Biology subjects

Negi, S.

Publications and source records attributed to Negi, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods

A precise analysis of shape instabilities in cell-sized membrane compartments requires thorough quantification of the shape dynamics of individual components over extended periods. Herein, we have used phase-contrast microscopy to image giant unilamellar membrane vesicles (GUVs), which are widely used as model systems for cellular structures. GUVs were prepared in the fluid lamellar phase of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine with and without cholesterol dissolved within a potato starch solution. The morphological changes and budding dynamics of individual GUVs with different chemical compositions were quantified using a deep-learning based image analysis framework over extended time periods, by using phase-contrast micrographs as input data. This automated framework integrates contour-based detection of the mother vesicle with precise segmentation of daughter compartments, thereby enabling explicit identification and tracking of mother and daughter vesicles at high temporal resolution. These findings further facilitate the measurement of the temporal evolution of vesicle morphometrics including area, and local membrane curvature during budding, that can be used as an input to develop computational models aimed at elucidating curvature-driven membrane remodeling in complex soft materials.

biophysics↗

bioRxiv2026

A Saliva-Inspired Model of Enzymatic Starch Hydrolysis by Alpha Amylase

We demonstrate that the enzymatic activity of alpha-amylase (AA) can serve as an effective physicochemical trigger for membrane remodeling in giant unilamellar vesicles (GUVs). This work provides insight into how primitive protocellular reactions directly translate into the membrane's mechanical responses. Analysis of the potato starch utilized in this work is composed of ~26\% amylose and ~75\% amylopectin. Quantitative DNS analysis revealed time- and concentration-dependent accumulation of reducing sugars during starch degradation by AA. The HPLC-RID chromatograms demonstrated the synthesis of glucose, maltose, maltotriose, maltotetraose, maltopentaose, maltohexaose, and maltoheptaose, with maltose and maltotriose as the major products. The accumulation of these soluble hydrolysis products gradually increases the osmolarity of the extracellular medium surrounding the vesicles, leading to osmotic asymmetry across the membrane. This biochemical framework provides a quantitative basis for understanding membrane deformation, curvature generation, vesicle deflation, and budding. In addition to the specific starch--$\alpha$-amylase system studied here, our work uncovers a general mechanism by which external biochemical reactions control membrane shape transformations via osmotic coupling. Such enzyme-driven remodeling offers a simple yet powerful strategy for inducing dynamic structural changes in artificial cells. It may provide insight into primitive protocellular processes in which metabolic activity and membrane mechanics were inherently coupled.

biophysics↗

bioRxiv2026

Novel retinoic acid receptor-related orphan receptor gamma inverse agonist enhances checkpoint blockade in hepatocellular carcinoma: a preclinical mouse study

Background and Aims: Neutrophil-associated inflammation is associated with poor outcomes in hepatocellular carcinoma (HCC), but tumour-intrinsic regulators of neutrophil recruitment and checkpoint response remain unclear. We evaluated whether inhibiting retinoic acid receptor-related orphan receptor gamma (RORgamma) alters neutrophil recruitment, programmed death-ligand 1 (PD-L1) expression, and response to PD-L1 blockade. Approach and Results: Survival associations were analysed in The Cancer Genome Atlas HCC cohort. Neutrophil migration toward HCC cells and PD-L1 expression were assessed after RORC silencing or TF-S10 treatment. Immunocompetent mice bearing orthotopic Hep-55.1c tumours received vehicle, TF-S10, anti-PD-L1, or combination treatment. Higher S100A9 and CXCL8 expression was associated with shorter survival (P = 0.000459 and P = 0.0004413, respectively), and tumour-bearing mice had higher neutrophil-to-lymphocyte ratios (P < 0.0001). RORgamma; targeting reduced neutrophil migration and intratumoural Ly6G; infiltration while increasing tumour-cell PD-L1 expression in vitro. Combination treatment reduced endpoint liver tumour volume versus anti-PD-L1 monotherapy (P < 0.01). Half of combination-treated mice had tumour volumes less than or equal to 0.10 cm^3 and none exceeded 1.00 cm^3, compared with 62% of controls exceeding 1.00 cm^3. Histologically, 85% of combination-treated mice had less than 10% liver tumour involvement, versus no controls. Combination treatment also reduced tumour-associated neutrophil populations and peripheral neutrophil-to-lymphocyte ratios. All active treatments reduced pulmonary metastatic burden versus controls, without significant differences among treatment groups; 53% of combination-treated mice had no detectable lung metastases. Conclusions: RORgamma inhibition reduces neutrophil recruitment and increases tumour-cell PD-L1 expression. Combined RORgamma inhibition and PD-L1 blockade improves hepatic tumour control in preclinical HCC and warrants further investigation.

cancer biology↗

bioRxiv2026

Thermoplastic polyurethane as a base material for soft, high-density neural interfaces

