Biology subjects

Gupta, S.

Publications and source records attributed to Gupta, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Genedex: Contextualizing gene signatures across Alzheimer's disease pathology, cell types, and brain regions

Systematic prioritization of candidate Alzheimer's disease (AD) genes and their target functions for therapeutic intervention requires a consistent understanding of their brain location, cell-type-specific expression, relationships with pathology, and direction of change. Defining the role of a gene in AD requires interrogation of multiple publications, each providing a different context. Often, it is also unclear whether a gene is up- or downregulated across these contexts. However, the narrow diversity of contexts in which they have been assayed and described limits interpretation. We integrated 1,562 gene signatures from 34 studies, annotated for pathological process, cell type, brain region, and direction of change, and showed that Genedex recovers established AD biology. Genedex addresses cellular and regional unexplained directional contradictions from 10,657 discordant genes in the published AD literature. We investigated the role of heat-shock chaperones and neurotrophic signaling in cognitive resilience using Genedex. Heat-shock chaperone genes are broadly upregulated with tau burden; but, in the presence of amyloid pathology, their expression diverges, showing upregulation in excitatory neurons and downregulation in glia and during A{beta} clearance. LINGO1, a regulator of BDNF, is upregulated with neuritic plaque load, tau burden, and selective vulnerability in cortical regions. Notably, in tau-affected RORB excitatory neurons, LINGO1 expression is inversely correlated with neurotrophic genes BDNF and NTRK2. A prioritized interneuronal resilience signature shows correlated pathway co-expression with somatostatin and parvalbumin interneuronal signatures. Vulnerability-associated signatures separate by cellular context: neuronal programs are positively correlated with vulnerable interneurons, while glial and vascular cells are predominantly anti-correlated. By systematically mapping genes across functional, cellular, spatial, and directional axes, Genedex provides a platform for discovery of contextualized targets and mechanistic hypotheses. Genedex is freely available at hidelab.github.io/genedex-app.

neuroscience↗

bioRxiv2026

Semantic Update as a Predictor of Reading Time: Moving Beyond Word-Level Surprisal

This study explores the role of semantic prediction error in shaping reading behavior, as reflected in reading time during naturalistic reading. We operationalize semantic prediction error using semantic update (SU), a measure derived from the Sentence Gestalt (SG) model, which simulates incremental predictive comprehension at the level of sentence meaning. While SU has been previously linked to the N400 ERP component - an index of semantic processing - here we test its behavioral relevance by examining how it predicts total word-fixation durations in an eye-tracking corpus. To contextualize the role of semantic prediction error, we compare SU with lexical prediction error, quantified as next-word surprisal estimated from a language model (LM) trained on the same corpus as the SG model. Our results show that SU significantly contributes to explaining total word-fixation durations, even after accounting for LM-based surprisal and lower-level lexical factors. We also find that this predictive relationship holds primarily for content words, but not for function words, which parallels findings for lexical surprisal. These findings support the view that semantic prediction error plays a unique and measurable role in guiding real-time reading behavior, complementary to lexical prediction.

neuroscience↗

bioRxiv2026

Hepatocyte UBE4A protects against MASLD/MASH by maintaining ER homeostasis via UBXN4 interaction

