Biology subjects

Ekambaram, S.

Publications and source records attributed to Ekambaram, S..

10 recordsLinked to original sources

bioRxiv2026

ALSmiR: a curated evidence resource for ALS-associated microRNAs for biomarker discovery

Amyotrophic lateral sclerosis is a multifactorial, late-onset neurodegenerative disease with incompletely understood pathogenesis. Limited survival after symptom onset creates an urgent need for biomarkers to support diagnosis, prognosis, and patient stratification. MicroRNAs are among the candidate biomarkers investigated in this disease. Although reviews have synthesized microRNA expression findings, differences in patient populations, biospecimens, and methods complicate comparisons. In the age of data-driven research, we need an updated, searchable resource that links each finding to its context and incorporates growing evidence. Here, we present ALSmiR, a public, manually curated database of polymerase chain reaction-based microRNA expression findings from human biological fluids and their components. ALSmiR currently contains 1,620 expression records from 49 publications comparing patients with amyotrophic lateral sclerosis with healthy controls, covering 816 microRNA names across 13 biospecimen categories. We link publication, cohort, and microRNA-result tables to preserve each finding's clinical and experimental context. We retain significant and non-significant results and provide searchable records and comma-separated values exports for independent analysis. We update ALSmiR annually and verify author submissions against source publications before inclusion. Researchers can use ALSmiR to assess candidate biomarkers, formulate hypotheses, and design independent validation studies. ALSmiR is freely accessible worldwide without registration at https://alsmir.dokhlab.org/.

bioinformatics↗

bioRxiv2026

Standardising Cellulose Production From Acetobacter diazotrophicus

Bacterial cellulose (BC) is a biopolymer that comes from natural sources. It has high purity, crystallinity, mechanical strength, and biocompatibility, which makes it suitable for various biomedical and industrial uses. Unlike plant cellulose, BC does not contain lignin or hemicellulose. This results in better material quality and simpler processing. However, producing BC on a large scale is limited by high production costs, expensive culture media, and dependence on a few bacterial strains. It is essential to find cost-effective production methods and alternative microbial sources to expand its commercial use. This study looked at the cellulose-producing ability of Acetobacter diazotrophicus, a safe and relatively unexplored bacterium, under different growth conditions. We compared bacterial growth and cellulose production using Hestrin-Schramm (HS) medium, the standard for BC production, and LB supplemented with glucose (LB+Glucose), which we explored as a more affordable option. We analyzed growth rates, inoculum age, and pH levels to find the best conditions for cellulose production. We observed faster bacterial growth in HS medium, with a doubling time of 3.906 hours, compared to 6.241 hours in LB+Glucose medium. Cellulose production was greatly affected by inoculum age, with successful synthesis from cultures that were agitated for 36 to 40 hours. The highest cellulose yield was at pH 6.0 in HS medium (4.6 mg/mL) and at pH 5.5 in LB+Glucose medium (3.6 mg/mL). FTIR analysis confirmed the presence of characteristic functional groups of bacterial cellulose. These results suggest that Acetobacter diazotrophicus is a promising and cost-effective option for producing bacterial cellulose and highlight the importance of medium composition, inoculum age, and pH for optimizing production.

microbiology↗

bioRxiv2026

Reconstruction of septin higher-order nano-size structures in ovarian cancer cells uncover susceptibility to the septin-targeting small molecule UR214-9

