Biology subjects

Pramanick, A.

Publications and source records attributed to Pramanick, A..

5 recordsLinked to original sources

bioRxiv2026

Dityrosine photocrosslinking of native collagen bioinks for controlled shape-fidelity of bioprinted cardiac tissue constructs: probing the interplay between fibrillogenesis and covalent bond formation

Collagen bioinks are widely used in biofabrication, but their relatively soft mechanical properties can lead to structural instabilities under cell-generated contraction forces. While synthetic functional groups can be conjugated for covalent crosslinking, these methods often disrupt natural protein fibrillogenesis, thereby compromising collagen fibre architecture. This work presents a strategy for the direct covalent stabilisation of native collagen bioinks with dityrosine bonds via visible-light photocrosslinking with ruthenium (Ru) and sodium persulfate (SPS), avoiding the need for polymer pre-functionalisation. Multimodal characterisation, including high-resolution microscopy, spectroscopy, mass spectrometry, and nanoindentation, identified photocrosslinking conditions that enhance collagen fibrillogenesis and reduce off-target polymer oxidation. Interestingly, the biofabrication process itself affected ultimate collagen fibre architecture, with shear-induced alignment during extrusion enhancing fibril proximity and self-assembly, overcoming inhibitory effects the crosslinkers had on fibrillogenesis via ionic and electrostatic interactions. Leveraging these insights, embedded bioprinting was used to fabricate cardiac constructs with high cell viability (>80%), where dityrosine crosslinking could be tuned to modulate geometric shape changes under cell-generated forces (1-15% shrinkage). Finally, the platform was used to bioprint anatomically accurate double-ventricle human heart models with robust shape fidelity. This research establishes a versatile photocrosslinking framework for bioprinting cardiac constructs with tunable shape stability using native collagen bioinks.

bioengineering↗

bioRxiv2026

Developmentally inspired bioprinting of nascent multicellular human heart tissue through in situ differentiation and morphogenesis of iPSCs

Traditional heart tissue bioprinting typically relies on using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes that are pre-differentiated in 2D culture. This approach differs fundamentally from embryonic heart development, where mesodermal progenitors differentiate into cardiomyocytes within 3D, matrix-rich, and shape-morphing microenvironments. Here, we introduce a novel developmentally inspired approach that enables in situ mesodermal and cardiac differentiation of iPSCs within bioprinted, shape-morphing pluripotent tissues. Using embedded bioprinting, Matrigel bioinks with high-density iPSC suspensions were deposited into granular support hydrogels to generate pluripotent tissue constructs with defined architectures. These constructs exhibited shape-morphing behaviour, tunable by modulating the support bath viscoelasticity. Support bath mechanics also regulated iPSC fate, with softer formulations reducing spontaneous differentiation. Building on this, mesodermal induction and cardiogenesis were directly driven within the morphing constructs via temporal WNT pathway modulation, resulting in multicellular cardiac tissues in which cardiomyocytes, fibroblasts, and endothelial cells co-emerge from a common progenitor pool. Importantly, these nascent tissues underwent structural maturation, with immunofluorescence and gene expression profiling revealing cardiac progenitors alongside maturing cardiomyocytes. Together, these findings highlight the potential for a new paradigm in biofabrication focused on printing pluripotent organ rudiments that recapitulate key aspects of embryonic development and support progressive tissue maturation.

bioengineering↗

bioRxiv2025

A Transformer based method for the Cap Analysis of Gene Expression and Gene Expression Tag associated 5' cap site prediction in RNA

