Biology subjects

Sharma, O.

Publications and source records attributed to Sharma, O..

10 recordsLinked to original sources

bioRxiv2026

Basolateral amygdala dopamine signals behavioural salience across exploration and learning

Animals must balance exploration with threat avoidance, yet how neuromodulatory signals shape information-seeking states remains unclear. Using fibre photometry recordings in the basolateral amygdala in freely moving mice, we show that dopamine release is selectively elevated during self-initiated exploratory actions, adapts with repeated exposure, and tracks the behavioural relevance of predictive cues in associative learning and extinction. Our results advance current models of amygdala dopamine function by suggesting that it conveys a state-dependent salience signal that highlights behaviourally relevant moments arising from both environmental cues and self-generated exploration, thereby priming amygdala circuits for plasticity.

neuroscience↗

bioRxiv2026

Deep Transfer Learning for Dormancy and Outbreaking State Classification in Metastatic Breast Tumor Cells: A Benchmark of Modern Deep Learning Models

Breast cancer cells that disseminate to distant organs can remain dormant (non-proliferative) for years before reactivating and progressing into lethal metastatic disease. Understanding the transition between dormancy and reactivation is therefore critical for early intervention and treatment. In this study, we investigate a comprehensive range of deep learning (DL) architectures to classify dormant versus proliferative breast tumor cells within a 3-dimensional growth factor reduced basement membrane extract (3D BME) system that models tumor dormancy and outgrowth. To capture the underlying spatiotemporal dynamics, we evaluate both spatial and sequence-based learning approaches. We consider convolutional neural networks (EfficientNet, ResNet, DenseNet, MobileNet, VGG, AlexNet), segmentation-based models (U-Net, U-Net++, Attention U-Net, DeepLabV3, HRNet) and transformer-based architectures (Vision Transformer, Swin Transformer, SegFormer). We investigate transfer learning using both fixed and fine-tuned strategies. Experimental results show that classification performance is greatly enhanced through the integration of temporal information. EfficientNet-B7, EfficientNet-B6, DenseNet-169, and DenseNet201 are consistently better than competing architectures for all tested models. EfficientNet-B7 with the use of temporal sequences input reaches an accuracy of 98.86% with a ROC-AUC of 0.998. The results highlight the significance of spatio-temporal feature learning and the value of DL frameworks in automated classification of dormant versus proliferative breast cancer cells in physiologically relevant microenvironments.

Cancer Biology↗

bioRxiv2026

iPSC-derived extracellular vesicles rescue deficits in human and mouse models of Parkinsons disease

Parkinsons disease (PD) pathogenesis often involves progressive -synuclein (-Syn)-mediated neuronal dysfunction, yet the earliest cellular events that link -Syn pathology to circuit failure remain poorly defined. Here, we used human induced pluripotent stem cell (iPSC)-derived dopaminergic (DA) neurons from patients carrying the familial A53T SNCA mutation to reconstruct a temporal course of dysfunction in vitro. We identified a biphasic trajectory with an early phase of hyperexcitability, characterized by elevated spontaneous firing, followed by a progressive transition into hypoexcitability as the neurons mature, accompanied by reduced network activity, synaptic dysfunction, and -Syn accumulation. Transcriptomic profiling at the critical transition point revealed a dual transcriptional signature, with upregulation of stress-inflammatory pathways (p53, JAK-STAT, apoptosis) coupled with systematic downregulation of metabolic and synaptic maintenance genes. This molecular profile preceded functional collapse, linking early hyperactivity-driven metabolic stress to subsequent neuronal exhaustion. To counteract this pathology, we used extracellular vesicles (EVs), small membrane-bound particles carrying intercellular signals, as a cell-free treatment approach. Strikingly, treatment with EVs derived from healthy iPSCs completely rescued both electrophysiological deficits and pathological -Syn accumulation, restoring normal firing patterns, synaptic function, and network activity. Consistent with these observations, EV treatment reduced -Syn aggregation and improved motor responses in -Syn fibril-injected mice, which are characterized by pathological -Syn accumulation and motor deficits. Overall, these findings demonstrate that EVs derived from healthy iPSCs can reverse PD-related phenotypes in human and mouse models.

neuroscience↗

bioRxiv2026

In silico transcriptomic analysis reveals shared molecular signatures and immune-associated pathways between Hashimotos thyroiditis and type 2 diabetes with exploratory drug repurposing

