Biology subjects

Majumder, S.

Publications and source records attributed to Majumder, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Cytoplasmic recapping commences the translation of a micropeptide, MARS from MALAT1 after the removal of an inhibitory RNA G-quadruplex

Long non-coding RNAs (lncRNAs), conventionally defined as RNA transcripts longer than 200 nucleotides that lack protein-coding potential, have emerged as key regulators of diverse biological processes, including metabolism, development, cellular differentiation, and stress responses. They modulate cellular functions through multiple mechanisms, including chromatin remodeling, transcriptional regulation, and the control of RNA stability and translation. Most lncRNAs are transcribed by RNA polymerase II and undergo canonical RNA processing, including splicing, polyadenylation, and 5' N7-methylguanosine (mG) capping. However, whether cytoplasmic recapping of lncRNAs can promote the production of functional micropeptides remains poorly understood. In this study, we investigated the functional consequences of cytoplasmic recapping of MALAT1, a highly abundant and broadly expressed lncRNA. We demonstrate that cytoplasmic processing and recapping of a MALAT1 decay intermediate support the production of a previously uncharacterized micropeptide, designated MARS (MALAT1 Responsive to Stress). We further identify RNA G-quadruplex (rG4) structures in the 5' region of MALAT1 that inhibit MARS translation, revealing an additional layer of structural regulation. MARS expression increases in response to arsenite-induced oxidative stress and is associated with nuclear factor erythroid 2-related factor 2 (Nrf2)-containing complexes. Furthermore, MARS is implicated in the regulation of antioxidant response element-associated genes, including SOD and CAT, suggesting a potential role in cellular antioxidant defense. Collectively, our findings identify a previously unrecognized functional output of cytoplasmic RNA recapping and expand the potential coding repertoire of lncRNAs. This study highlights a mechanism through which cytoplasmic RNA processing may contribute to micropeptide production and suggests a role for MALAT1-derived MARS in oxidative stress-responsive gene regulation.

molecular biology↗

bioRxiv2026

Non-canonical alternate interactors of SARS CoV-2 Spike protein implicated in placental susceptibilty

Vertical transmission of SARS-CoV-2 is controversial. Owing to the low expression of ACE2 along with TMPRSS2 in placenta, role of secondary receptors becomes significant. In our study we predicted twelve interactors of spike protein expressed in placenta through interactome analysis followed by docking and molecular dynamic simulation. Three interactors of SARS-CoV-2 spike protein were found to be CD147, EGFR and LDLR. Expression at transcriptomic and translational level by real-time PCR and immunohistochemistry respectively was observed in placenta samples obtained from women with COVID exposure risk and compared to control. Elevated protein levels of CD147 (OR=14.667), ACE2 (OR=8.25) and LDLR (OR=1.9) was associated with higher exposure risk. However, the variation in mRNA expression was not statistically significant. Computational study showed that CD147/basigin, EGFR and LDLR stably interact with spike protein outside its primary receptor binding domain. In-vitro binding assay confirmed the physical interaction between spike protein and the placental receptors. In-silico site-directed mutagenesis further identified key potential binding hotspots of spike protein- Tyr28 and Trp64 for CD147, Phe643, Val642, Thr632 for LDLR and Leu8, Phe59, Phe32, Tyr28, Trp64, Trwp152, His245, and Val213 for EGFR. Finally, concluded that CD147, EGFR and LDLR might act as secondary non-canonical cofactors for SARS-CoV-2 spike protein in placenta, thereby increasing its susceptibility and tissue tropism. The study overall gives a better insight into potential placental suscpetibility providing targeted therapeutic opportunities.

microbiology↗

bioRxiv2026

Interchain base-stacking and spermine bridges govern ssDNA condensation and reentrant transitions

