLOWThe high proportion of zeros in typical scRNA-seq datasets has led to widespread but inconsistent use of terminology such as "dropout" and "missing data". Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help reduce confusion. These include: (1) observed scRNA-seq counts reflect both true gene expression levels and measurement error, and carefully distinguishing these contributions helps clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps address questions of biological interest, such as whether mRNA expression levels are multimodal among cells.

genomics↗

bioRxiv2018

A simple new approach to variable selection in regression, with application to genetic fine-mapping

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model -- the "Sum of Single Effects" (SuSiE) model -- which comes from writing the sparse vector of regression coefficients as a sum of "single-effect" vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure -- Iterative Bayesian Stepwise Selection (IBSS) -- which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a Credible Set of variables for each selection. Our methods are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outper-form existing methods for this task, and illustrate their application to fine-mapping genetic variants influencing alternative splicing in human cell-lines. We also discuss the potential and challenges for applying these methods to generic variable selection problems.

bioinformatics↗

bioRxiv2018

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 424 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.\n\nAuthor summaryCommon genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

genomics↗

bioRxiv2017

Multi-tissue polygenic models for transcriptome-wide association studies

I.Transcriptome-wide association studies (TWAS) have proven to be a powerful tool to identify genes associated with human diseases by aggregating cis-regulatory effects on gene expression. However, TWAS relies on building predictive models of gene expression, which are sensitive to the sample size and tissue on which they are trained. The Gene Tissue Expression Project has produced reference transcriptomes across 53 human tissues and cell types; however, the data is highly sparse, making it difficult to build polygenic models in relevant tissues for TWAS. Here, we propose fQTL, a multi-tissue, multivariate model for mapping expression quantitative trait loci and predicting gene expression. Our model decomposes eQTL effects into SNP-specific and tissue-specific components, pooling information across relevant tissues to effectively boost sample sizes. In simulation, we demonstrate that our multi-tissue approach outperforms single-tissue approaches in identifying causal eQTLs and tissues of action. Using our method, we fit polygenic models for 13,461 genes, characterized the tissue-specificity of the learned cis-eQTLs, and performed TWAS for Alzheimers disease and schizophrenia, identifying 107 and 382 associated genes, respectively.

genetics↗