Biology subjects

Sarkar, C.

Publications and source records attributed to Sarkar, C..

18 recordsLinked to original sources

bioRxiv2026

KH1 Domain of Poly C Binding Protein 1 (PCBP1) Interacts with Utrophin-A mRNA and Modulates Utrophin-A Expression in Mouse Myoblasts

Utrophin upregulation is an attractive strategy to ameliorate Duchenne Muscular Dystrophy (DMD). Here, we investigated whether the murine poly(C)-binding protein 1 (PCBP1) directly interacts with the utrophin-A mRNA, and how the interaction shapes the fate of utrophin-A in muscle cells. As no experimentally validated structure of the murine PCBP1 is available, AlphaFold2 was utilized to predict the structure of the full-length protein and the KH1-KH2 domain of the protein. The more stable and reliable model of the KH1-KH2 domain of PCBP1 was then docked with the 5'UTR of the utrophin-A mRNA, and the top-ranked docked complex was utilized for MD simulation to identify the most stable hydrogen bond-forming amino acid residues from PCBP1. ITC showed a thermodynamically favorable spontaneous interaction between the full-length PCBP1 and the 5'UTR of utrophin-A mRNA, but the deletion mutant of PCBP1, lacking the predicted contact region, failed to exhibit any interaction. This observation was also confirmed by an RNA-immunoprecipitation assay in C2C12 cells. However, the mutant PCBP1 was unable to alter the expression of the utrophin-A in the C2C12 cells. In cells, PCBP1 overexpression lowered utrophin-A expression, while shRNA knockdown produced reciprocal increases. A 5'UTR-luciferase reporter mirrored this observation. Finally, subcellular fractionation showed that PCBP1 promotes nuclear retention of utrophin-A transcripts and reduces their cytoplasmic availability, with reciprocal shifts upon PCBP1 knockdown. Considering earlier work on PCBP2, our data suggest both paralogs recognize the same 5'UTR element, and the KH1-dependent interaction represses utrophin-A by coupling nuclear retention to reduced protein output. Thus, the PCBP1-5'UTR interaction emerges as a potent target for utrophin de-repression in DMD.

molecular biology↗

bioRxiv2026

Next-generation insect digitization: combining phenomics and genomics by subsequent synchrotron X-ray imaging and DNA sequencing

Recent technological advances allow for the large-scale acquisition of genetic and morphological data: high-throughput sequencing has transformed the field of genomics while synchrotron X-ray microtomography enables rapid, noninvasive 3D imaging. However, integrating these approaches for the same specimens is challenging because X-rays can fragment DNA, and DNA extraction damages internal morphology, particularly relevant for small bodied organisms, such as insects. We systematically tested multiple extraction protocols and irradiation conditions across three model insect species. We irradiated more than 1,000 specimens under varying conditions and tested DNA quality through DNA barcoding and UCE sequencing. Our results demonstrate that high-quality DNA and high-resolution tomograms can be obtained from the same individuals, provided that the parameters are carefully optimized and rapid SR-CT scanning precedes DNA extraction. In this respect, our findings establish practical guidelines for combining genomics and phenomics, paving the way for comprehensive integrative digitization of biodiversity.