Polyvalent-cation-induced condensation and reentrant transition in double-stranded DNA are classically described by backbone electrostatics. Whether the same principles govern single-stranded DNA, whose solvent-exposed bases can interact within and between chains to form complex phases, is unknown. Here, we employ simulations and experiments to show that the tetravalent cation spermine (Spm4+) drives two equilibrium transitions in poly(dT) and poly(dA). The onset of phase separation, resulting in thermodynamic equilibrium between the dispersed low-density phase and high density coacervate, occurs at a low sequence independent Spm4+ concentration and is primarily driven by charge balance between phosphate groups and Spm4+. Strikingly, the condensed phases are stabilized by at most 1.5 (poly dT) and 2.5 (poly dA) bridging Spm4+ per chain. The ssDNA chains condense to form fluid-like networks without chain collapse. The fluidity of chains in the dense phases is confirmed by fluorescence recovery after photobleaching experiments. Both simulations and experiments show that the range of spermine concentration over which phase coexistence persists is sequence dependent and nearly threefold wider in poly(dA). At higher sequence dependent spermine concentrations, a reentrant phase, characterized by dissolution of the coacervates, emerges. The transition to the reentrant phase occurs when the number of bridging Spm4+ molecules vanishes at high sequence dependent spermine concentration. The combination of experiments and simulations show that the two phase transitions in ssDNA are driven by an interplay of stacking and electrostatic interactions.

biophysics↗

bioRxiv2026

Protein Language Model-Conditioned Graph Neural Networks for Multitask GPCR Ligand Activity Prediction

Predicting ligand activity across G protein-coupled receptors (GPCRs) requires models that capture both molecular structure and receptor-specific information while remaining robust to chemical and target-domain shifts. We developed a multimodal graph neural network that combines explicit ligand molecular graphs with frozen protein language model representations of GPCR sequences and jointly predicts quantitative pActivity and binary activity. The model was trained on 271,739 curated ligand-GPCR pairs spanning 183,694 ligands and 216 human GPCRs and evaluated using random, Bemis-Murcko scaffold, and strict cold-ligand partitions. With ESM-2 650M receptor embeddings, the selected model achieved mean absolute errors of 0.513 {+/-} 0.006, 0.540 {+/-} 0.006, and 0.641 {+/-} 0.005 pActivity units under random, cold-ligand, and scaffold evaluation, respectively, substantially outperforming a protein-aware fixed-feature multilayer perceptron and consistently improving quantitative prediction over a matched GINE reference. Removing receptor embeddings markedly degraded both regression and classification, whereas differences among ESM-2 35M, ESM-2 650M, and ProtT5 were comparatively small. Independent evaluation on 6,319 ChEMBL 37/BindingDB pairs revealed a substantial external domain shift, with mean absolute error increasing to approximately 0.94-0.95 despite chemically stringent internal validation. Nevertheless, receptor-specific information remained highly informative in a DRD2-DRD3 selectivity analysis: the cold-ligand ensemble achieved R^2 = 0.853, Spearman {rho} = 0.916, and ROC-AUC = 0.966 for strong DRD3 selectivity, whereas the receptor-independent ablation approached chance-level discrimination. These results establish protein-language-model-conditioned molecular graph learning as a scalable strategy for GPCR bioactivity prediction while identifying chemotype novelty, external-domain transfer, and richer ligand-receptor interaction representations as key remaining challenges.

bioinformatics↗

bioRxiv2026

A Nanomechanical Fingerprint of Centromeric Methylation Co-Localization in TNBC Chromosomal Instability

Centromeric heterochromatin is maintained by a self-reinforcing loop between DNA methylation (5mC) and histone H3 lysine 9 trimethylation (H3K9me3), yet whether this coupling is preserved at the level of individual chromosomes in cancer has been inaccessible to conventional epigenomic assays, which average signals across cell populations and cannot resolve single-chromosome heterogeneity. Here, we use antibody-functionalized atomic force spectroscopy to simultaneously map 5mC and H3K9me3 at the centromeres of individual metaphase chromosomes from non-tumorigenic, luminal, and triple-negative breast cancer (TNBC) cell models. Co-localized 5mC/H3K9me3 sites are mechanically stiffer than sites carrying either mark alone across the panel, revealing a cooperative biophysical signature of epigenetic crosstalk. Luminal chromosomes acquire the highest mark density, co-localization, and stiffness, whereas TNBC chromosomes show reduced density and diminished, though not absent, co-localization and stiffness, converging toward the low baseline of non-tumorigenic epithelium. TGF-{beta}1-induced epithelial-mesenchymal transition reproduces this attenuated phenotype in luminal cells, linking it directly to a process central to TNBC aggressiveness. These biophysical parameters classify cancer versus non-tumorigenic chromosomes with near-perfect, cross-validated accuracy, and further resolve the two TNBC subtypes using colocalization-derived features alone (macro-F1 = 0.933). These results establish the mechanical consequence of epigenetic mark co-localization as a single-chromosome-resolvable signature of centromeric organization, attenuated in aggressive breast cancer, and demonstrate a force-spectroscopy platform for mechano-epigenetic profiling of cancer chromosomes.

