Biology subjects

Pillai, M.

Publications and source records attributed to Pillai, M..

12 recordsLinked to original sources

bioRxiv2025

A Unifying Mechanism for Shared Splicing Aberrations in Splicing Factor Mutant Cancers

Cancer-associated splicing factor (SF) mutations in SF3B1, U2AF1, and SRSF2 induce distinct changes in alternative splicing (AS). Yet these mutations are strikingly mutually exclusive, pointing to a convergent downstream mechanism. We hypothesized this would be reflected in the AS transcriptome. By analyzing transcriptomes of 395 patients with clonal myeloid disorders and 64 healthy donors, we found most AS alterations to be mutation-specific. However, a robust subset, enriched in the retained intron (RI) program, was shared across mutants. These RI events were bidirectional but highly concordant, and mirrored the effects of SRSF1 loss. SF-mutant states induced hypophosphorylation of RS domains in SRSF1, reducing its function. This arose from an altered AMPK-AKT balance impairing the AKT-SRPK1-SRSF1 axis. A common upstream trigger was activation of DNA damage response (DDR) by transcriptional R-loops, which increased AMPK signaling and reduced AKT activity. Pharmacologic DDR activation recapitulated reduced AKT/SRPK1 activity and SRSF1 hypophosphorylation, while relieving DDR restored SRSF1 phosphorylation and corrected RI defects. Thus, beyond cis-acting, mutation-specific changes, SF-mutant cancers share a trans-acting, stress-driven AS signature wherein DDR signaling rewires SRSF1 activity impacting AS. Our results link replication stress, kinase signaling, and RNA processing across genetically diverse clonal states, highlighting potential therapeutic approaches at these nodes. HighlightsO_LIWhile most splicing changes differ by splicing factor (SF) mutation, certain retained introns are common across subtypes. C_LIO_LIChanges in RI are bidirectional, concordant across mutant groups, and mirrors SRSF1 loss. C_LIO_LISF mutations activate DDR, triggering an AMPK/AKT imbalance that culminates in SRSF1 hypophosphorylation. C_LIO_LIRelieving R-loop induced DDR restores SRSF1 phosphorylation and reverses RI. C_LI

cancer biology↗

bioRxiv2025

A principled approach to the identification of biologically relevant transcription factor binding sites

Transcription factors (TF) regulate gene expression in cells by binding to specific DNA sequences. However, previous studies indicate that TF binding is a necessary but not sufficient condition for regulating gene expression.1-3 While recent advances in sequencing technologies such as Chromatin Immunoprecipitation sequencing (ChIP-seq) enable researchers to identify binding sequences at single nucleotide resolution, they often fail to distinguish between biologically relevant and non-relevant - i.e., spurious - binding events. Reliably distinguishing between these two types of binding is essential if one aims for reproducible findings. Here, we report on a systematic study of the sensitivity of "called" TF-target gene interactions to the choice of various data processing parameters. We find that acceptable distance from the transcription start site (TSS) is a critical parameter for reducing subjectivity. We, thus, develop a two-state mechanistic model that captures the positional distribution of both biologically relevant and spurious binding events, allowing us to identify interaction distance thresholds that minimize spurious binding. We validate our model with independent data by showing that biologically relevant binding events identified by our model are capable of recruiting RNA Polymerase II and, subsequently, regulating mRNA expression levels of the target gene. Finally, we investigate the impact of our approach on the detection of genes that are differentially bound and regulated by BCL3, a transcription coactivator, during molecular perturbation experiments. Within the genes expected to show differential expression, our model predicts that over 70% of reported interactions are spurious and we demonstrate the lack of change in mRNA expression in these genes following perturbation. We also identify genes that display a shift from spurious to biologically relevant binding that account for close to 17% of the genes displaying a sharp change in mRNA expression and strong association with known functions of BCL3 such as cell cycle regulation and DNA damage response. By providing a systematic TF-specific method to identify binding events that are directly involved in transcriptional regulation our model will increase reproducibility of analysis.