5 RNA capping is one of the major post-transcriptional modifications for the mobility and stability of RNA molecules. Measuring 5 caps of RNAs can help quantify expression levels of mRNAs and lncRNAs. One of the most successful RNAseq methods that have used capping as a tool to quantify expression of transcription is Cap Analysis of Gene Expression(CAGE). Computational prediction of capping can therefore be used as a precursor to the prediction of transcriptional expression. Unfortunately, there is hardly any computational technique that has focused purely on predicting 5 capping. We have developed a transformer-based method for computational prediction of capping from DNA sequences. Our Llama and ReLoRA-based pre-training model, and Llama and LoRA-based fine-tuning model predict 5 cap sites. We have used Leave-one-chromosome-out-cross-validation for our model. The average accuracy, and F1-score after fine-tuning the human genome hg19(mouse genome mm9) for sequence classification is 79.12%(78.09%), and 78.11%(76.17%), respectively. We noted attention peak-based motifs having an aggregate Wilcoxon rank-sum p-value of 1.075e-10 between the attention peak region and the entire context window for the predicted positive motifs; an aggregate p-value of 7.17e-18 for the predicted negative motifs; and an aggregate p-value of 6.70e-08 between the attention peaks of the predicted positive and the predicted negative motifs. Our Llama-based approach aims to create a sequence-based framework to identify 5 capping sites corresponding to CAGE peaks. Our analysis reveals statistically significant motifs from the regions of peak attention scores, which demonstrates biological relevance for some through their resident sites matching with known TF motifs.

bioinformatics↗

bioRxiv2025

DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool for TCR Epitope interaction using Context-aware Transformers

Background: The development of personalized cancer vaccines relies on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Traditional computational approaches focus primarily on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of TCR binding. Furthermore, the clinical applicability of existing methods is constrained by fragmented pipelines that require separate workflows for variant calling, HLA typing, and independent peptide-MHC (pMHC) or peptide-TCR (pTCR) evaluation stages. Results: We present DeepPROTECTNeo, a unified deep learning framework that integrates genomic variant detection, HLA typing, high-affinity pMHC binding prediction, variant-driven TCR repertoire mining, followed by a hybrid transformer-Convolutional Neural Network dual-branch feature extractor with an explicit cross-attention-based deep learning model for TCR-epitope binding prediction. Our reverse vaccinology-inspired biologically informed architecture integrates Bidirectional Long short-term memory (Bi-LSTM) sequence features, convolutional-attention physicochemical/evolutionary descriptors via gated fusion, and TCR numbered contextual embeddings to enable residue-level interpretable modelling. Under a strict TCR-split strategy, it achieved a mean AUROC of 0.7856 and AUPRC of 0.7932 outperforming six state-of-the-art predictors by 4-5% with tight inter-fold stability. The architecture maintains high robustness against structural hard negatives and imbalanced datasets, successfully recovering 18 of 34 validated high-affinity neoepitopes from a patient-specific cancer cohort. Conclusions: Experiments results demonstrate that DeepPROTECTNeo is a powerful, reliable end-to-end neoantigen prioritization framework that effectively models complex TCR-epitope interfaces directly from clinical sequencing data, providing a robust interpretable foundation to accelerate personalized cancer immunotherapy.

bioinformatics↗

bioRxiv2024

4D bioprinting shape-morphing tissues in granular support hydrogels: Sculpting structure and guiding maturation

During embryogenesis, organs undergo dynamic shape transformations that sculpt their final shape, composition, and function. Despite this, current organ bioprinting approaches typically employ bioinks that restrict cell-generated morphogenetic behaviours resulting in structurally static tissues. Here, we introduce a novel platform that enables the bioprinting of tissues that undergo programmable and predictable 4D shape-morphing driven by cell-generated forces. Our method utilises embedded bioprinting to deposit collagen-hyaluronic acid bioinks within yield-stress granular support hydrogels that can accommodate and regulate 4D shape-morphing through their viscoelastic properties. Importantly, we demonstrate precise control over 4D shape-morphing by modulating factors such as the initial print geometry, cell phenotype, bioink composition, and support hydrogel viscoelasticity. Further, we observed that shape-morphing actively sculpts cell and extracellular matrix alignment along the principal tissue axis through a stress-avoidance mechanism. To enable predictive design of 4D shape-morphing patterns, we developed a finite element model that accurately captures shape evolution at both the cellular and tissue levels. Finally, we show that programmed 4D shape-morphing enhances the structural and functional properties of iPSC-derived heart tissues. This ability to design, predict, and program 4D shape-morphing holds great potential for engineering organ rudiments that recapitulate morphogenetic processes to sculpt their final shape, composition, and function.

bioengineering↗