biophysics↗

bioRxiv2026

Multiple Fault Analysis and Drug Therapy on Signaling Pathways Using Dynamic Bayesian Network-based Model

Cancer-associated signaling pathways often exhibit abnormal activation under simultaneous dysregulation of multiple molecular components. This study presents a probabilistic temporal Dynamic Bayesian Network (DBN)-based framework for analyzing multi-fault behaviour and intervention response in Growth Factor (GF) and Mitogen-Activated Protein Kinase (MAPK) signaling pathways. Unlike deterministic Boolean propagation, the proposed model represents each pathway component through an activation probability and propagates these probabilities over discrete time steps using soft-logic update rules. One-, two-, three-, and four-fault scenarios were systematically evaluated under a common lowest-burden input vector. The resulting output probabilities were summarized using an encoded pathway-burden score, and known-drug combinations were ranked using efficiency scores relative to no-intervention baselines. Pareto analysis was further used to balance intervention efficiency against drug-vector burden, while a custom dual-target search was performed to identify computational intervention hypotheses beyond predefined drug targets. Results showed that encoded burden increased with fault order in both pathways, with MAPK producing a higher baseline burden than GF. Among known-drug vectors, U0126+LY294002+Temsirolimus consistently emerged as the strongest low-burden candidate, achieving efficiency close to the maximum six-drug vector. Custom dual-target analysis identified ERK1/2+RPS6KB1 in GF and Raf+MEK1 in MAPK as high-impact computational target pairs. Runtime benchmarking showed that batched vectorized NumPy execution substantially improved scalability for higher-order fault simulations. Overall, the framework provides an interpretable and scalable approach for probabilistic pathway-level fault analysis and intervention prioritization.

bioinformatics↗

bioRxiv2026

Generative design of programmable asymmetric β-barrel nanopores

Protein nanopores are powerful tools for molecular sensing, sequencing, and separation, but designing pores with programmable function remains challenging. Native homo-oligomeric transmembrane {beta} barrels (TMBs) are used for these applications, but their uniform lumens limit spatial resolution and analyte discrimination. Although monomeric TMBs can be designed using energy-based methods, these approaches remain highly manual and limited to structural design rather than function. Here, we present a generative AI framework for TMB design, with backbone and sequence design models trained on a curated distillation set. We characterized 48 designs spanning 0.7-1.5 nm in diameter, diverse lumen chemistries, and hydrophobic thicknesses. Crystal structures closely match the design models. We demonstrate that our method produces customizable nanopores for ion sensing, DNA translocation, and transport across polymer membranes.

biochemistry↗

bioRxiv2026

Integron-Mediated Convergence of Carbapenemase and Disinfectant Resistance in Acinetobacter spp. from Critical Care Units

Acinetobacter spp. represents critical opportunistic pathogens driving severe bloodstream infections (BSIs) in intensive care unit (ICU) and neonatal intensive care unit (NICU) settings. The convergence of carbapenem resistance and emerging biocide tolerance, often mediated by mobile genetic elements, has intensified concerns regarding co-selection and persistence in clinical environments. A total of 90 molecularly confirmed Acinetobacter isolates (ICU = 44; NICU = 46) from bloodstream infections were analyzed. Antimicrobial susceptibility was determined using the Kirby-Bauer disk diffusion method in accordance with CLSI M100 (2024) guidelines and extended-spectrum {beta}-lactamase production was assessed by combined disc diffusion. Polymerase chain reaction (PCR) was employed to detect carbapenemase genes (blaVIM, blaNDM, blaIMP, blaOXA-23, blaOXA-58), biocide resistance determinants (qacE, qac{Delta}E1), and the class 1 integron-integrase gene (intI1). Multidrug-resistant (MDR) and extensively drug-resistant (XDR) phenotypes were identified in 71.1% (64/90) and 22.2% (20/90) of isolates, respectively. High resistance (>71%) was observed against meropenem and cephalosporins, whereas colistin (58.8%, 53/90) and amikacin (47.8%, 43/90) showed moderate susceptibility. The most prevalent genotypes were qac{Delta}E1 (76.6%, 69/90) and blaVIM (56.6%, 51/90). Statistical and network analyses revealed significant correlations between biocide and carbapenemase genes, identifying IntI1 as a primary driver of co-resistance. The findings indicate a significant co-occurrence of carbapenemase genes, biocide resistance determinants, and class 1 integrons among Acinobacter spp. isolates. These associations suggest that mobile genetic elements may contribute to the dissemination of resistance traits.