genomics↗

bioRxiv2025

Transient Poly(ADP-Ribose) Triggers FUS Condensation Hysteresis via a Prion-Like Mechanism

Hysteresis--where a system retains memory of a transient stimulus--is common in signaling but can also arise in intracellular organization. DNA repair foci, a type of biomolecular condensate, are initiated by the short-lived noncanonical nucleic acid poly(ADP-ribose) (PAR). PAR recruits proteins with prion-like domains (PrLDs), such as Fused in Sarcoma (FUS), and initiates their condensation, which persists even after PAR degradation. How FUS transitions from PAR-dependent to PAR-independent condensation remains unclear. Here, we show that PAR binding triggers a conformational switch in FUS, enabling sustained condensation. PAR binds to the C-terminal arginine-rich region of FUS, displacing intramolecular contacts, and exposing the N-terminal PrLD. This conformational opening allows PrLD interactions in trans, stabilizing condensates independently of PAR. FUS thus undergoes a regulated, nucleated conformational conversion--reminiscent of classical prions. This mechanism implies a paradigm of nucleic acid-induced conformational memory that may underlie hysteresis in intracellular organization in health and disease. HIGHLIGHTS[bullet] PAR-initiated FUS condensation follows a bi-modular mechanism involving both FUS termini. [bullet]Simulations predict and experiments confirm PAR disrupts FUS intramolecular contacts. [bullet]Upon condensation, FUS adopts a conformation with its N-terminus open for interactions. [bullet]N-terminal interactions maintain FUS condensation when PAR degrades during DNA repair.

molecular biology↗

bioRxiv2023

Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.

systems biology↗

bioRxiv2023

The RNA-binding protein Imp specifies olfactory navigation circuitry and behavior in Drosophila

Complex behaviors depend on the precise developmental specification of neuronal circuits, but the relationship between genetic programs for neural development, circuit structure, and behavioral output is often unclear. The central complex (CX) is a conserved sensory-motor integration center in insects that governs many higher order behaviors and largely derives from a small number of Type II neural stem cells. Here, we show that Imp, a conserved IGF-II mRNA-binding protein expressed in Type II neural stem cells, specifies components of CX olfactory navigation circuitry. We show: (1) that multiple components of olfactory navigation circuitry arise from Type II neural stem cells and manipulating Imp expression in Type II neural stem cells alters the number and morphology of many of these circuit elements, with the most potent effects on neurons targeting the ventral layers of the fan-shaped body. (2) Imp regulates the specification of Tachykinin expressing ventral fan-shaped body input neurons. (3) Imp in Type II neural stem cells alters the morphology of the CX neuropil structures. (4) Loss of Imp in Type II neural stem cells abolishes upwind orientation to attractive odor while leaving locomotion and odor-evoked regulation of movement intact. Taken together, our work establishes that a single temporally expressed gene can regulate the expression of a complex behavior through the developmental specification of multiple circuit components and provides a first step towards a developmental dissection of the CX and its roles in behavior.

neuroscience↗

bioRxiv2023

Androgen Receptor driven gene score identifies tumors with Epithelial to Mesenchymal Transition features in triple negative breast cancer

