Biology subjects

Saran, S.

Publications and source records attributed to Saran, S..

4 recordsLinked to original sources

bioRxiv2026

LINE-1 Retrotransposon Activation and 5-Gene Dysregulation as a Common Pathological Signature in Post-Traumatic and Status Epilepticus Models of Epilepsy.

The transition from an initial brain insult to chronic epilepsy remains a critical challenge in clinical neuroscience, often involving complex molecular shifts that are difficult to capture through single-gene analysis. This study investigated the existence of a convergent pathway of genomic instability across two distinct etiological models: iron-induced (FeCl3) post-traumatic epilepsy (PTE) and lithium-pilocarpine-induced (LiCl-Pilo) status epilepticus (SE). By integrating Electrophysiology (EEG), {gamma}-oscillation spectral analysis, multi-unit activity (MUA) recordings with behavioural assessments Morris Water Maze (MWM) and Open Field Test (OFT) we validated and established the chronic hyperexcitability and associated cognitive- emotional co-morbidities of these two models. Which further served as a functional backdrop for investigating a targeted 8-gene panel consisting of GABRA2, KCNN2, KCNAB1, GRIK1, BCL11A, BRD7, STX1B, and PNPO. RT-PCR analysis demonstrated a systemic dysregulation of 5 genes out of these 8 genes, mirroring the simultaneous collapse of inhibitory signalling, membrane stability, and neurotransmitter metabolism seen in human phenotypes. While the clinical roles of these genes are well documented, our study demonstrates that these rat models provide a high-fidelity platform for complex multigene studies and gene-level therapeutic exploration. Furthermore, in-silico docking identified Fisetin as a high-affinity ligand for the dysregulated proteins, offering a promising multi-target strategy for stabilizing the epileptic genome.

neuroscience↗

bioRxiv2024

Cell-type specific prediction of RNA stability from RNA-protein interactions

RNA-binding proteins (RBPs) are important contributors to post-transcriptional regulatory processes. The combinatorial action of expressed RBPs and non-coding factors bound to the same transcript determines post-transcriptional properties of the mRNA in a context-dependent manner. To gain a better understanding of RNA stability and translational activity across different conditions, we have compiled and analyzed a set of ribosome profiling datasets for four human cell lines and used existing and newly generated metabolic labeling data to determine matching RNA degradation rates. We then used machine learning methods to predict RNA degradation rate and translation level from RBP binding information, which comprised existing in vivo binding datasets and computationally predicted binding sites. Utilizing this new RNA stability resource, we predicted RNA degradation rate and translation level from RBP binding alone. In vivo binding sites had higher importance for prediction compared to computationally predicted binding sites, likely due to confounding effects. We further explored the feature importance of different RBPs for stability prediction in the context of differential stability conferred by 3UTR isoforms. Taken together, an RNA stability machine learning model trained on one context successfully generalizes but is impacted by the availability and reliability of current data.

bioinformatics↗

bioRxiv2024

Selective disruption of Traf1/cIAP2 interaction attenuates inflammatory responses and limits sepsis and rheumatoid arthritis

Tumor necrosis factor receptor-associated factor 1 (TRAF1) is an immune signaling adapter protein linked to increased susceptibility to rheumatoid arthritis (RA). TRAF1 has dual roles in regulating NF-{kappa}B and MAPK signaling: it promotes signaling through its association with cellular inhibitor of apoptosis 2 (cIAP2) downstream of certain tumor necrosis factor receptor (TNFR) family members but inhibits Toll-like receptor (TLR) signaling by limiting linear ubiquitination of key signaling proteins. Here, we identify a critical mutation in TRAF1 (V203A in humans, V196A in mice) that disrupts its interaction with cIAP2, leading to a significant reduction in TLR signaling and downstream inflammation in human and murine macrophages. We demonstrate that TRAF1 is recruited to the TLR4 complex and is indispensable for the recruitment of cIAP2, facilitating TAK1 phosphorylation and the activation of NF-{kappa}B and MAPK signaling pathways. Remarkably, mice harboring the TRAF1 V196A mutation are protected from LPS-induced septic shock and exhibit markedly reduced joint inflammation and disease severity in a collagen antibody-induced arthritis (CAIA) model of RA. These findings reveal a previously unrecognized and crucial role for the TRAF1/cIAP2 axis in promoting inflammation and offer a promising foundation for the development of novel therapeutic strategies for inflammatory conditions, such as sepsis and RA.

immunology↗

bioRxiv2020

"Tracking the Rain Bird": Modeling the monthly distribution of Pied cuckoo in India

Pied cuckoo Clamator jacobinus (Boddart, 1783) is a migratory, brood-parasitic bird found in the African and Indian Subcontinent. Although the southern Indian population is presumably resident, the North Indian Population migrates from Africa to India during the summer. The arrival of the bird is linked to the onset of monsoon in India from scientific literature to folklore. It is known to make its appearance in central and northern India in the last week of May or early June, indicating the imminent arrival of the monsoon with its unmistakably loud metallic calls. There have been few attempts to compile relevant information on the species migration in the early 1900s and citizen science approach by Bird-count India; little information is available on how environmental factors might be affecting its migration. Here, we have used Maximum Entropy modeling to identify the monthly and seasonal distribution patterns and major bioclimatic factors that might be influencing the distribution of the species in India. We have used E-Bird citizen science platform data, seven bioclimatic variables, and monthly NDVI of respective months for building the models. The predicted output shows the species presence throughout the year in southern India. In contrast, in northern India, the distribution is dynamic, peaking in summers in the Month of May-June and no presence in winter. The influence of bioclimatic variables used in SDM varied monthly; Water vapor pressure was the primary contributing variable in the months prior to species arrival. In July, it was NDVI (Higher NDVI suggests abundance of food resources for the species). In August-September, Windspeed and water vapor pressure (Factors might be responsible for the departure of the species) have contributed highest. Our approach provides a more concise understanding of Pied cuckoos monthly distributions throughout India, which helps understand the complex seasonal shifts in the distribution of such migratory birds.

ecology↗