genetics↗

bioRxiv2026

Evolutionary diversification of the biological screw joint in weevils

Complex biomechanical innovations are often treated as discrete evolutionary breakthroughs, yet their diversification within large radiations remains poorly understood. Beetle leg joints provide a striking example: some weevils possess screw-like coxa-trochanteral articulations in which rotation and axial displacement are mechanically coupled, resembling engineered screw-and-nut mechanisms. Whether these joints represent isolated mechanical extremes, discrete adaptive types or part of a broader continuum of phenotypic variation has remained unknown. Here we combine synchrotron X-ray microtomography, landmark-free atlas-based morphometrics, quantitative functional morphology and phylogenetic comparative analyses to examine the mesocoxa-trochanteral joint in 68 specimens representing seven sampled family-level groups across early-diverging and derived weevil lineages. We show that screw joint evolution combines continuous variation in trochanteral shape with a restricted set of mechanically plausible joint-character combinations, rather than forming sharply separated morphological classes. True screw-and-nut joints are not confined to a distinct region of morphospace, indicating that overall form and mechanical configuration are not necessarily coupled. The occurrence of this configuration in the early diverging Caridae shows that it is not restricted to more derived families. Three-dimensional helix fitting revealed a mosaic geometry, with winding angle showing the clearest relationship with overall shape and joint architecture, whereas axial pitch varied largely independently of shape, size and lineage. Together, these patterns show that screw joint components diversified with different degrees of evolutionary integration. These results recast the weevil screw joint from a singular biomechanical curiosity into a diversified evolutionary system. They suggest that complex functional structures can evolve through the gradual recombination and differential persistence of structurally constrained and evolutionary flexible components, rather than through a single shift from simple to fully specialized designs.

zoology↗

bioRxiv2026

Learnable Graph Network Model (LGNM): A Physics Constrained Graph Neural Network with Quantum Hamiltonian Learning

Elastic Network Models (ENMs), particularly the Gaussian Network Model (GNM) and its distance-weighted variant (mENM), predict per-residue protein flexibility from C contact graphs at low computational cost. Their central limitation is the assumption of uniform spring constants, which ignores the chemical identity, burial depth, and evolutionary conservation of individual residue contacts. We introduce the Learnable Graph Network Model (LGNM), a heterogeneous ENM in which per-edge spring constants{theta} ij = fi {middle dot} fj {middle dot} (dc/rij)2 are parameterised by per-residue flexibility coefficients {fi} predicted by a physics-constrained Graph Neural Network (GNN). The GNN is trained on molecular dynamics (MD)-derived root-mean-square fluctuation (RMSF) profiles from 413 proteins in the ATLAS database, using fold-disjoint CATH superfamily splits. The learning objective is an instance of the Quantum Neural PDE (QNPDE) Hamiltonian learning framework, with K = 3 operator types enabling an O(K) quantum gradient versus O (N3) classical pseudo-inversion. On 91 held-out test proteins, LGNM achieves mean per-protein Pearson correlation r = 0.8549{+/-} 0.1055, versus r = 0.8024 {+/-} 0.1167 for mENM ({Delta}r = +0.0525; 77/91 proteins improved). The implementation of this methedology is aviliable at https://lgnm.compbiosysnbu.in/ allowing researchers to evaluate flexibility and downstream processses.

bioinformatics↗

bioRxiv2026

Deep-Interact Studio: An Interactive Deep Learning Model Building Platform for Biomolecular Interaction Prediction

MotivationDeep learning has rapidly become essential for predicting biomolecular interactions; however, most web-tools expose only a single, pre-built model with a fixed, non-configurable architecture that users cannot redesign, retrain on their own data, or compare; they are typically dedicated to one interaction type and often one species, and report prediction scores with little interpretability. These constraints force researchers across several disconnected, single-purpose tools and limit the flexibility, reproducibility, and long-term usability of existing platforms. ResultsWe present Deep-Interact Studio, a unified, web-based deep-learning platform that addresses these limitations by shifting interaction prediction from a model-centric to a user-driven, comparative, and interpretable paradigm. Within a single interface spanning all four interaction classes, namely protein-protein, drugtarget, RNA-protein, and protein-DNA, users design their own model architectures layer by layer, configure training hyperparameters, and train them on their own data, including custom, species-specific datasets. Multiple user-built models can then be trained under identical conditions and compared side by side at both the training and inference levels, while integrated interpretability, including SHAP-based feature attribution, embedding-space visualization, and interaction hub analysis, turns predictions into auditable, mechanistically grounded results. Deep-Interact Studio is, to our knowledge, the only such platform to combine fine-grained per-layer model customization with multi-model comparison and interpretability, offering a flexible and transparent alternative to fixed, single-purpose tools. Availability and implementationDeep-Interact Studio is freely available as a web application at https://deepinteract.compbiosysnbu.in/, with no login or installation required.