Molecular understanding of the regulation of endoplasmic reticulum (ER) functions in healthy and MASLD livers is limited. Here, we elucidate that Ube4A, a cytosolic E3 ubiquitin ligase, protects against MASLD development by maintaining ER homeostasis during nutritional stress. Hepatocyte-Ube4A deletion (HUKO) augments Gubra-Amylin-diet (GAN-D)-induced steatosis, inflammation, insulin resistance, ER stress and markers of steatohepatitis of the liver in both sexes of mice. Proteomic studies reveal that in addition to altered metabolic and fibrogenic pathways, levels of many ER proteins are also changed in HUKO livers. Impaired ER functions sensitize HUKO livers to nutrition- or chemical (tunicamycin)-induced ER stress. Moreover, HUKO primary hepatocytes exhibit increased ER stress. Furthermore, Ube4A maintains ER homeostasis by facilitating ER-associated protein degradation (ERAD). The lipogenic protein SCD1 is identified as one of the ERAD targets of Ube4A. SCD1 stability and triglyceride accumulation are amplified in HUKO primary hepatocytes. The liver Ube4A-interactome studies identified the ERAD mediator UBXN4 as a major Ube4A interacting protein. Alphafold3-based molecular modelling and interaction site mapping analyses pinpointed the Ube4A binding residues for UBXN4. ER-localized Ube4A-UBXN4 complex facilitates ERAD (e.g., SCD1) and UBXN4-binding deficient Ube4A mutants fail to enhance this process. Finally, Ube4A and UBXN4 are downregulated in the livers of human MASLD and MASH patients and this complex is diminished in GAN-D-fed mouse livers. In conclusion, Ube4A preserves ER homeostasis by maintaining ERAD via its interaction with UBXN4, which maintains liver health. Enhancing levels and/or preserving interaction of these proteins may offer new therapeutic strategies for MASLD treatment.

physiology↗

bioRxiv2026

The mitochondrial transaminase GOT2 supports Japanese encephalitis virus replication in neuronal cells

Japanese encephalitis virus (JEV) is the leading cause of viral encephalitis in Asia, yet the host pathways that sustain its replication in the central nervous system remain poorly defined, and no antiviral therapies exist. Here we combine time-resolved transcriptomics and proteomics in mouse neuroblastoma (Neuro-2a) cells with time-resolved proteomics of infected mouse brain, integrated using directionality-penalized p-value merging. JEV infection was accompanied by progressive induction of interferon-stimulated proteins and by changes in the abundance of enzymes of central carbon and energy metabolism, spanning the tricarboxylic acid cycle, glycolysis, oxidative phosphorylation and glutaminolysis. Temporal clustering of the brain proteome linked oxidative phosphorylation remodelling to a dependence on glutamine metabolism. Glutamine antagonism with DON reduced viral RNA, whereas glycolytic inhibition and AKT modulation did not significantly alter it under the conditions tested. Silencing of the mitochondrial transaminase GOT2 reduced JEV RNA, viral protein, and infectious virus production in human SH-SY5Y cells. Our findings identify a glutamine-GOT2 axis as a metabolic dependency of JEV and a candidate host-directed antiviral target.

microbiology↗

bioRxiv2026

From Sparse Visits to Continuous Molecular Trajectories with Flow Matching

Longitudinal transcriptomic studies often capture only a few molecular snapshots of each patient over time. This makes it challenging to understand how patients are progressing, compare them when progression rates differ, and capture molecular changes between observed visits. We introduce CohortFM, a flow-matching framework that reconstructs continuous patient-specific molecular trajectories from sparse longitudinal observations and organizes them through an integrated mixture model into a compact set of cohort-level progression patterns. CohortFM separates molecular progression from calendar time, places patients on a shared progression scale, uses observed intermediate states to guide the learned trajectories, and maps the resulting patterns back to real patient samples for biological interpretation. We evaluated CohortFM across COVID-19, sepsis, influenza, and tuberculosis. Despite different sampling patterns, the method generalizes well and consistently produces meaningful trajectories. These trajectories capture biological differences between patients that cannot be explained by the observed samples alone. Compared with state-of-the-art trajectory baselines, CohortFM produces more biologically distinct trajectories and a more complete representation of the patient population. Its finer progression scale also reveals biological programs and temporal dynamics that are not captured by visit-level analysis. We further evaluated interpolation and true forward prediction to test whether CohortFM can accurately recover unobserved states. CohortFM remains competitive with strong interpolation baselines and is among the strongest learned predictors in the harder forecasting setting. Ablation experiments further show that flow matching is the core learning mechanism and that our design choices improve continuous trajectory learning. Our results support CohortFM as a practical framework for turning sparse molecular snapshots into continuous, biologically interpretable trajectories. By going beyond visit labels, CohortFM makes it possible to study disease progression and predict future molecular states even when patients are sampled only a few times.