microbiology↗

bioRxiv2026

DOT1L-AF10-mediated H3K79me3 promotes NF-kB p65-dependent inflammatory activation in endothelial cells

DOT1L-catalyzed H3K79 methylation is a hallmark of actively transcribed genes and has been extensively studied in developmental and disease contexts. While DOT1L inhibition has emerged as a promising therapeutic strategy in cancer, its role in pro-atherogenic endothelial inflammation remains unclear. To investigate this, we utilized an in vivo partial carotid artery ligation model and observed increased DOT1L expression and H3K79me3 level. Consistently, in vitro studies employing a 3D-printed human coronary artery model and TNF- stimulation corroborated these results, showing elevated DOT1L expression and H3K79me3 deposition, while levels of H3K79me and me2 remained unchanged. Further analyses identified key DOT1L-containing complex (DotCom) components, AF10 and AF9 (upregulated) and AF17 (downregulated), as contributors to the enhanced H3K79me3 landscape. CUT&RUN sequencing showed prominent H3K79me3 enrichment at the RELA (NF-{kappa}B p65) promoter, corresponding with increased NF-{kappa}B p65 expression and activation. Notably, inhibition/knockdown of the methyltransferase DOT1L or overexpression of the demethylase FBXL10 significantly reduced H3K79me3 levels, thereby suppressing NF-{kappa}B p65 expression and attenuating endothelial inflammation, independent of canonical NF-{kappa}B p65 activation. These findings establish DOT1L-mediated H3K79me3 as a crucial epigenetic regulator of endothelial inflammation, highlighting a potential therapeutic avenue for mitigating NF-{kappa}B p65-driven pro-atherogenic endothelial dysfunction.

cell biology↗

bioRxiv2026

Allosteric disulfide control of ligand binding and endocytosis of the natural killer cell receptor for HLA-G

Human Leukocyte Antigen (HLA)-G is selectively expressed by fetal trophoblast cells that invade maternal tissue and encounter maternal natural killer (NK) cells early in pregnancy. In NK cells, the endosomal receptor KIR2DL4 responds to soluble HLA-G by inducing a broad transcriptional program to support placental development. Structural features of KIR2DL4 that control ligand binding and endocytosis are unclear. Random mutagenesis revealed that three cysteines in the first immunoglobulin domain of KIR2DL4 regulate endocytosis and uptake of HLA-G. We found that the atypical Cys10-Cys28 disulfide bond observed in the KIR2DL4 crystal structure is an allosteric disulfide with potential to switch to a conventional Cys28-Cys74 bond. KIR2DL4 in human cells exists in both disulfide-bonded states, as shown by mass spectrometry analysis. The Cys10-Cys28 bond in a Cys74Ser KIR2DL4 mutant was reduced by protein disulfide isomerase (PDI) in vitro. Inhibition of PDI prevented HLA-G uptake by KIR2DL4 in both transfected 293T cells and primary NK cells. Mutants in the Cys10-Cys28 configuration endocytosed spontaneously but did not bind HLA-G. Conversely, KIR2DL4 with a Cys28-Cys74 bond was at the plasma membrane and responded to soluble HLA-G by endocytosis and transcription of the interferon stimulated gene IFI44L, like wild-type cells. A purified Cys10Leu KIR2DL4 mutant, which exhibited a reduced tendency to oligomerize, bound HLA-G in a peptide dependent manner with an affinity in the low micromolar range. Disulfide switching from the Cys10-Cys28 to the Cys28-Cys74 form correlated with a distant allosteric change, as predicted by AlphaFold, that could reorient a D0 domain loop to facilitate HLA-G binding. Thus, conversion from an inactive state to an HLA-G binding form regulates KIR2DL4 cellular localization and function to promote fetal development.