bioinformatics↗

bioRxiv2026

MoE-Bind: Guiding De Novo Protein Binder Generation with Sparse Experts

De novo protein binder design has been dominated by structure-based pipelines that require known three-dimensional target conformations and consume substantial compute and generation time per design, limiting their throughput and accessibility for routine large-scale binder exploration. Sequence-only generative models promise a faster and lighter alternative, yet existing systems remain uniformly dense and frequently reintroduce structural computation at inference, undermining the core advantages they were intended to deliver. Across the broader language modelling community, transformers have meanwhile transitioned from fully dense designs to sparse Mixture-of-Experts architectures that decouple capacity from per-token compute, a shift that has yet to reach sequence-only protein binder generation. We present MoE-Bind, an autoregressive protein binder generator that, for the first time in this domain, combines Multi-head Latent Attention with a sparse Mixture-of-Experts feed-forward network and is evaluated under two independent structure predictors, Boltz-2 and AlphaFold2-Multimer. Despite activating less than half the per-token parameters of compute-matched dense baselines, MoE-Bind matches or exceeds them on full-length receptor-conditioned binder generation on a leakage-free Docking Benchmark 5.0 evaluation, transfers without peptide-specific training to short-peptide design, and reduces training and inference compute by a large margin. Routing analysis on generated binders reveals interpretable expert specialization at both the individual amino acid and biochemical group level, a structured expert-token alignment not previously reported for natural-language MoE models. These results show that sparse architectural design, rather than scale, can deliver fast, structure-free, and interpretable protein binder generation.

bioinformatics↗

bioRxiv2026

ARACoFusion: Uncertainty-aware calibrated deep learning for protein-protein interaction network prediction in Arabidopsis thaliana

Accurate mapping of the Arabidopsis thaliana protein-protein interaction (PPI) network is essential for deciphering complexity of plant systems biology. Here, we present ARACoFusion, a specialized deep learning architecture designed to predict inter-protein connectivity directly from primary sequences. To capture the asymmetric dependencies between plant proteins, the framework utilizes a reciprocal cross-attention encoder combined with latent interaction projections and multi-source feature fusion. Addressing the severe class imbalance inherent in plant interactomes, the model integrates uncertainty-aware variance regularization and focal loss with label smoothing, further enhancing reliability through post-hoc probability calibration via temperature scaling. Extensive benchmarking on gold-standard Arabidopsis datasets demonstrates that ARACoFusion significantly outperforms existing plant-specific predictors, achieving superior scores in Area Under the Precision-Recall Curve (AUPRC), Balanced Accuracy, and Matthews Correlation Coefficient (MCC). Additionally, the model exhibits robust cross-species generalization and clear class separability in t-SNE latent space visualizations. To facilitate community-wide usage, we provide a dedicated web server for scalable network-level inference at https://ARAcofusion.compbiosysnbu.in/.

bioinformatics↗

bioRxiv2026

Rubicon modulates neuroimmune responses following traumatic brain injury

Traumatic brain injury (TBI) elicits robust neuroinflammation and oxidative stress, coupled with an acute inhibition of macro-autophagy (autophagy) in neurons and microglia. Rubicon (Rubcn), a Beclin1 interacting protein that suppresses autophagy and mediates LC3-associated phagocytosis and endocytosis (LAP/LANDO), influences inflammatory signaling in metabolic, neurodegenerative, and inflammaging diseases; yet its role in acquired brain injury has not been defined. Using a controlled cortical impact model, we investigated the role of Rubicon in acute neuroinflammatory responses following injury by comparing wild-type and Rubcn-mutant mice. Bulk-RNA sequencing of injured cortex revealed attenuated induction of inflammatory pathways and reduced activation of pro-inflammatory microglial/macrophage phenotype in injured Rubcn-mutant mice. Rubcn-mutant mice demonstrated less pronounced inhibition of autophagy during the acute phase of injury. Although the inflammatory dicerences were transient, Rubicon mutant mice exhibited improved motor coordination and gait stability during recovery. Proteomic analyses revealed the presence of a truncated Rubicon protein in the mutant mice and identified the negative regulator of reactive oxygen species (NRROS) as a novel interactor of Rubicon. Consistent with this interaction, Rubcn-mutant mice displayed markedly reduced oxidative damage, indicated by decreased lipid peroxidation after injury. Together, these findings indicate that Rubicon promotes acute neuroinflammatory and oxidative stress responses following TBI by modulating autophagy and ROS production. Rubicon mediated pathways may serve as therapeutic targets that ocer a neuroprotective strategy to improve outcomes after TBI.