bioinformatics↗

bioRxiv2026

Lung FDG-PET is an early pharmacodynamic biomarker of antiviral efficacy in preclinical influenza

Background. Influenza remains a major cause of global morbidity and mortality. Pharmacodynamic biomarkers that quantify treatment response within the lung could accelerate influenza therapeutic development. We evaluated [18F]-fluorodeoxyglucose positron emission tomography ([18F]-FDG-PET) as a non-invasive biomarker of antiviral efficacy. Methods. Serial [18F]-FDG-PET was evaluated in two murine H1N1 influenza models using mouse-adapted PR8 strain and CA-09 clinical isolate following treatment with favipiravir, baloxavir marboxil, or ribavirin. PET measures were compared with pulmonary viral burden, inflammatory cytokine expression, computed tomography (CT) imaging, and respiratory function measured by whole-body plethysmography (WBP). Results. Antiviral treatment in PR8 infection model reduced lung [18F]-FDG uptake by 27-49% and metabolic lung volume (MLV) by 40.5-74.6% (both p < 0.001). Antiviral response was detectable as early as 3 days post-infection, when uptake decreased by 28.7-53.2% and MLV by 33.8-75.0%. Lung uptake also strongly correlated with MLV (r = 0.93, p < 0.001) and with pulmonary viral burden and inflammatory mediators. These findings were reproduced in a model of pandemic H1N1/CA-09, where baloxavir reduced uptake by 37.4% (p = 0.02) and MLV by 67.5% (p < 0.001) at day 3. In both models, changes in [18F]-FDG-PET parameters preceded improvements in lung CT and respiratory function. Conclusions. Lung [18F]-FDG-PET provides an early, quantitative measure of antiviral response in experimental influenza, supporting its further evaluation as a translational pharmacodynamic endpoint.

microbiology↗

bioRxiv2026

DCMax - A novel Delayed Cmax Drug Delivery System for Improved Chronotherapy

This study evaluated the Delayed Cmax Drug Delivery System (DCMax), an erosion-based controlled- release formulation designed to delay peak plasma concentration (Cmax) and sustain drug release. The tablet matrix contained hydroxypropyl methylcellulose, hydroxypropyl methylcellulose acetate succinate, and polyethylene oxide. In-vitro dissolution and erosion studies confirmed the delayed release profile. In-vivo performance was assessed in six healthy volunteers by measuring salivary caffeine concentrations after administration of immediate-release caffeine tablets and DCMax tablets. DCMax induced a delayed Cmax of 5 hours compared with 1 hour for the immediate-release formulation, while peak concentrations were lower compared to immediate release. These findings demonstrate that erosion-based drug delivery can achieve delayed release, with potential application in chronotherapy for endocrine disorders.

pharmacology and toxicology↗

bioRxiv2026

Plasma Extracellular Vesicle Lipidomics Reveals an SMPD1-Driven Sphingomyelin Salvage Pathway Collapse in Parkinson's Disease

While lysosomal lipid dysregulation is implicated in Parkinsons disease (PD), identifying the precise enzymatic drivers remains limited by the lack of subcellularly resolved metabolic mapping. Leveraging endosome-origin plasma extracellular vesicle (PsEV) lipidomics, we identified a profound bidirectional disruption of the ceramide-sphingomyelin (SM) axis in PD, driven by asymmetric upregulation of sphingomyelin phosphodiesterase 1 (SMPD1) and sphingomyelin synthase 1 (SGMS1). BODIPY-C5 SM tracking revealed that SMPD1 perturbation severely impairs lysosomal SM turnover. Crucially, this SMPD1-driven hydrolysis forces a salvage pathway collapse, bottlenecked by SPHK1/SGPL1 downregulation, which precipitates oxidative membrane damage and PINK1/Parkin-dependent mitophagy failure. We robustly validated this multi-pathway dysfunction across orthogonal in vitro, in vivo mouse, and postmortem human brain models. Furthermore, SMPD1/SGMS1 expression distinctly stratifies clinical phenotypes, marking the aggressive Postural Instability/Gait Difficulty (PIGD) subtype. Our findings establish SMPD1-mediated lysosomal impairment and ceramide-SM imbalance as a core biochemical axis in PD pathophysiology.

