Biology subjects

Karmakar, A.

Publications and source records attributed to Karmakar, A..

6 recordsLinked to original sources

bioRxiv2026

Glycocalyx-Directed Enzymatic Hydrogels Unlocks Fibroblast Regeneration to Promote Diabetic Wound Healing

Chronic diabetic wounds remain a major clinical challenge because current therapies address infection or supplement growth factors without correcting the cellular dysfunction that prevents regeneration. We identify pathological glycocalyx thickening in diabetic dermal fibroblasts (DDFs) as a driver of elevated caspase 3, 8, and 9 expression and heightened apoptosis -- deficits that stall wound repair. Cleavage of sialic acid residues by neuraminidase (NMase) reverses this dysfunction, restoring fibroblast migration, proliferation, and contractility. We engineer a photo-crosslinked hybrid hydrogel combining methacrylated gelatin (GelMA) with high-molecular-weight methacrylated chitosan (HMW ChMA). ChMA increases storage modulus 8-12-fold, reduces pore size, and confers antibacterial activity against gram-positive and gram-negative bacteria, addressing infection susceptibility. The fortified HMW hybrid (HMWH) network enables sustained, localized NMase delivery that outperforms GelMA alone in resisting degradation and controlling release kinetics. NMase-loaded HMWH (N-HMWH) gels enhance DDF proliferation and migration in vitro, correlating with reduced focal adhesion size and increased turnover. In a diabetic rat model, N-HMWH patches achieve superior wound closure, outperforming EGF therapy, with robust epidermal regeneration, neovascularization, and collagen deposition. This work establishes glycocalyx-targeting hydrogels as a new class of wound therapeutics addressing the root cause of diabetic fibroblast failure, not just compensating with growth factors.

bioengineering↗

bioRxiv2026

Systematic Evaluation of Feature Representations for Cancer-Associated sORF Prediction in Non-coding RNA

Short open reading frames (sORFs) within non-coding RNAs (ncRNAs) have arisen as a hidden layer of gene regulation, encoding small peptides that represent a new class of cancer regulators with diagnostic and therapeutic potential. However, inferring associations between sORFs to specific cancer types remains challenging and requires computational approaches for accurate prediction. Recently, the CoraL framework introduced the first computational approach for predicting cancer-associated peptides, focusing primarily on model architecture while overlooking how feature extraction strategies influence predictive accuracy. We present a systematic evaluation of machine learning models and feature extraction approaches to predict cancer-associated sORFs across 15 cancer types. We benchmarked seven traditional machine learning algorithms combined with three feature extraction methods: k-mer frequency, Word2Vec embeddings, and genomic language model (gLM)-based embeddings. To our knowledge, this is the first study applying gLM-derived embeddings to the prediction of cancer-associated sORFs in ncRNA. Our results show that traditional machine learning models with appropriate feature extraction outperform the CoraL baseline across all cancer types, achieving up to 10% higher accuracy in some of the 15 evaluated datasets. Interestingly, k-mer features consistently outperformed gLM embeddings without fine-tuning, suggesting that local sequence composition may provide more discriminative information for this task and that pre-trained genomic representations may require task-specific adaptation to fully capture these patterns. Additionally, we observed that the way sequences are tokenized, such as the k-mer length, can affect performance: longer fragments (e.g., k=7) sometimes reduced accuracy for Random Forest but had a smaller effect on MLP. Our findings suggest that appropriate feature engineering can provide greater improvements than increasing model complexity.

bioinformatics↗

bioRxiv2026

Benchmarking siRNA Prediction: The Role of Representation and Validation Strategies