neuroscience↗

bioRxiv2026

Angiotensin II- Angiotensin II Receptor Type 1 Signaling Facilitates Gastric Cancer Metastasis via Kruppel-like Factor 4 Suppression and Tight Junction Breakdown

Cell-Cell adhesion maintained by tight junctions (TJs) is essential for epithelial integrity; loss of TJs correlates with poor prognosis, metastasis, and adverse clinical outcome in gastric cancer (GC). Restoring TJ integrity is therefore considered a promising therapeutic strategy in GC. The study identifies the stomach renin angiotensin system (stRAS) as a crucial regulator of TJ function in GC. Using integrative analysis of GC patient tissues, human GC cell lines, and orthotopic GC xenograft models, here we show that angiotensin II (ATII), the principal effector peptide of stRAS, drives TJ disassembly through an autocrine loop involving angiotensin receptor type 1 (AT1R) expressed on GC cells. Both ATII and AT1R are overexpressed in GC, where they suppress the expression of key TJ proteins. By analyzing global RNA-sequencing (RNA-seq) data and performing CRISPR/Cas9 gene deletion, chromatin immunoprecipitation, and functional assays, we mechanistically reveal that ATII, which is predominantly produced by cancer cells within the tumor microenvironment (TME), inhibits the expression of kruppel-like factor 4 (KLF4), a transcription factor crucial for the transcription of key TJ genes (CLDN1, 3, 4, and TJP1), leading to reduced synthesis of TJ proteins via AT1R expressed on cancer cells. Notably, the study demonstrates the effectiveness of pharmacological inhibition of AT1R with clinically established AT1R antagonists in preventing GC growth and metastasis by restoring TJ stability in vivo. These findings delineate a previously unrecognized role for ATII in governing TJ disassembly in GC and highlight the ATII/AT1R axis as a promising therapeutic target. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/698396v3_ufig1.gif" ALT="Figure 1000"> View larger version (33K): org.highwire.dtl.DTLVardef@a1b9adorg.highwire.dtl.DTLVardef@1a53c5dorg.highwire.dtl.DTLVardef@11e3e1corg.highwire.dtl.DTLVardef@7fd735_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

High-throughput phenomics of global ant biodiversity

The big data era in biology is underway, but the study of organismal form has been slow to capitalize on advances in imaging and computation. Modern imaging can digitize whole organisms, but low throughput has limited the effort to document morphological diversity. Within the open science initiative Antscan, we applied high-throughput synchrotron X-ray microtomography to capture phenotypes across a diverse and ecologically dominant insect group -- ants. We provide 2193 whole-body 3D ant datasets from 792 species to broadly cover the ant phylogeny with a global scope, also pairing phenomic data with genome sequencing projects. Scans acquired with standardized parameters facilitate automated analysis and free access to data can broaden the audience and incentivize methods development. Antscan presents a scalable approach to create libraries of diverse anatomies, heralding a new era of studies on the evolution, structure, and function of organismal phenotypes.

zoology↗

bioRxiv2025

Lysosomal multi-omics reveals altered sphingolipid catabolism as driver of lysosomal dysfunction in the aging brain.