neuroscience↗

bioRxiv2026

Evaluating Brain Flow Index from a temple-worn wearable against depth-resolved time-domain NIRS during head-down tilt and postural transitions in healthy young men

Significance: Wearable optical sensors could make continuous cerebral hemodynamic monitoring practical in daily life. Because any surface head sensor also samples extracerebral tissue, demonstrating cerebral relevance requires a reference that separates deep from superficial hemodynamics. Aim: We evaluated whether the Temple Brain Flow Index (Temple-BF), derived from a temple-worn photoplethysmographic wearable, covaried more strongly with the deeper (brain-assigned) than the superficial (scalp-assigned) hemodynamics recovered from time-domain near-infrared spectroscopy (TD-NIRS) during controlled postural challenges. Approach: Sixty sessions from 44 healthy young adult men were analyzed across three physiological challenges: 30{degrees} head-down tilt, stand-to-squat, and stand-to-supine transitions. Optical density and mean time of flight from a three-module TD-NIRS system were inverted with a Monte Carlo-derived two-layer model to obtain scalp- and brain-layer hemoglobin time series. Temple-BF was compared with these model-derived signals using lag-adjusted and zero-lag correlations, paired brain-versus-scalp comparisons, heart rate and short-separation-HbO adjusted partial correlations. Results: Median lag-adjusted correlations between Temple-BF and brain-layer {Delta}HbO traces were 0.912, 0.843, and 0.839 for head-down tilt, stand-to-squat, and stand-to-supine transitions, respectively; corresponding scalp-layer medians were 0.494, 0.700, and 0.767. Paired brain-minus-scalp differences were significant for head-down tilt (median 0.333, p < 0.001) and stand-to-squat (0.110, p < 0.001) but not for stand-to-supine (0.036, p = 0.082) transitions. Zero-lag brain-layer medians were 0.826, 0.701, and 0.796, and the association persisted after adjustment for heart rate (partial r = 0.832, 0.850, 0.654) and for short-separation superficial HbO (0.649, 0.570, 0.438). Transition-window correlations had medians of 0.878, 0.861, and 0.876, with directionally concordant transition responses in 59 of 60 sessions. Conclusions: Temple-BF tracked model-derived brain-layer TD-NIRS {Delta}HbO across all three postural challenges, with larger median correlations against the brain-layer than the scalp compartment in all three protocols, and transition responses in the same direction for almost all sessions. These results support Temple-BF as a relative marker of cerebral hemodynamic change during postural perturbations. Establishing cerebral specificity at the temple, and excluding residual systemic and superficial contributions, will require flow-sensitive references and fuller systemic monitoring.

neuroscience↗

bioRxiv2026

A Hybrid Residual-Swin Transformer Design with Attention for Prostate Cancer Segmentation