The management of Hashimotos thyroiditis (HT), one of the most prevalent autoimmune disorders worldwide, becomes more complex when it coexists with type 2 diabetes (T2D) compared with the management of either disease alone. This complexity may arise from overlapping genetic, metabolic, and immune dysregulation, as well as potential therapeutic conflicts. Although HT and T2D are known to co-occur and share immune and metabolic features, the molecular characteristics underlying these overlaps have not been systematically explored. This study aimed to identify shared gene expression signatures and associated biological pathways between HT and T2D using an in silico, hypothesis-generating approach, and to explore candidate compounds that may be relevant to both conditions. Independent transcriptomic datasets (GSE138198 for control/HT and GSE29231 for control/T2D) were analyzed, leading to the identification of 59 genes that were differentially expressed in both HT and T2D compared with control samples. Protein-protein interaction (PPI) network analysis prioritized five shared key genes (sKGs): CDC42, CD74, FOS, RAC2, and YWHAB. Functional enrichment analysis of these sKGs revealed overlapping biological processes, molecular functions, cellular components, and immune-related signaling pathways, as well as shared regulatory networks involving transcription factors (FOXC1 and HNF4A) and microRNAs (hsa-miR-221-3p and hsa-miR-29a-3p). Immune infiltration analysis demonstrated broadly similar patterns of immune dysregulation in both diseases, providing additional biological context for the observed shared molecular signatures. Finally, an exploratory in silico drug repurposing pipeline incorporating molecular docking, ADMET profiling, drug-likeness assessment, and molecular dynamics simulations prioritized three candidate compounds: gliquidone, oleanolic acid, and glipizide for further investigation. Overall, this study provides a hypothesis-generating framework highlighting shared molecular features between HT and T2D, which may inform future experimental validation and clinical research. Author SummaryIn this work, we wanted to better understand why Hashimotos thyroiditis, an autoimmune condition that affects the thyroid gland, is often seen in people who also have type 2 diabetes. Treating patients who live with both conditions can be difficult, and we were interested in finding out whether they share common biological causes. To do this, we examined genetic data from individuals with each disease and looked for patterns that appeared in both groups. We discovered several genes that seem to act in similar ways in the two conditions, particularly genes linked to immune system activity and associated pathways. This finding suggests that shared molecular signatures and immune-associated pathways may play a role in the development of both diseases. We also explored how these shared genetic features influence larger biological processes and immune responses. The similarities we found support the idea that the two diseases may be connected through related biological pathways. In addition, we used computer-based screening methods to identify existing drugs that might influence these shared pathways. While these results need further testing, we hope our findings help open new directions for research and eventually contribute to better care for patients affected by both conditions.

bioinformatics↗

bioRxiv2025

Divergent Excitability of GABAergic Neurons Derived from Bipolar Disorder Patients Shapes Energy Shifts of Network Dynamics, possibly mimicking mania and depression

Bipolar disorder (BD) is characterized by fluctuating mood states, yet the cellular and circuit-level mechanisms distinguishing lithium-responsive (LR) from non-responsive (NR) patients remain elusive. We derived dentate gyrus granule neurons and GABAergic interneurons from induced pluripotent stem cells (iPSCs) of BD patients stratified by lithium response, and from healthy controls. Using patch-clamp electrophysiology, we assessed intrinsic excitability. We further developed a computational model simulating large-scale neuronal networks based on patient-derived electrophysiological properties and ion channel conductance distributions. Granule neurons from both LR and NR patients exhibited hyperexcitability compared to controls. However, GABAergic neurons showed a striking divergence: LR neurons were hyperexcitable, while NR neurons were hypoexcitable. Transcriptomic profiling revealed distinct molecular signatures between NR and LR neurons, including dysregulation of GABA receptor genes (GABRR1). Computational simulations over 10,000 iterations, mimicking long-term network activity, revealed that dentate granule neuron changes alone failed to recapitulate netwrok shifts between global hyperexcitability and global hypoexcitability. Networks incorporating only granule neuron phenotypes entered persistent hyper- or hypoactive states, depending on lithium response. Remarkably, when GABAergic neuron phenotypes were added to the model, both LR and NR networks exhibited spontaneous transitions between high and low activity states, that may be associated with the mood episodes that the patients exhibit. Control networks did not show such bistability. We, therefore, conclude that GABAergic neuronal excitability is a key determinant of lithium responsiveness in BD and critically shapes the emergence of state-shift dynamics in neural networks. These findings suggest that restoring excitatory/inhibitory (E/I) balance via targeted modulation of interneuron function may offer novel therapeutic avenues for BD.

neuroscience↗

bioRxiv2025

Digestive Dimensions of Autism: A Multiscale Exploration of Gut-Brain Interactions