Recent data indicate that lipid composition has profound influence on the brain function and that changes in lipid homeostasis affect brain aging and predisposition to neurodegenerative diseases. Lipids dynamically reside in multiple intracellular locations and their organellar distribution is important for specific interactions and biological function. During brain aging lipid changes have been specifically noted in lysosomes, but the identity of the accumulated lipids, their interactions with other biomolecules such as proteins, and their functional relevance have not been characterized. We used mass spectrometry (MS) to assess longitudinal changes in the lipidome and proteome of lysosomes isolated from the mouse cortex, from the age of 3- to 24-months. Our statistical and machine learning analyses identified two factors demonstrating predictive power for age and differences in both lipids and proteins. Of these, factor 1 was the best predictor of sample age. Factor 1 lipids with the highest feature importance included multiple species of hexosylceramides (HexCer) and their sulfonated derivatives, sulfatides (SHexCer), all of which increased with age. Increased factor 1 proteins included myelin proteins, select sphingolipid catabolism enzymes and proteins associated with lysosomal storage diseases. Our analyses suggested that mechanisms underlying factor 1 encompass the combination of an age-dependent increase in lysosomal delivery of myelin components and alterations in lysosomal sphingolipid catabolism favoring degradation of sphingomyelin over HexCer. The overall age-related lysosomal changes resembled those observed in lysosomal storage diseases, particularly Gaucher disease, where accumulation of HexCer species is associated with lysosomal dysfunction. To corroborate factor 1 predictions, we employed a combination of biochemical, imaging and flow cytometry approaches, which confirmed alterations in sphingolipid catabolism and lysosomal accumulation of myelin components. These changes were associated with age-related alteration in lysosomal morphology, lysosomal dysfunction and inhibition of autophagy in both neurons and microglia. Our findings indicate that factors contributing to lysosomal aging resemble those observed in lysosomal storage diseases and underscore the significance of organelle-specific analyses for dissecting mechanisms contributing to brain aging.

neuroscience↗

bioRxiv2025

Inhibition of autophagy-lysosomal function exacerbates microglial and monocyte lipid metabolism reprograming and dysfunction after brain injury

CNS has an overall higher level of lipids than all tissues except adipose and contains up to 25% of total body cholesterol. Recent data demonstrate a complex crosstalk between lipid metabolism and inflammation, suggesting potential contribution of the lipid-rich brain environment to neuroinflammation. While recent data support the importance of brain lipid environment to inflammatory changes observed in age related chronic neurodegenerative diseases, in vivo interactions between lipid environment, lipid metabolism and neuroinflammation in acute brain disease and injury remain poorly understood. Here we utilize a mouse model of traumatic brain injury (TBI) to demonstrate that acute neurotrauma leads to widespread lipid metabolism reprograming in all microglial and brain associated and infiltrating monocyte populations. Additionally, we identify unique microglial and monocyte populations with higher degree of lipid metabolism reprograming and pronounced accumulation of neutral storage lipids, including cholesteryl esters and triglycerides. These lipids accumulate not only in lipid droplets but also in the microglial and monocyte lysosomes and are associated with lysosomal dysfunction and inhibition of autophagy after TBI. Our data indicate that lipid accumulation in these cells is the result of altered lipid handling rather than lipid synthesis and is triggered by phagocytosis of lipid-rich myelin debris generated after TBI. Finally, we use mice with autophagy defects in microglia and monocytes to demonstrate that further inhibition of autophagy leads to more pronounced lipid metabolism reprograming and exacerbated cellular lipid accumulation. Our data suggest a pathological feedback loop, where lipid phagocytosis causes inhibition of autophagy-lysosomal function, which in turn exacerbates cellular lipid retention, reprograming and inflammation.

neuroscience↗

bioRxiv2025

Idebenone Enhances the Early Microglial Response to Traumatic Brain Injury and Mitigates Acute Gene Expression Changes to Ephrin-A and Dopamine Signaling Pathways

