Biology subjects

Bhattacharyya, R.

Publications and source records attributed to Bhattacharyya, R..

8 recordsLinked to original sources

bioRxiv2026

A Stress-Adaptive Lipid Kinase Axis Defines Metabolic Vulnerabilities in Neuroendocrine Prostate Cancer

Neuroendocrine prostate cancer (NEPC) persists in a profoundly hypoxic microenvironment, yet the mechanisms enabling tumor adaptation to this metabolically challenging niche remain undefined. Here, we identify the lipid kinase PIKfyve as overexpressed in NEPC, functioning as a central node in a stress-lipid kinase axis that drives adaptation to persistent endoplasmic reticulum (ER) stress. Mechanistically, NEPC requires PIKfyve-mediated lysosomal degradation and lipid recycling to maintain metabolic homeostasis under hypoxia. PIKfyve inhibition disrupts lysosomal function, leading to ER stress accumulation and activation of a compensatory, sterol regulatory element-binding protein (SREBP)-dependent de novo lipogenesis program essential for NEPC survival. This stress-lipid axis creates a synthetic vulnerability between PIKfyve and fatty acid synthase (FASN), where dual inhibition synergistically amplifies ER stress, triggers the terminal unfolded protein response, and induces tumor cell death. These findings reveal a metabolic adaptation in NEPC and provide preclinical evidence that co-targeting PIKfyve and FASN can overcome hypoxia-associated stress adaptation.

cancer biology↗

bioRxiv2026

Alternative organelle targeting of OPA1 mediates fatty acid release from lipid droplets

Mitochondria and lipid droplets (LDs) are functionally coupled to coordinate fatty acid utilization and storage. However, a comprehensive understanding of mitochondria-LD alliances remains elusive. We have identified a previously unrecognized role for optical atrophy 1 (OPA1), a mitochondrial fusion factor, in the regulation of fatty acid release from LDs. We demonstrated that OPA1s exon 4 adapts an amphipathic helix to target OPA1 to LDs. OPA1 localized to LDs promote fatty acid release by facilitating the recruitment of lipases to LDs. In addition, OPA1s residence on LDs competes with its mitochondrial entry, influencing mitochondria fusion and connectivity. Furthermore, the S158N polymorphism within OPA1s exon 4 exhibiting attenuated fatty acid release from LDs is associated with changes in metabolic traits in pediatric cancer survivors. Altogether, our findings reveal that OPA1 actively mediates fatty acid release from LDs and provide a mechanistic link between OPA1 and human metabolism.

cell biology↗

bioRxiv2024

Targeting PIKfyve-driven lipid homeostasis as a metabolic vulnerability in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) subsists in a nutrient-deregulated microenvironment, making it particularly susceptible to treatments that interfere with cancer metabolism1 2. For example, PDAC utilizes and is dependent on high levels of autophagy and other lysosomal processes3-5. Although targeting these pathways has shown potential in preclinical studies, progress has been hampered by the challenge of identifying and characterizing favorable targets for drug development6. Here, we characterize PIKfyve, a lipid kinase integral to lysosomal functioning7, as a novel and targetable vulnerability in PDAC. In human patient and murine PDAC samples, we discovered that PIKFYVE is overexpressed in PDAC cells compared to adjacent normal cells. Employing a genetically engineered mouse model, we established the essential role of PIKfyve in PDAC progression. Further, through comprehensive metabolic analyses, we found that PIKfyve inhibition obligated PDAC to upregulate de novo lipid synthesis, a relationship previously undescribed. PIKfyve inhibition triggered a distinct lipogenic gene expression and metabolic program, creating a dependency on de novo lipid metabolism pathways, by upregulating genes such as FASN and ACACA. In PDAC, the KRAS-MAPK signaling pathway is a primary driver of de novo lipid synthesis, specifically enhancing FASN and ACACA levels. Accordingly, the simultaneous targeting of PIKfyve and KRAS-MAPK resulted in the elimination of tumor burden in a syngeneic orthotopic model and tumor regression in a xenograft model of PDAC. Taken together, these studies suggest that disrupting lipid metabolism through PIKfyve inhibition induces synthetic lethality in conjunction with KRAS-MAPK-directed therapies for PDAC.

cancer biology↗

bioRxiv2023

Decoding Internal Decision Making During Reverse Engineering Tasks

Neural decoding is often limited to tasks with known stimuli and limited response options . Real world tasks, however, are often completely stimulus free with unconstrained user response possibilities. Real time decoding of internal decision making would allow for more complex and interactive Huma Machine Teaming in a way that is not currently possible. To address this problem, we present here a novel method of decoding moments of recognition and their associated internal value judgments in the context of highly complex software reverse engineering tasks. This is done through a combination of P300 detection (a neural marker of recognition) and the Engagement Index (a ratio of neural band powers) to determine whether an item has been identified as relevant to the task (to be further explored) or irrelevant to the task (to be quickly ignored). Artificial neural networks were trained to identify P300s in each subject during the reverse engineering tasks. Dimensionality reduction of neural data during the tasks showed the existence of separately clustering subgroups of P300s with differences in Engagement Index. Subgroups of P300s differentiated by Engagement were further verified as distinct groupings with pupil dilation and user behavior metrics. This decoded information could be used to aid in the reverse engineering process via cognitive offloading of the users own decision making on to the visual interface in a completely automated and personalized fashion. This represents a significant advance in domain of real-time neural decoding, and opens up many further possibilities for usage in a broad range of intelligent human systems integration applications.