BackgroundAndrogen receptor (AR) is considered marker associated with better prognosis within hormone receptor positive tumors. Its role in triple negative tumors however is controversial showing both better and worse prognosis and different methods are used for identification of AR driven tumors. Conflicting results could be due to intrinsic molecular differences or scoring method for AR positivity. We attempted to develop an AR driven gene score and examined its utility in subtypes of breast cancer (BC). MethodsA bioinformatic pipeline was constructed and applied on publicly available microarray data sets obtained from AR positive BC cell lines treated with dihydrotestosterone (DHT). Expression levels of genes identified through the pipeline along with set of epithelial mesenchymal transition (EMT) markers, proliferation associated genes and enzymes involved in intracrinal androgen metabolism was evaluated in a cohort of Indian Breast cancer patients. Tumors were divided into AR high and low based on the gene score and association with clinical parameters, circulating androgens, disease free survival, proliferation and EMT markers were examined, all results were further validated in external public datasets. ResultsAR driven gene score was calculated as average expression of the 6 genes selected through bioinformatic analysis. 53% (133/249) tumors were classified as AR high by the gene score and had significantly better clinical parameters such as higher age, smaller tumor size, lower grade and lower proliferation. Tumors with high AR driven gene score had significantly better disease-free survival (mean survival time of 86.13 vs 72.69 months, log rank p=0.032) when compared to the AR low tumors. A subset analysis within TNBC (N=66) showed 36% (24/66) were AR high and had significantly higher expression of EMT markers (p=0.024). Though circulating levels of total testosterone was not different between the groups, intratumoral levels of 5 alfa reductase (SRD5A1) was significantly high in tumors with high AR driven score. ConclusionRole of AR in breast cancer is debatable and difficult to decipher with protein detection alone. Our results support the context dependent function of AR in driving better prognosis, while identifying its role in driving EMT within TNBC tumors.

cancer biology↗

bioRxiv2022

Emergent dynamics of underlying regulatory network links EMT and androgen receptor-dependent resistance in prostate cancer

Advanced prostate cancer patients initially respond to hormone therapy, be it in the form of androgen deprivation therapy or second-generation hormone therapies, such as abiraterone acetate or enzalutamide. However, most men with prostate cancer eventually develop hormone therapy resistance. This resistance emerges in several ways, such as through genetic mutations, epigenetic mechanisms, or through non-genetic pathways, such as lineage plasticity along epithelial-mesenchymal or neuroendocrine-like axes. These mechanisms of hormone therapy resistance often co-exist within a single patients tumor and can overlap within a single cell. There exists a growing need to better understand how phenotypic heterogeneity and plasticity results from emergent dynamics of the regulatory networks governing androgen independence. Here, we investigated the dynamics of a regulatory network connecting the drivers of androgen receptor (AR) splice variant-mediated androgen independence and those of epithelial-mesenchymal transition. Model simulations for this network revealed four possible phenotypes: epithelial-sensitive (ES), epithelial-resistant (ER), mesenchymal-resistant (MR) and mesenchymal-sensitive (MS), with the latter phenotype occurring rarely. We observed that well-coordinated "teams" of regulators working antagonistically within the network enable these phenotypes. These model predictions are supported by multiple transcriptomic datasets both at single-cell and bulk levels, including in vitro EMT induction models and clinical samples. Further, our simulations reveal spontaneous stochastic switching between the ES and MR states. Addition of the immune checkpoint molecule, PD-L1, to the network was able to capture the interactions between AR, PD-L1, and the mesenchymal marker SNAIL, which was also confirmed through quantitative experiments. This systems-level understanding of the driver of androgen independence and EMT could aid in understanding non-genetic transitions and progression of such cancers and help in identifying novel therapeutic strategies or targets.

cancer biology↗

bioRxiv2022

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Drug resistance and tumor relapse in melanoma patients is attributed to a combination of genetic and non-genetic mechanisms. Non-genetic mechanisms of drug resistance commonly involve reversible changes in the cell-state or phenotype, i.e., alterations in molecular profiles that can help cells escape being killed by targeted therapeutics. In melanoma, one of the most common mechanisms of non-genetic resistance is dedifferentiation, which is characterized by loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remains poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switch in melanoma based on its underlying regulatory network. These landscapes reveal the existence of multiple alternative paths to resistance - de-differentiation and transition to a hyper-pigmented phenotype. Finally, by visualizing the changes in the landscape during in silico molecular perturbations, we identify combinatorial strategies that can lead to the most optimal outcome - a landscape with the minimal occupancy of the two drug-resistant states. Therefore, we present these landscapes as platforms to screen possible therapeutic interventions in terms of their ability to lead to most favourable patient outcomes.