Traumatic Brain Injury (TBI) leads to persistent pro-inflammatory microglial activation implicated in neurodegeneration. Idebenone, a coenzyme Q10 analogue that interacts with both mitochondria and the tyrosine kinase adaptor SHC1, inhibits aspects of microglial activation in vitro. We used the NanoString Neuropathology panel to test the hypothesis that idebenone post-treatment mitigates TBI pathology-associated acute gene expression changes by moderating the pro-inflammatory microglial response to injury. Controlled cortical impact to adult male mice increased the microglial activation signature in peri-lesional cortex at 24 hours post-TBI. Unexpectedly, several microglial signature genes upregulated by TBI were further increased by post-injury idebenone administration. However, idebenone significantly attenuated TBI-mediated perturbations to gene expression associated with behavior, particularly in the gene ontology:biological process (GO:BP) pathways "ephrin receptor signaling" and "dopamine metabolic process." Gene co-expression analysis correlated levels of microglial complement component 1q (C1q) and the neurotrophin receptor gene Ntrk1 to large (>3-fold) TBI-induced decreases in dopamine receptor genes Drd1 and Drd2 that were mitigated by idebenone treatment. Bioinformatics analysis identified SUZ12 as a candidate transcriptional regulator of idebenone-modified gene expression changes. Overall, results suggest that idebenone enhances TBI-induced microglial proliferation within the first 24 hours of TBI and identify Ephrin-A and dopamine signaling as novel idebenone targets.

neuroscience↗

bioRxiv2022

Bioassay-guided isolation and characterization of anti-bacterial compound from Sonneratia apetala Buch. Ham leaf, from Maharashtra coast of India

Mangroves, collection of plants growing in coastal intertidal zones, are rich source of unique bioactive phytochemicals present in their bark, roots, leaves and fruits. They show anti-diabetic, anti-cancer, anti-hypertensive, anti-malarial, anti-bacterial, anti-inflammatory and anti-oxidant properties. In this study, a bioassay-guided isolation and characterization approach was taken to study the anti-bacterial compound from Sonnerartia apetala Buch. Ham leaf. Petroleum ether, acetone and water extracts were prepared and tested against eight human pathogenic bacteria. Water and acetone extracts have shown the inhibition of bacteria but the acetone extract was carried forward for further study. The compound was isolated using preparative column chromatography method. The isolated compound was studied for anti-bacterial activity using TLC-bioautography . The bioactive compound was isolated and tested against standard antibiotics and showed positive results against E. coli and B.subtilis. The isolated pure compound was studied for detection of elements and melting point. Further chacterization was done using UV-Vis spectroscopy and FT-IR spectroscopy indicating absorption in the UV spectrum and presence of aromatic and aliphatic groups. This study validates the importance of Sonneratia apetala leaves as an important resource in traditional medicine for anti-bacterial remedies aimed at human pathogenic diseases.

pharmacology and toxicology↗

bioRxiv2021

Molecular signature of postmortem lung tissue from COVID-19 patients suggests distinct trajectories driving mortality

The precise molecular mechanisms behind life-threatening lung abnormalities during severe SARS-CoV-2 infections are still unclear. To address this challenge, we performed whole transcriptome sequencing of lung autopsies from 31 patients suffering from severe COVID-19 related complications and 10 uninfected controls. Using a metatranscriptome analysis of lung tissue samples we identified the existence of two distinct molecular signatures of lethal COVID-19. The dominant "classical" signature (n=23) showed upregulation of unfolded protein response, steroid biosynthesis and complement activation supported by massive metabolic reprogramming leading to characteristic lung damage. The rarer signature (n=8) potentially representing "Cytokine Release Syndrome" (CRS) showed upregulation of cytokines such IL1 and CCL19 but absence of complement activation and muted inflammation. Further, dissecting expression of individual genes within enriched pathways for patient signature suggests heterogeneity in host response to the primary infection. We found that the majority of patients cleared the SARS-CoV-2 infection, but all suffered from acute dysbiosis with characteristic enrichment of opportunistic pathogens such as Staphylococcus cohnii in "classical" patients and Pasteurella multocida in CRS patients. Our results suggest two distinct models of lung pathology in severe COVID-19 patients that can be identified through the status of the complement activation, presence of specific cytokines and characteristic microbiome. This information can be used to design personalized therapy to treat COVID-19 related complications corresponding to patient signature such as using the identified drug molecules or mitigating specific secondary infections.