immunology↗

bioRxiv2026

SIP-BERT: A multi-organism deep strategy for predicting self interaction in proteins

Self-interacting proteins (SIPs) are critical to cellular regulation, yet their experimental identification remains challenging due to high costs, inefficiencies, and frequent false positives. Leveraging recent advances in deep language models, we introduce SIP-BERT, a family of lightweight transformer-based models trained on organism-specific self-interaction datasets curated from existing protein-protein interaction databases. We developed three variants: SIP-BERT(H), SIP-BERT(Y), and SIP-BERT(HY) -trained on human, yeast, and combined datasets, respectively. These models significantly outperform existing methods, exceeding baseline accuracies by 18%, 8% and 15% respectively. SIP-BERT models also generalise effectively to under-annotated organisms such as the mouse and the fruit fly, achieving high recall despite minimal labeled data. Furthermore, structural analysis of predicted false positives using PDB-derived alpha-carbon distance maps reveals close spatial residue proximities, suggesting plausible but undocumented self-interactions. These results highlight the potential of SIP-BERT to uncover novel SIPs and expand our understanding of protein self-interaction across diverse species. The dataset and the developed models are available at https://github.com/CMATERJU-BIOINFO/SIP-BERT for academic use only.

bioinformatics↗

bioRxiv2026

NetPolicy-RL: Network-Informed Offline Reinforcement Learning for Pharmacogenomic Drug Prioritization

Large-scale pharmacogenomic screens provide extensive measurements of drug response across diverse cancer cell lines; however, most computational approaches emphasize point-wise sensitivity prediction or static ranking, which are poorly aligned with practical decision-making, where only a limited number of candidate drugs can be tested. We propose NetPolicy-RL, a biologically informed and decision-centric framework for pharmacogenomic drug prioritization that integrates network diffusion modeling with offline reinforcement learning. Drug selection for each cell line is formulated as an offline contextual bandit problem, enabling direct optimization of ranking quality rather than surrogate regression objectives. Mechanistic biological context is incorporated by propagating drug targets over curated interaction networks (STRING and Reactome) using random walk with restart, and combining the resulting diffusion profiles with cell-specific molecular importance derived from multi-omics data to compute network disruption scores. These biologically grounded signals are integrated with normalized drug response measurements to construct a joint state representation, which is optimized using an offline actor-critic architecture. Across held-out test splits, NetPolicy-RL consistently outperforms global ranking heuristics and learning-to-rank baselines, achieving statistically significant improvements in per-cell Normalized Discounted Cumulative Gain (NDCG@10) and substantial reductions in per-cell regret. Relative to GlobalTopK, the policy improves NDCG@10 for 88.7% of cell lines, while improvements exceed 95% compared with LambdaMART and regression-to-ranking baselines. Ablation analyses show that neither empirical response signals nor network-derived features alone are sufficient, and that their integration yields the most robust performance. Overall, this study demonstrates that combining mechanistic network biology with offline policy learning provides an effective and interpretable framework for drug prioritization in precision oncology.

bioinformatics↗

bioRxiv2026

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

--Alzheimers disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways. MicroRNAs (miRNAs) act as key post-transcriptional regulators by modulating messenger RNAs (mRNAs), and their disruption can influence synaptic function, neuroinflammation, and neuronal survival. In this study, we present an integrative transcriptome-driven framework to identify AD-associated miRNA-mRNA regulatory signatures and potential biomarkers. Transcriptomic and clinical data were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the GEO dataset GSE48552. Differential expression analysis (Welchs t-test with FDR correction) identified 148 significantly dysregulated genes (35 up-regulated, 113 down-regulated) between AD and cognitively normal controls. Experimentally validated and predicted miRNA-target interactions were integrated using miRTarBase and additional target resources, yielding 1,669,089 miRNA-gene interactions involving 3,055 unique miRNAs, with strong enrichment toward down-regulated gene targeting. Functional enrichment analysis revealed convergence of miRNA-regulated genes on synaptic signaling, neuronal communication, intracellular transport, apoptosis, oxidative stress, and PI3K-Akt/MAPK-related pathways. A bipartite miRNA-mRNA regulatory network (2,343 nodes; 14,603 edges) was constructed and analyzed using centrality metrics, highlighting key hub regulators including PBX1 and SLC7A5. Finally, supervised machine learning models trained on selected molecular features achieved strong performance, with ensemble approaches (XGBoost/LightGBM) demonstrating robust discrimination of AD from controls.