Small interfering RNAs (siRNAs) offer transformative potential for targeted therapeutics, yet the design of highly effective and non-toxic candidates is hindered by the risk of off-target effects and RNA instability. A critical flaw in in silico prediction models is pervasive data leakage in cross-validation protocols, which artificially inflates performance metrics and produces untrustworthy results. To address this, we developed a rigorous framework that eliminates data leakage through strict cross-validation, leverages z-curves (3D representations of RNA physico-chemical properties) for context-aware sequence encoding, and identifies key sequence regions critical for efficacy. Our model achieves an AUC of 0.845 on leakage-free validation, surpassing prior work at 380x faster computation speed, demonstrating that superior representation trumps model complexity. Crucially, we demonstrate how experimental variability and cross-validation choices directly impact model reliability, establishing the first benchmarked methods for robust siRNA efficacy prediction. This work provides a foundation for trustworthy sequence design and validation in RNA therapeutics.

bioinformatics↗

bioRxiv2026

Nanobodies against the S2 region of the spike protein potently neutralize SARS-CoV-2 viruses and show resistance to virus escape

Entry of coronaviruses into cells is mediated by the viral spike (S) glycoproteins each consisting of S1 receptor binding and S2 membrane fusion subunits. The sequence of the S2 region is very highly conserved amongst variants of SARS-CoV-2 and compared to the S1 unit shares significant sequence identity amongst different beta-coronavirus lineages. By targeting the S2 of SARS-CoV-2 we have identified two selective and potent neutralizing nanobodies (BA.1-C2 and BA.1-D3) that bind to two different quaternary epitopes in the S2 formed by the Heptad Repeat 2 (HR2) trimer at the base of the spike protein. The HR2 sequence is identical in SARS-CoV and SARS-CoV-2 but differs in other beta-coronaviruses explaining the lack of binding to the spike proteins of MERS-CoV or HuCoV-OC43. No viral escape was observed following serial passaging of SARS-CoV-2 (JN.1) with a combination of BA.1-C2 and BA.1-D3 and the most potent of these nanobodies reduced viral load in the hamster model of COVID-19, following intranasal administration. Overall, the results show the value of nanobody technology for identifying novel neutralising epitopes in the S2 region of beta-coronaviruses with potential for the development of new selective anti-viral agents.

molecular biology↗

bioRxiv2025

Multi-site Assessment of Methods for Cell Preservation Upstream of Single Cell RNA Sequencing

Single cell RNA sequencing (scRNA-seq) is a revolutionary technique to identify cell types and their molecular phenotype in heterogeneous biological specimens. ScRNA-seq typically requires fresh, high quality single cell suspensions that are processed immediately to preserve their molecular profiles. This presents a challenge for samples with long preparation times and prevents collection at remote sites lacking the required instrumentation for sample processing. Recently, several commercial assays have been released that enable sample preservation at the time of collection either via fixation or cryopreservation, allowing for sample processing to occur months after the initial collection. The Association of Biomolecular Research Facilities (ABRF) DNA Sequencing (DSRG) and Genomics Bioinformatics (GBiRG) Research Groups have undertaken a cross-platform, multi-site study to assess the performance and reproducibility of three platforms: a) 10x Genomics FLEX, b) Parse Bioscience Evercode WT v2 and c) Honeycomb Bio HIVE. Total leukocytes were isolated from a single healthy individual using the EasySep RBC depletion reagent. Cells were then characterized by collecting a 21-color flow cytometry dataset for reference and the remaining material was used for scRNA-seq procedures where different sites then processed either the fixed or cryopreserved cells for each method. We evaluated performance of each method across traditional scRNA-seq quality control metrics and analysis applications, including gene/transcript detection sensitivity, cell type discovery and annotation, and differential expression. We demonstrate that data from the methods tested can be effectively integrated and produce concordant results with regard to cell type annotation and relative abundance, though we observe platform-specific differences in the expression of a subset of genes. Preservation-based methods also show better retention of fragile granulocyte populations compared with fresh samples processed using the 10x 3 workflow. The improvements to preservation methods are changing the way research is conducted and our thorough investigation into the performance of each method provides a valuable resource to help scientists determine the most appropriate single cell preservation workflow given their sample collection logistics and laboratory infrastructure constraints.

genomics↗

bioRxiv2023

Universal Digital High Resolution Melt for the detection of pulmonary mold infections

BackgroundInvasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning. MethodsA universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. ResultsU-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered. ConclusionsU-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.

microbiology↗