Prostate cancer, a leading cause of cancer-related deaths among men globally, necessitates the development of precise diagnostic and treatment strategies. Accurate segmentation of prostate cancer in medical imaging, particularly in MRI scans, is crucial for early diagnosis and clinical decisions. Traditional manual segmentation techniques, while efficient, are labor-intensive and necessitate significant expertise, resulting in an increasing demand for automated alternatives. The RSAUNet is a novel deep learning architecture designed to improve prostate cancer segmentation. It incorporates essential components, including Residual Blocks, Swin Transformer Blocks, and Attention Mechanisms within a U-Net architecture. This markedly enhances the model's capacity to discern complex anatomical features and accurately segment malignant tissues. Using sophisticated deep Learning methods, RSAUNet addresses the complexities of prostate imaging, delivering reliable, consistent segmentation results. The model was evaluated against various cutting-edge techniques on extensive multi-parametric MRI datasets, attaining an impressive Dice coefficient (DC) of 0.998 and a Jaccard Index (IoU) of 0.965. These findings highlight the innovative characteristics of RSAUNet and its capacity to transform prostate cancer diagnosis and treatment strategies. The proposed model surpasses current methods and shows potential for practical clinical applications, providing an efficient, precise, and scalable solution for automated prostate cancer segmentation and fostering optimism for the future of medical imaging and diagnosis.

cancer biology↗

bioRxiv2026

Large-scale structural analysis of pre-mRNA structure using RADIS

Pre-mRNA secondary structure can modulate the regulatory function of intronic sequences by masking or exposing splice-site signals and altering the accessibility of other features. Yet experimentally supported structural models for human pre-mRNAs have been largely restricted to splice-site-proximal sequences, leaving the deep intronic regions where ~70% of intronic disease-causing variants reside almost entirely unmapped. We developed RADIS (Reactivity Analysis of Deep and Intergenic RNA Structure), a strategy that yields strand-resolved chemical probing reactivity profiles across entire intron-rich loci by creating tiled arrays of RNAs that comprehensively span long genomic distances. This approach removes the abundance and locus-specificity constraints that otherwise limit pre-mRNA probing approaches. RADIS recovers known E. coli ribosomal RNA architectures and yields high-correlation with in-cell dimethyl sulfate (DMS)-MaP reactivities at representative splice sites (r = 0.87-0.90). RADIS-constrained folding of 109 5' and 88 3' splice sites uncovers an inverse relationship between base-pairing within the spliceosome footprint and splice-site strength, and partitions 81 branchpoints into three structural classes. Across 233 full-length Alu elements, sense Alu RNAs are more structured than antisense elements, and both partition into strand- and lineage-dependent structural classes. RADIS enables experimentally grounded structure analysis at disease-associated intronic and intergenic loci, complementing sequence-based variant-effect predictors.

molecular biology↗

bioRxiv2026

Cryptic Genes as Building Blocks of Antimicrobial Resistance: Evidence from a Novel Marine Imipenem-Hydrolyzing Metallo-β-Lactamase

The emergence of antimicrobial resistant pathogens has led to increase in mortality rate, longer hospital stays, and need for complex and expensive treatment regimens. However, it is not very clear from where does the bacteria get the building blocks to develop resistance to these synthetic antimicrobial agents which have been integral part of modern medicine. One hypothesis is that non-coding cryptic genes without any vital function or positive contribution to fitness act as versatile endogenous genetic repertoires that help in bacterial adaptation and evolution of new phenotypes when conditions change. This study reports the presence of putative cryptic metallo beta lactamase gene in the marine bacterium Cytobacillus oceanisediminis isolated from the Gulf of Mannar, India. Using in silico, in vitro and in vivo assay, we demonstrate that putative cryptic metallo beta lactamase encodes an enzyme that can cleave only imipenem, has two characteristic domains: HXHXDH and TPGH, but does not share homology with the other reported Metallo-beta-Lactamase genes. The findings underscore the importance for comprehensive screening of putative cryptic antimicrobial resistance genes to fully address and mitigate the challenges posed by antimicrobial resistance.

microbiology↗

bioRxiv2026

Myeloperoxidase (MPO) exacerbates dengue-associated liver injury and contributes to disease pathogenesis in mouse models