bioinformatics↗

bioRxiv2026

Multidimensional Cellular Micro-Compartments to Model Invasive Lobular Carcinoma Dormancy

Invasive lobular carcinoma (ILC) accounts for 10-15% of breast cancers. Despite favorable responses to anti-estrogen therapy, the dissemination of cancer cells and resistance to therapies are significant risks for patients with ILC. Late recurrences are prevalent in ILC, suggesting that disseminated tumor cell dormancy may be a mechanism preceding their late overt growth into metastatic lesions. Herein, we investigated the relationship between anti-estrogen resistance and dormancy through multidimensional in vitro models. The bioengineered platforms recapitulated the morphological characteristics of ILC and highlighted its distinction from invasive ductal carcinoma. Inducing a dormant phenotype revealed epigenetic changes and enhanced chemical and mechanical sensing of anti-estrogen-resistant ILC cells to the substrate surface, with p27Kip1 signaling playing a central role. We propose this platform as a high-throughput method to investigate the propensity of dormancy and its manifestation via a simplified and expedited approach.

bioengineering↗

bioRxiv2025

Tricotyledonous Seedling in Jute (Corchorus capsularis): First Observation During Tissue Culture Selection

White jute (Corchorus capsularis) is a dicotyledonous, annual, bast fiber-producing crop. Under controlled culture room conditions (27 {+/-} 2{degrees}C, 16 h light/8 h dark photoperiod) and in a 15 mg/L hygromycin-B supplemented nutrient medium, one out of 50 tested seeds (2%) exhibited a rare tricotyledonous seedling phenotype. This is the first documented occurrence of tricotyledon in white jute during tissue culture selection of the cultivar JRC321 (Sonali). Given the potential advantages of polycotyledon in other plant species--such as improved vigor, and biomass accumulation--this finding opens new avenues for investigating the genetic and physiological mechanisms underlying tricotyledon development in jute. Future studies could explore its implications for breeding programs aimed at enhancing fiber yield and production.

plant biology↗

bioRxiv2025

A brief exposure to rotenone alters microtubule dynamics resulting in aberrant elongation of primary cilia with impaired function

Rotenone, an environmental toxin, is widely used to model Parkinsons disease owing to its well-studied effect to inhibit mitochondrial complex I function and to increase Reactive Oxygen Species (ROS), thereby leading to dopaminergic neuron degeneration. Less appreciated is its impact on microtubules (MT), except for suggesting that it promotes microtubule depolymerization, raising the question of whether rotenone also impacts primary cilia (PC) that are microtubule-based signalling hubs, which are essential for neuronal function. Using hTERT-RPE1 (or RPE1) cells, which assemble PC upon serum starvation, we discovered that brief exposure to rotenone (2-6 h) at low concentration (100 nM) causes a striking elongation of PC and hyperacetylation of cytoskeletal microtubules. At this concentration, rotenone does not significantly alter mitochondrial dynamics or bioenergetics, and these phenotypes could not be reversed by pre-incubating cells with ROS scavenger NAC, or supplementing cells with NR. Thus, these observed effects of rotenone treatment in quiescent cells are predominantly independent of mitochondrial toxicity. Depletion of -TAT1, the -tubulin acetyltransferase, eliminated microtubule acetylation without preventing ciliary elongation, suggesting that rotenone drives PC extension through additional mechanisms. Importantly, rotenone increases soluble tubulin pools, owing to its microtubule destabilizing effect, which are likely the mechanism contributing to ciliary axoneme elongation as well as hyperacetylation of cytoskeletal microtubules. Critically, such aberrant increase in PC length impaired Sonic Hedgehog (SHH) signalling, which is exclusively transduced by PC during embryonic development and is critical for maintaining adult tissue homeostasis. Thus, our findings reveal that even brief, low-dose rotenone exposure induces aberrant elongation of PC in quiescent cells that assemble PC, while simultaneously disrupting the function of PC. Based on our observation and previous studies, we propose that such ciliary dysfunction represents an underappreciated mechanism by which rotenone contributes to the pathogenesis of neurodegenerative disease via affecting neuronal primary cilia.