In cancer cells, septins assemble into enigmatic higher-order structures of 300-700 nanometers, including long needle-like filaments, thick perinuclear rings, and cytoplasmic bundles or aggregates. The absence of genetic or pharmacological tools to recapitulate these architectures in-vitro has impeded mechanistic studies of their formation, function, and therapeutic targeting. Here, first, determining the overexpression of septin-2 in epithelial ovarian cancer (EOC) and its association with increased mortalities and dependencies, we select SKOV-3 ovarian cancer cells as a tractable model in which septin supramolecular assemblies can be recreated in-vitro and interrogated. This system shows that the forchlorfenuron (FCF) analog UR214-9 remodels septin architecture, converting co-expressed human septin octamers (SEPT2-SEPT6-SEPT7-SEPT9-SEPT9-SEPT7-SEPT6-SEPT2) into large cytoplasmic aggregates. In parallel, transiently expressed SEPT2 is reorganized into septin-rich noodle-like filaments, perinuclear rings, and web-like networks encircling the nucleus upon UR214-9 treatment. Mechanistically, UR214-9 disrupts the incorporation of SEPT2, SEPT7, and SEPT9 into canonical septin hetero-octamers, resulting in assembly-defective or imperfect oligomers that preferentially reorganize into these aberrant higher-order structures. This aggregation likely prevents septin-2 migration during interphase-to-cleavage furrow transition in NRK-49F-SEPT2-EGFP homozygous cells and impacts SKOV-3 cytokinesis, cell proliferation, adhesion and invasion and migration while sparing ceramide transport to the Golgi, preserving ER and cis-Golgi structure. These effects manifested in reduced growth of ovarian, endometrial and breast cancer xenografts without attracting significant off-target engagements per the global transcriptomic analysis of JIMT1 breast cancer and PANC-1 pancreatic cells. UR214-9 treated animals showed observable safety in animals. Thus, a tool to recreate aberrant septin structures and identification of septins as a druggable cytoskeletal target for ovarian, endometrial, breast and pancreatic cancer by perturbing their hetero-octamerization assembly is presented. SignificanceWe provide a method to reconstruct the higher-order septin architecture observed in cancer cells, to study their assembly and functions. Intriguingly, cancer cells tolerate hetero-oligomeric septins lacking specific subunits, suggesting that compositionally deficient oligomers are not efficiently targeted for degradation, unlike unincorporated septin monomers in normal cells. This tolerance may enable accumulation of structurally aberrant septin complexes acquiring long-needles, rings or thick-aggregates in disease cells. We further show that septin oligomerization can be pharmacologically perturbed. By integrating structural, cellular, and energetic readouts using in-silico techniques, we establish a quantitative framework for septin-targeted modulation, generating UR214-9 as a new chemotype that disrupts septin oligomeric assembly via preventing incorporation of SEPT2/7/9, into canonical hetero-octamers, causes defects in cytokinesis, altered cell migration, viability, and remodels septin-actin architectures, ultimately impairing tumor cell growth. Thus, pharmacological targeting of septin assembly represents a tractable strategy to perturb septin-dependent cellular processes in cancer and neurodegenerative diseases with reported septin dysregulation.

cancer biology↗

bioRxiv2026

ATLAS: Graph-based 3D RNA Motif Library Incorporating non-Watson-Crick Interactions

The structures of RNA exhibit recurrent patterns, which are defined as motifs. Motifs are crucial for the biological functions of RNA molecules. The 3D RNA motif library includes 3D RNA structures, providing a resource for motif-based RNA 3D structure prediction and design. We built the Advanced Template Library for Assembly and Structure (ATLAS) that features graph representations of RNA structures, atomic 3D structures from the Protein Data Bank (PDB), and an isomorphism graph searching algorithm. ATLAS includes nucleotide-level graph representations of RNA motifs and corresponding PDB IDs from the PDB and 3D atomic structures retrieved from the PDB. We provide the web service to search for and download RNA motifs and user-defined structures, supporting non-WC interactions. Graph representations include two types of graphs: motifs with non-Watson-Crick (non-WC) and without non-WC. Compared to existing motif libraries, ATLAS includes graph representations with non-Watson-Crick interactions and pseudoknots and integrates the latest RNA structures from the PDB. We also develop a measure of similarity between RNAs based on shared motifs. We proposed a physics-inspired, time-dependent mechanistic model for RNA evolution, whose steady-state solution fits the global distribution of RNA structural similarity.

biophysics↗

bioRxiv2025

PSP-0119: Targeted IRAK4 Degradation as a Novel Therapeutic Strategy for FLT3-Mutant AML