Severe dengue can damage the liver through unestablished mechanisms. We investigated the role of myeloperoxidase (MPO), a neutrophil enzyme, in dengue through patients, fatal liver samples, and mouse infection models. Observations from two independent clinical cohorts revealed elevated plasma MPO levels in dengue and, in one cohort, MPO was further linked to liver injury markers during the critical phase of disease, whereas livers from dengue fatal cases revealed MPO build-up in the vicinity of CD177+ activated neutrophils. In mice, dengue led to MPO overexpression, oxidative damage, and broad activation of innate and systemic inflammatory pathways in livers. Blocking MPO activity alleviated these and improved survival in one model and delayed disease progression without preventing death in another. These findings establish MPO as a functional mediator of severe dengue-associated liver injury and inflammation, which warrants further preclinical investigation into its hepatic pathogenic mechanism and its validity as target for therapeutic intervention.

pathology↗

bioRxiv2026

Nanoluciferase reporter preserves immunocompetent glioma model fidelity while facilitating longitudinal molecular imaging

Glioblastoma studies employ syngeneic orthotopic models to preserve tumor-immune interactions, but intracranial tumor burden is challenging to monitor longitudinally. Bioluminescence imaging enables non-invasive assessment, although reporter immunogenicity may compromise model fidelity. We engineered murine GL261 glioma cells to stably express nanoluciferase (NLuc) and compared them with parental GL261 (WT) and GL261 cells expressing red-shifted firefly luciferase (Red-FLuc). In vitro, GL261-NLuc retained growth kinetics and morphology comparable to GL261-WT and produced >100-fold stronger bioluminescence than GL261-Red-FLuc. In immunocompetent mice, GL261-NLuc formed lethal brain tumors with survival and tumor histopathology, immune profile, and response patterns to experimental oncolytic virus therapy broadly resembling GL261-WT. In contrast, GL261-Red-FLuc tumors regressed and exhibited heightened inflammation and increased infiltration of activated CD8+ T-cells. Longitudinal imaging of GL261-NLuc tumors detected treatment-associated changes in growth kinetics not captured by survival alone. These establish GL261-NLuc as a practical reporter for longitudinal immunocompetent glioblastoma studies amenable to immunotherapy evaluations.

molecular biology↗

bioRxiv2026

MPGEM: A harmonized and transcriptome-complete resource for large-scale reuse of legacy human microarray data

Abstract Background: Legacy microarray datasets provide an extensive record of human transcriptomic biology, but their reuse is constrained by differences in platform design, preprocessing, measurement scale, and gene coverage. Platforms measuring only subsets of genes cannot readily be integrated with higher-coverage platforms, limiting large-scale analysis and computational modeling. Results: We developed Multi-Platform Gene Expression Matrix (MPGEM), a computational framework and resource for harmonizing and completing gene-expression profiles across heterogeneous microarray platforms. MPGEM uses a Reference Quantile Distribution (RQD) and generalized Reference Subset Quantile Distribution (RSQD) framework to transform profiles with different gene coverage onto a common quantitative scale. The MPGEM Engine, a multilayer perceptron, predicts expression of unmeasured genes from genes shared across platforms. Applied to Affymetrix GPL570, GPL571, and GPL96, MPGEM uses GPL570 as a 19,320- gene reference space comprising 12,712 predictor and 6,608 target genes. The resulting resource contains 207,135 human gene-expression profiles across 19,320 genes. Evaluation using masked GPL570 profiles yielded mean sample-wise Pearson and Spearman correlations of 0.944 and 0.939, respectively, and mean gene-wise correlations of 0.830 and 0.825. The lowest-performing 5% of target genes achieved a mean Pearson correlation of 0.683. MPGEM showed comparable or higher predictive performance than baseline mean imputation and K-nearest-neighbor approaches. Conclusions: MPGEM transforms heterogeneous, partially measured legacy microarray profiles into a harmonized, transcriptome-complete representation, facilitating their reuse for large-scale transcriptomic analysis, biomarker discovery, systems biology, and machine learning. The framework, trained models, and expression resource are provided as open-source resources.