systems biology↗

bioRxiv2022

Mapping phenotypic heterogeneity in melanoma onto the epithelial-hybrid-mesenchymal axis

Epithelial to mesenchymal transition (EMT) is a well-studied hallmark of epithelial-like cancers that is characterized by loss of epithelial markers and gain of mesenchymal markers. Interestingly, melanoma, which is derived from melanocytes of the skin, also undergo phenotypic plasticity toward mesenchymal-like phenotypes under the influence of various micro-environmental cues. Our study connects EMT to the phenomenon of de-differentiation (i.e., transition from proliferative to more invasive phenotypes) observed in melanoma cells during drug treatment. By analyzing 78 publicly available transcriptomic melanoma datasets, we found that de-differentiation in melanoma is accompanied by upregulation of mesenchymal genes, but not necessarily a concomitant loss of an epithelial program, suggesting a more "one-dimensional" EMT that leads to a hybrid epithelial/ mesenchymal phenotype. Samples lying in the hybrid epithelial/mesenchymal phenotype also correspond to the intermediate phenotypes in melanoma along the proliferative-invasive axis - neural crest and transitory ones. Interestingly, as melanoma cells progress along the invasive axis, the mesenchymal signature does not increase monotonically. Instead, we observe a peak in mesenchymal scores followed by a decline, as cells further de-differentiate. This biphasic response recapitulates the dynamics of melanocyte development, suggesting close interactions among genes controlling differentiation and mesenchymal programs in melanocytes. Similar trends were noted for metabolic changes often associated with EMT in carcinomas in which progression along mesenchymal axis correlates with the downregulation of oxidative phosphorylation, while largely maintaining glycolytic capacity. Overall, these results provide an explanation for how EMT and dedifferentiation axes overlap with respect to their transcriptional and metabolic programs in melanoma.

cancer biology↗

bioRxiv2021

Computational analysis reveals similarities and differences between SCLC subtypes

Small cell lung cancer (SCLC) is a neuroendocrine malignancy with dismal survival rates. Previous studies have revealed inter and intra tumoral heterogeneity of SCLC driven by neuroendocrine differentiation and multiple gene expression signatures have been proposed to classify the distinct SCLC molecular subtypes However, few questions remain unanswered: a) how many SCLC subtypes exist? b) how similar or different are these subtypes?, c) which gene list(s) can be used to identify those specific subtypes? Here, we show that irrespective of the three gene sets (33 genes, 50 genes, 105 genes) proposed in different studies to classify SCLC into different subtypes, the markers of phenotypic heterogeneity in SCLC form a "teams" like pattern of co-expressed modules. Moreover, the 105 geneset could classify SCLC cell lines into five clusters, three of which can be distinctly mapped to the SCLC-A, SCLC-N and SCLC-Y subtypes. Intriguingly, we noticed a high degree of similarity in the transcriptional landscape of two non-neuroendocrine subtypes: SCLC-Y and SCLC-I*, as well as in their enrichment of EMT. Thus, our analysis elucidates the landscape of phenotypic heterogeneity enabling diverse SCLC subtypes.

cancer biology↗

bioRxiv2021

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. We developed ENVIM based on the Elastic Net Model (ENM) to predict metabolites using micorbiome data. ENVIM introduces an extra step to ENM to consider variable importance scores and thus achieve better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 297 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health and vagina health. We select gene-family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly-developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan using metatranscriptomics data only, metagenomics data only, or both of these two. Both methods perform better prediction using gut or vagina microbiome data than using oral microbiome data for the samples corresponding metabolites. The top predictable compounds have been reported in all these three datasets from three different body sites. Enrichment of prediction some contributing species has been detected.

microbiology↗

bioRxiv2021

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

systems biology↗