cell biology↗

bioRxiv2025

Traumatic Conditioning Induces a Combination of Anesthesia Resistant Memory and Protein Synthesis Dependent Long-Term Memory in Adult Drosophila

Stressful experiences elicit long-lasting memory trace that often persists for life-time. Though storage of experience dependent memory is key to survival, post-traumatic memories are detrimental to the physical and emotional wellbeing. The generation of any form of long-term memory culminates from synaptic plasticity and potentiation through persistent change in protein synthesis. Drosophila has been a robust model for the study of memory mechanisms; olfactory memory paradigms have been enormously successful in elucidating neuroanatomical, molecular, physiological and signalling pathways underlying the process. Long-term memory (LTM) has been induced by protein synthesis dependent mechanisms followed by a post-learning consolidation process whereas a medium-term anaesthesia resistant memory (ARM) is mediated by the Radish protein in flies. Here we present a novel and simple memory paradigm in adult flies for inducing sustained stress-induced memory paradigm, where a single attractive odorant is associated with an aversive stimulus-copper sulfate, which provides bitter taste as well as sustained malaise due to toxicity. We find that the elicited memory is a combination of ARM and LTM which is dependent on the number of training cycles. The eight-cycle training paradigm leads to robust, CREB and protein synthesis dependent memory persisting for about 7 days. The mushroom body (MB) Kenyon cells neurons as well as the dopaminergic input neurons (PPL1 subset) are involved in the formation of memory mediated by enhanced calcium as well as synaptic elaboration in the {gamma} subset of the MB neurons.

neuroscience↗

bioRxiv2025

1-Deoxysphingolipids Require Very-Long-Chain Ceramide Synthesis to Induce ER Stress and Neurotoxicity

Sphingolipids play key roles in cellular systems both as membrane components and as signaling molecules. Their biosynthesis, which occurs in the endoplasmic reticulum (ER), begins with the condensation of an amino acid, typically serine, and a fatty acyl-CoA. Under certain pathological conditions, alanine can be substituted for serine in the condensation reaction, producing 1-deoxysphingolipids, which lack the 1-hydroxyl group on the sphingoid base. Unlike typical sphingolipids, 1-deoxysphingolipids are unable to accept a head group modification, which alters their metabolic processing and prevents their canonical degradation. The accumulation of these "headless" 1-deoxysphingolipids causes neurotoxicity in various neurological and metabolic disorders. Here, we conducted a genome-wide CRISPR-Cas9 screen to identify pathways leading to 1-deoxysphinganine-induced toxicity in SH-SY5Y cells, a model used to study neurotoxic responses. Our top genetic hits highlighted the pathway involved in synthesizing ceramides with very-long-chain fatty acids (C22-C26). Using CRISPR-Cas9-modified SH-SY5Y cells with loss-of-function (LOF) mutations in the TECR or CERS2 genes--both critical for producing very-long-chain ceramides--we validated that this pathway was essential for 1-deoxysphinganine-mediated toxicity. Furthermore, we demonstrated that the ceramide synthesis pathway is required for 1-deoxysphinganine to trigger ER stress, as evidenced by significantly increased expression of the unfolded protein response in WT, but not TECR or CERS2 LOF mutant, SH-SY5Y cells exposed to 1-deoxysphinganine. Collectively, these findings identify a specific metabolic pathway for 1-deoxysphinganine leading to very-long-chain 1-deoxyceramide production that culminates in ER stress and toxicity. The findings highlight potential therapeutic targets for neuropathological diseases caused by 1-deoxysphingolipid accumulation.

biochemistry↗