neuroscience↗

bioRxiv2020

Transmission of allosteric response within the homotrimer of SARS-CoV-2 spike upon recognition of ACE2 receptor by the receptor-binding domain

The pathogenesis of novel SARS-CoV-2 virus initiates through recognition of ACE2 receptor (Angiotensin-converting enzyme 2) of the host cells by the receptor-binding domain (RBD) located at spikes of the virus. Following receptor-recognition, proteolytic cleavage between S1 and S2 subunits of the spike protein occurs with subsequent release of fusion peptide. Here, we report our study on allosteric communication within RBD that propagates the signal from ACE2-binding site towards allosteric site for the post-binding activation of proteolytic cleavage. Using MD simulations, we have demonstrated allosteric crosstalk within RBD in apo- and receptor-bound states where dynamic correlated motions and electrostatic energy perturbations contribute. While allostery, based on correlated motions, dominates inherent distal communication in apo-RBD, electrostatic energy perturbations determine favorable crosstalk within RBD upon binding to ACE2. Notably, allosteric path is constituted with evolutionarily conserved residues pointing towards their biological relevance. As revealed from recent structures, in the trimeric arrangement of spike, RBD of one copy interacts with S2 domain of another copy. Interestingly, the allosteric site identified is in direct contact (H-bonded) with a region in RBD that corresponds to the interacting region of RBD of one copy with S2 of another copy in trimeric constitution. Apparently, inter-monomer allosteric communication orchestrates concerted action of the trimer. Based on our results, we propose the allosteric loop of RBD as a potential drug target.

biophysics↗

bioRxiv2020

Diversified agroforestry systems improve carbon footprint and farmers livelihood under limited irrigation conditions

Increasing weather aberrations cause frequent crop failure in monoculture cropping system. Specialized crop production systems, where few seasonal crops occupy vast arable lands, resulting in more biotic and abiotic stresses in agri-ecosystem. Therefore a diversified agroforestry systemwas evaluated to ensure resilience underlimited water conditions, with an aim to augment carbon footprint with enhanced productivity and profitability. The study hypothesised that integration of perennial fruits trees with seasonal crops will have benign effect for sequestering more carbon and improving livelihood of the farmers. This is one of the first timesthat arid fruits tress along with leguminous,and other low water requiring crops were studied for improved carbon sequestration, livelihood of the farmers andfor better resilience in production system. The experimental findings showed that arid fruit trees along with leguminous, oilseeds and cash crops resulted in higher profitability and thus improved livelihood of the farmersin arid and semi-arid areas of South Asia. Diversified phalsa-mung bean-potato and moringa-mung bean-potato were the most productive agroforestry system (36.7t/ha and 36.2 t/ha respectively. Under limited irrigation conditions, Karonda (Carisa spp.)-mung bean potato system was found best in improving livelihood with maximum net return of $ 3529.1/ha with higher profitability/day ($ 19.9/day). Phalsa -MB-potato system was also recorded maximum water use efficiency (33.0 kg/ha-mm), whereas density of SOC was in Phalsa-cowpea-mustard (9.10 Mg/ha) and moringa-mung bean -potato AFS (9.16 Mg/ha). Carbon footprint analysis revealed that maximum net C gain was in Phalsa-mung bean -potato system (7030 Carbon equivalent kg CE/ha/year).

ecology↗

bioRxiv2019

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

AO_SCPCAPBSTRACTC_SCPCAPO_ST_ABSPurposeC_ST_ABSPersonalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.\n\nDatasets and methodsWe developed TransPRECISE, a multi-scale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intra-pathway activities, globally assess cell lines as representative models for patients and develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (>7700) from The Cancer Proteome Atlas across [≥]30 tumor types and a set of 640 cancer cell lines from the M.D. Anderson Cell Lines Project spanning16 lineages, and [≥]250 cell lines response to >400 drugs.\n\nResultsTransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: head and neck patient tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines, and had different prognostic patterns (456 vs. 654 days of median overall survival; P=0.02). The TransPRECISE-based sample-specific pathway scores achieved high predictive accuracy for drug sensitivities in cell lines across multiple drugs (median AUC >0.8).\n\nConclusionOur study provides a generalizable analytical framework to assess the translational potential of preclinical model systems and guide pathway-based personalized medical decision-making, integrating genomic and molecular data across model systems.

bioinformatics↗

bioRxiv2019

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the \"missing link\" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.\n\nData and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

bioinformatics↗