molecular biology↗

bioRxiv2020

Elucidation of novel miRNA candidates and their role in unraveling the pathology of Non-Alcoholic Fatty Liver Disease

Non-Alcoholic Fatty Liver Disease (NAFLD) is a chronic liver disease which is observed in people who do not abuse alcohol. Main cause of NAFLD is Non-Alcoholic Steatohepatitis (NASH) where there is accumulation of fats such as triglyceride in liver and the disease progression ranges from simple steatosis to fibrosis and cirrhosis. MicroRNAs are critical players in post-transcriptional gene regulation of diseases with complex etiology. In this study, we have elucidated the role of microRNAs (miRNAs) in the pathophysiology of NAFLD/NASH and unravelled molecular markers for diagnosis of NAFLD. A subset of genes (n=10) responsible for NAFLD/NASH were selected and detailed in silico analysis carried out using multiple tools. miRDB and DIANA-microT were used to find putative miRNA binding sites followed by analysis using miRTarBase which is an experimentally validated database of miRNA-target interactions. The study elucidated a number of statistically significant predictions for both miRDB (scores >80) and DIANA-microT (values >0.90) and also strong experimental validation in miRTarBase. The analysis revealed that certain miRNAs like miR-7 & miR-548 family members are found in both the programmes, miRDB and DIANA-microT, targeting genes involved in liver function. They were also identified in the experimental validation database miRTarBase. These miRNAs probably play an important role in the pathophysiology of this disease. They can also be used as prognostic/diagnostic markers for assessment of NAFLD.

molecular biology↗

bioRxiv2019

N-acetyl-L-leucine treatment attenuates neuronal cell death and suppresses neuroinflammation after traumatic brain injury in mice

Traumatic brain injury (TBI) is a major cause of mortality and long-term disability around the world. Even mild to moderate TBI can lead to lifelong neurological impairment due to acute and progressive neurodegeneration and neuroinflammation induced by the injury. Thus, the discovery of novel treatments which can be used as early therapeutic interventions following TBI is essential to restrict neuronal cell death and neuroinflammation. In this study, we demonstrate that orally administered treatment with N-acetyl-L-leucine (NALL) led to attenuation of cell death and reduced the expression of neuroinflammatory markers after controlled cortical impact (CCI) induced experimental TBI in mice. Our data indicate that partial restoration of autophagy flux mediated by NALL may account for the positive effect of treatment in the injured mouse brain. Taken together, our study indicates treatment with NALL would be expected to be beneficial in restricting neuronal death and hence improving neurological function after injury, and is a promising novel, neuroprotective drug candidate for the treatment of TBI.

neuroscience↗

bioRxiv2016

Codon based co-occurrence network motifs in human mitochondria

Human mitochondrial DNA (mtDNA) generates a highly divergent pool of alleles even within species that have dispersed and expanded in size. On top of that codon position bias plays a relative role in prompting population wide sequence based variations. Codon positions are immensely studied as independent evolutionary genetic markers, however co-evolution of codon positions are still largely unexplored. Using 18,411 genome samples, we analyze nucleotide co-occurrences over mitochondrial DNA of human sub-populations covering most of the mtDNA diversity on the earth. Our codon based network motif investigation reveals that codon position is one of the critical factor to form higher order motifs. It is interesting to report a different evolution of Asian genomes than those of the rest which is divulged by motifs in non-coding regions. Most notably, we demonstrate that there are preferences of codon position co-evolution with respect to codon position sensitivity implying the selective role of evolutionary processes on fundamental formation of genetic code in terms of codon bias evolution. These network based findings not only recapitulates the established facts regarding the importance of each codon position in providing codon bias but also highlight their preferential role in resulting correlated mutations. Moreover, codon based nucleotide co-occurrence provides framework to investigate important evolutionary insights into human mitochondrial evolutionary genomics.

evolutionary biology↗