Social communication difficulties characterize autism spectrum disorders (ASD). Gastrointestinal (GI) symptoms are more common in ASD than in the general population. The identification of GI problems in individuals with ASD is challenging due to their altered pain perception and irregular behaviors. Importantly, GI symptoms and ASD can potentially aggravate each other. However, it is unclear if GI problems cause ASD symptoms or vice versa. A crosstalk between the digestive system, gut microbiota, and the central and enteric nervous systems has been repeatedly reported. The enteric nervous system (ENS) regulates the GI tract with the central nervous system (CNS) and the autonomic nervous system (ANS), as well as independently through specific neural circuits. Several mechanisms contribute to GI problems in ASD, including genetic mutations that affect the enteric nervous system (ENS), dysregulation of the ANS, alterations in gut microbiota, unhealthy dietary preferences, and changes in metabolomic profiles. Furthermore, studies have shown molecular and cellular differences in the GI biopsy of children with and without ASD. These findings highlight the unique nature of GI issues in ASD, underscoring the importance of further investigating the changes that occur in the digestive system and enteric nervous system (ENS) in ASD models.

neuroscience↗

bioRxiv2024

Predicting Suicide Risk in Bipolar Disorder patients from Lymphoblastoid Cell Lines genetic signatures

This research investigates the genetic signatures associated with a high risk of suicide in Bipolar disorder (BD) patients through RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) and their enrichment in pathways and disease associations, we uncover insights into the molecular mechanisms underlying suicidal behavior. LCL gene expression analysis reveals significant enrichment in pathways related to primary immunodeficiency, ion channel, and cardiovascular defects. Notably, genes such as LCK, KCNN2, and GRIA1 emerged as pivotal in these pathways, suggesting their potential roles as biomarkers. Machine learning models trained on a subset of the patients and then tested on other patients demonstrate high accuracy in distinguishing low and high-risk of suicide in BD patients. Moreover, the study explores the genetic overlap between suicide-related genes and several psychiatric disorders. This comprehensive approach enhances our understanding of the complex interplay between genetics and suicidal behavior, laying the groundwork for future prevention strategies.

neuroscience↗

bioRxiv2021

Temperature-dependent Self assembly of biofilaments during red blood cell sickling

Molecular self-assembly plays vital role in various biological functions. However, when aberrant molecules self-assemble to form large aggregates, it can give rise to various diseases. For example, the sickle cell disease and Alzheimers disease are caused by self-assembled hemoglobin fibers and amyloid plaques, respectively. Here we study the assembly kinetics of such fibers using kinetic Monte-Carlo simulation. We focus on the initial lag time of these highly stochastic processes, during which self-assembly is very slow. The lag time distributions turn out to be similar for two very different regimes of polymerization, namely, a) when polymerization is slow and depolymerization is fast, and b) the opposite case, when polymerization is fast and depolymerization is slow. Using temperature dependent on- and off-rates for hemoglobin fiber growth, reported in recent in-vitro experiments, we show that the mean lag time can exhibit non-monotonic behaviour with respect to change of temperature.

biophysics↗

bioRxiv2017

Platelet enrichment from whole blood in a clog-free microfluidic radial pillar device (RAPID)

Pillar-based passive microfluidic devices combine the advantages of simple designs, low device footprint, and high selectivity for size-based separation of blood cells. Most of these device designs have been validated with dilute blood samples. Handling whole blood in pillar-based devices is extremely challenging due to clogging. The high proportion of cells (particularly red blood cells) in blood, the varying sizes and stiffness of the different blood cells, and the tendency of the cells to aggregate lead to clogging of the pillars within a short period. We recently reported a radial pillar device (RAPID) design for contin-uous and high throughput separation of multi-sized rigid polystyrene particles in a single experiment. In this manuscript, we have given detailed guidelines to modify the design of RAPID for any application with deformable objects (e.g. cells). We have adapted RAPID to work with blood samples directly without any pre-processing steps. We were successful in operating the device with whole blood for almost 6 hours, which is difficult to achieve with most pillar-based devices. Finally, we demonstrated up to ~ 60-fold enrichment of platelets as an illustration of the improved device design. Whole blood pillar-based platelet clog-free RAPID

physiology↗

bioRxiv2017

A radial pillar device (RAPID) for continuous and high-throughput separation of multi-sized particles

Pillar-based microfluidic sorting devices are preferred for isolation of rare cells due to their simple designs and passive operation. Dead-end pillar filters can efficiently capture large rare cells, such as, circulating tumor cells (CTCs), nucleated red blood cells (NRBCs), etc., but they get clogged easily. Cross flow filters are preferred for smaller rare particles (e.g. separating bacteria from blood), but they need additional buffer inlets and a large device footprint for efficient operation. We have designed a new microparticle separation device i.e. Radial Pillar Device (RAPID) that combines the advantages of dead-end and cross flow filters. RAPID can simultaneously isolate both large and small rare particles from a mixed population, while functioning for several hours without clogging. We have achieved simultaneous separation of 10{micro}m and 2{micro}m polystyrene particles from a mixture of 2 {micro}m, 7 {micro}m and 10 {micro}m beads. RAPID achieved average separation purity and recovery in excess of 90%. The throughput of our device ( 3ml/min) is 10 and 100 times higher compared to cross flow and dead-end filters respectively, thereby justifying the name RAPID.

bioengineering↗