Acute Myeloid Leukemia (AML) is a life-threatening hematologic malignancy. Despite recent therapeutic advances, rising incidence rates emphasize the urgent need for identification of new targets and therapies. Roles of interleukin receptor-associated kinases IRAK1/4 are emerging in hematologic and solid malignancies. In AML, IRAK4 mRNA is overexpressed at diagnosis, relapses, in residual disease, and in FLT3-ITD-mutant cells, MDS, MPN, and MDS/MPN-negative subtypes. Compared with hematopoietic stem cells, IRAK4 is elevated in t(15;17), inv(16)/t(16;16), and t(11q23)/MLL subtypes, correlating with poor survival. Here, we disclose anti-AML activity of PSP-0119, a novel IRAK4 PROTAC degrader. PSP-0119, inhibited IRAK4 kinase activity, NF-{kappa}{beta} activity, and IL-1{beta}-induced IRAK4 phosphorylation. In-silico docking revealed interactions in CRBN/IRAK4/PSP-0119 ternary complex. PSP-0119 degraded IRAK4 in FLT3-mutant AML cell lines sparing FLT3-wild-type AML cells, FLT3-wild-type patient samples, and normal bone-marrow. Bulk-seq of PSP-0119 treated MOLM-13 cells revealed downregulation of eNOS, a poor AML prognosticator. PSP-0119 suppressed colony formation, cell viability, and MOLM-13 xenograft growth, and synergized with IRAK1 covalent inhibitor JH-X-119-01. PSP-0119 is metabolically stable, retaining 71% of parent compound at 60 minutes in human liver microsomes. In summary, IRAK4 degradation via PSP-0119 as a promising therapeutic strategy for treatment of FLT3-mutant AML.

cancer biology↗

bioRxiv2025

Rationale design of peptide inhibitors for α-Synuclein liquid condensates and fibrillar aggregates using multiscale modelling approach

-Synuclein is an intrinsically disordered protein (IDP) whose aggregation is implicated in Parkinsons disorder. Herein, we computationally design a -Synuclein derived potential peptide inhibitor against the proteins monomeric, fibrillar and liquid condensate forms using multi-scale molecular modelling approaches. Since the conventional structure-based design paradigm often is not applicable to these highly labile IDPs, we first develop a pipeline to generate an exhaustive library of small candidate peptides from an available repository of -Synuclein 70 {micro}s all-atom molecular dynamics (AAMD) trajectory data. We then use high throughput screening techniques such as PATCHDOCK and HPEPDOCK as well as AAMD simulations to arrive at a single candidate peptide. AAMD simulations data show that -Synuclein bound peptide chain leads to an expanded conformational ensemble for the chain and also reduces the {beta}-sheet propensity of the proteins fibrillar amylogenic aggregates. Coarse-grained simulations using HPS-Cation forcefield with 100 chains of -Synuclein and varying levels of candidate peptides shows decreased density and increased apparent critical temperature of the condensate system. Our detailed molecular interactions analyses show that peptides bind to the -Synucleins through the "dynamic shuttling mechanism" where interaction are frequently made and broken around a given set of structurally proximal residues, which likely softens the dynamic interaction network in the condensates. Together, we could illustrate the inhibitory effect of the final designed peptide against distinct forms of -Synuclein monomer and aggregates. Our work provides a multiscale simulation based prescription towards the futuristic development of therapeutic strategies against the disordered proteins.

biophysics↗

bioRxiv2024

Comprehensive mapping of the Interaction of levodopa and iron metabolism in Parkinson's disease

Levodopa remains the primary treatment for Parkinsons disease (PD), yet its long-term use has been associated with iron accumulation in the brain, a phenomenon linked to neurodegeneration. We utilize deep machine learning to determine plausible molecular mechanisms that may underlie the effects of levodopa on iron metabolism. Using the DRIFT platform, we performed a proteome-wide target identification of levodopa and uncovered significant interactions potentially involved in cellular iron transport. Pathway analysis revealed that levodopa may influence critical iron-related pathways, including the response of EIF2AK1 to heme deficiency, heme signaling, and ABC-family protein-mediated transport. These findings suggest that levodopa may contribute to iron dysregulation in PD by interacting with iron transporters and modulating iron-related pathways. Because levodopa is used at relatively high doses in PD, our findings provide new insight into secondary effects unrelated to being a precursor of dopamine. This highlights the need for careful consideration of its effects on iron metabolism as a consequence of use in the long-term management of PD. Further experimental validation is required to confirm these interactions, and also to explore potential strategies to mitigate iron-related side effects while preserving therapeutic efficacy.