bioinformatics↗

bioRxiv2026

AP-1 activation in Drosophila neuropil ensheathing glia improves traumatic brain injury survival

Traumatic brain injury (TBI) impacts millions of individuals annually causing death, disability, and a heightened risk for long-term neurological and neuropsychiatric disorders. In recent years the fruit fly, Drosophila melanogaster has become a valuable model organism to study the cellular and molecular responses following TBI. AP-1 mediated transcriptional responses to TBI have previously been identified in Drosophila using pan-glial approaches. Fruit flies possess multiple glial subtypes which vary greatly in both cellular morphology and function, including glia of the blood hemolymph barrier, cortex, astrocyte-like, and ensheathing glia. By generating and utilizing a nuclear localized AP-1 transcriptional reporter, we identified glial subtype-specific differences in the extent of AP-1 activation following injury. Our findings identify a strong AP-1 response in the blood hemolymph barrier and ensheathing glia, a moderate response in cortex glia and little to no AP-1 activation in astrocyte-like glia. In addition, we inhibited AP-1 signaling in each glial subtype and tested the effect on acute survival. We found that inhibition of the AP-1 response in neuropil ensheathing glia leads to increased mortality following mild and moderate TBI. These results show that AP-1 activation levels vary across glial subtypes after TBI, with activation in neuropil ensheathing glia having a particularly important role in promoting post-injury survival. ARTICLE SUMMARYUsing Drosophila as a model organism, we investigated the early molecular and cellular response to traumatic brain injury. Our findings substantiate the requirement of a functional glial associated AP-1 transcriptional activation response for survival. Using colocalization studies, we characterized the AP-1 glial response in six morphologically and functionally distinct glia subtypes. After TBI, we find high levels of AP-1 activation in glia of the hemolymph brain barrier, cortex glia, and ensheathing glia. We further show the importance of AP-1 transcription within the neuropil ensheathing glia subtype for optimal survival following TBI.

neuroscience↗

bioRxiv2026

A Systems Neuroscience Approach Identifies IL1B-CASP3 Signaling as a Molecular Link Between Polystyrene Exposure and Alzheimer's Disease

The widespread presence of microplastics and nanoplastics has emerged as a significant environmental concern, with increasing evidence suggesting potential adverse effects on neurological health. However, the molecular mechanisms linking polystyrene exposure to Alzheimers disease (AD) remain poorly understood. In this study, an integrative systems biology framework was employed to investigate the molecular interplay between environmental polystyrene exposure and AD pathogenesis. AD-associated genes were retrieved from the Comparative Toxicogenomics Database (CTD) and DisGeNET, while polystyrene-responsive genes were obtained from CTD. Integration of these datasets identified 16 shared genes potentially connecting polystyrene exposure with AD. Transcriptomic analysis of the hippocampal dataset GSE29378 revealed significant differential expression of several overlapping genes between AD and healthy controls. Functional enrichment analyses demonstrated that these genes are predominantly involved in oxidative stress, inflammatory signaling, apoptosis, and synaptic function, all of which are central to AD pathology. Weighted gene co-expression network analysis (WGCNA) further identified disease-associated modules containing multiple intersecting genes strongly correlated with AD clinical traits. Protein-protein interaction analysis highlighted IL1B, CASP3, BCL2, ACHE, and APOE as key hub genes, indicating their potential roles in integrating environmental stress responses with neurodegenerative pathways. Independent validation using the GSE48350 dataset confirmed the robust diagnostic performance of several hub genes in discriminating AD from control samples. Collectively, these findings suggest that environmental polystyrene exposure may promote AD progression through neuroinflammation, oxidative stress, apoptosis, and synaptic dysfunction, providing novel mechanistic insights and identifying promising molecular targets for future experimental, clinical, and epidemiological investigations.

neuroscience↗

bioRxiv2026

Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature

BackgroundSevere dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. MethodsPlasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. ResultsIncreasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. ConclusionIntegrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.

systems biology↗