bioinformatics↗

bioRxiv2024

Chronic β3 adrenergic agonist treatment improves brain microvascular endothelial function and cognition in aged mice

Microvascular endothelial dysfunction, characterized by impaired neurovascular coupling, reduced glucose uptake, blood-brain barrier disruption, and microvascular rarefaction, plays a critical role in the pathogenesis of age-related vascular cognitive impairment (VCI). Emerging evidence points to non-cell autonomous mechanisms mediated by adverse circulating milieu (an increased ratio of pro-geronic to anti-geronic circulating factors) in the pathogenesis of endothelial dysfunction leading to impaired cerebral blood flow and cognitive decline in the aging population. In particular, age-related adipose dysfunction contributes, at least in part, to an unfavorable systemic milieu characterized by chronic hyperglycemia, hyperinsulinemia, dyslipidemia, and altered adipokine profile, which together contribute to microvascular endothelial dysfunction. Hence, in the present study, we aimed to test whether thermogenic stimulation, an intervention known to improve adipose and systemic metabolism by increasing cellular energy expenditure, could mitigate brain endothelial dysfunction and improve cognition in the aging population. Eighteen-month-old old C57BL/6J mice were treated with saline or CL ({beta}3-adrenergic agonist) for 6 weeks followed by functional analysis to assess endothelial function and cognition. CL treatment improved neurovascular coupling responses and rescued brain glucose uptake in aged animals. In addition, CL treatment also attenuated blood-brain barrier leakage and associated neuroinflammation in the cortex of aged animals. More importantly, these beneficial changes in microvascular function translated to improved cognitive performance in radial arm water maze and Y-maze tests. Our results suggest that {beta}3-adrenergic agonist treatment improves multiple aspects of brain microvascular endothelial function and can be potentially repurposed for treating age-associated cognitive decline.

neuroscience↗

bioRxiv2024

The metabolic benefits of thermogenic stimulation are preserved in aging

Adipose thermogenesis has been actively investigated as a therapeutic target for improving metabolic dysfunction in obesity. However, its applicability to middle-aged and older populations, which bear the highest obesity prevalence in the US (approximately 40%), remains uncertain due to age-related decline in thermogenic responses. In this study, we investigated the effects of chronic thermogenic stimulation using the {beta}3-adrenergic (AR) agonist CL316,243 (CL) on systemic metabolism and adipose function in aged (18-month-old) C57BL/6JN mice. Sustained {beta}3-AR treatment resulted in reduced fat mass, increased energy expenditure, increased fatty acid oxidation and mitochondrial activity in adipose depots, improved glucose homeostasis, and a favorable adipokine profile. At the cellular level, CL treatment increased uncoupling protein 1 (UCP1)-dependent thermogenesis in brown adipose tissue (BAT). However, in white adipose tissue (WAT) depots, CL treatment increased glycerol and lipid de novo lipogenesis (DNL) and turnover suggesting the activation of the futile substrate cycle of lipolysis and reesterification in a UCP1-independent manner. Increased lipid turnover was also associated with the simultaneous upregulation of proteins involved in glycerol metabolism, fatty acid oxidation, and reesterification in WAT. Further, a dose-dependent impact of CL treatment on inflammation was observed, particularly in subcutaneous WAT, suggesting a potential mismatch between fatty acid supply and oxidation. These findings indicate that chronic {beta}3-AR stimulation activates distinct cellular mechanisms that increase energy expenditure in BAT and WAT to improve systemic metabolism in aged mice. Our study provides foundational evidence for targeting adipose thermogenesis to improve age-related metabolic dysfunction.

pharmacology and toxicology↗

bioRxiv2024

Application of Quantum Tensor Networks for Protein Classification

Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various proteinrelated machine-learning problems. We classify proteins based on their sub-cellular locations--a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. Its noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.

bioinformatics↗