Polymers used as substrate and encapsulation for implantable devices are typically deposited layer-by-layer, leaving polymer-polymer interfaces that adhere poorly and become a critical failure point. Thermoplastic polyurethane (TPU) elastomers combine four properties not found together in any other candidate: decades of clinical use in long-term implants and availability in medical grade, a stiffness well below that of polyimide and parylene C, compatibility with microfabrication, and thermoplastic self-fusion, which removes the polymer-polymer interface entirely. This work introduces TPU as a base material for soft, high-density neural interfaces and demonstrates one route to realizing them. Gold is patterned on a sacrificial copper sheet by maskless lithography and transferred into TPU by vacuum lamination, giving conformally coated tracks down to 15 um at 30 um pitch within a single, merged polymer body. An additional electroplating step produces a nanostructured pinecone gold surface that increases the electrochemically active area of the electrodes, approximately doubling the charge injection capacity. Daisy-chain silicon chips were also embedded within the soft polymer body. The films transmit up to 88% at 470 nm, a wavelength commonly used for optogenetics. TPU showed no cytotoxicity towards differentiated SH-SY5Y cells and only a mild tissue response after 9.5 months of subcutaneous implantation in rats.

bioengineering↗

bioRxiv2026

GraPPI: A Self-Supervised Graph Encoder for Transferable Protein--Protein Interaction Modeling

Protein--Protein Interactions (PPIs) are fundamental to cellular processes and represent major targets for therapeutic development. Recent advances in machine learning for protein modeling, particularly protein language models, have enabled powerful representations of protein sequences and monomeric structures. However, compared with the increasingly generalizable representations available for individual proteins, transferable representations explicitly designed to capture protein-complex and interface contexts remain less developed. Here, we developed GraPPI, a unified graph-based deep learning framework for learning transferable representations of protein--protein complexes. At its core, PPIencoder represents protein complexes as heterogeneous residual graphs and uses self-supervised masked-edge prediction to learn interaction-aware representations that integrate sequence, three-dimensional geometry, and physicochemical properties. We then evaluated the transferability of these pretrained representations across complementary PPI tasks spanning binding-mode classification, binding-affinity prediction, and mutation-associated affinity prediction. GraPPI accurately distinguished plausible binding interfaces from perturbed or decoy binding interfaces, achieving AUROC values above 0.92 on an independent benchmark. For protein--protein binding-affinity prediction, GraPPI achieved Pearson correlation coefficients of 0.690 and 0.684 on the S90 and S79 benchmarks, respectively, with performance competitive with or exceeding that of recently reported affinity-prediction methods. GraPPI further transferred effectively to mutation-associated binding-affinity prediction, achieving a Pearson correlation of 0.84 and a mean absolute error of 1.20 kcal/mol on an independent SKEMPI benchmark. Together, these results establish GraPPI as a generalizable framework for protein-complex representation learning, extending pretrained protein representations from individual proteins toward the interaction context in which protein function is realized.

biochemistry↗

bioRxiv2026

Competition between co-localized gut symbionts underlies inter-individual variation in the honeybee gut microbiota

Microbes in the animal gut compete to colonize spatially restricted host niches. However, whether competition for space drives inter-individual variation among hosts, and which factors determine the outcome of such competition, remain poorly understood. Here, we investigated competitive interactions of the western honeybee gut symbiont Frischella perrara with other members of the bee gut microbiota. Shotgun metagenomics analysis of individual bees revealed that F. perrara is negatively correlated with a specific species of the genus Gilliamella. Co-colonization of microbiota-depleted bees with these two bacteria resulted in their competitive exclusion. The outcome of this competition depended on the relative number of bacteria each bee received and benefited Gilliamella when one of the two type VI secretion systems of F. perrara was mutated. Using fluorescently tagged strains, high-resolution microscopy, and gut region-specific quantification, we show that both bacteria localize to the same host niche in the ileum of mono-colonized bees, indicating competition in a spatially restricted host niche. Moreover, both microbes protected against infection, promoting bee health. This competition provides an explanatory mechanism underlying variation in the occurrence of F. perrara across honeybee colonies and highlights the importance of gut spatial structure and microbial competition in shaping microbiome composition and inter-individual variability.

microbiology↗

bioRxiv2026

Protocol-dependent cardiomyocyte states determine disease modelling capacity of human iPSCs

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are widely used to model cardiovascular disease, yet numerous differentiation protocols generate cardiomyocytes with heterogeneous molecular and functional properties, complicating experimental design. Here we systematically compare sixteen commonly used cardiomyocyte differentiation protocols and characterize their resulting cell states using single-nucleus RNA sequencing, functional phenotyping and computational integration with human genetic data. Despite similar cardiomyocyte yields, protocols produced distinct transcriptional programs, subtype compositions and physiological properties. By integrating protocol-specific gene expression signatures with genome-wide association studies of cardiovascular traits, we identify cardiomyocyte states enriched for genetic architectures underlying specific diseases. These analyses accurately predict protocols most suitable for modelling particular disease contexts, including electrophysiological defects associated with Brugada syndrome and metabolic vulnerability relevant to myocardial infarction. Our results demonstrate that differentiation protocols encode biologically distinct cardiomyocyte states with differential disease relevance and establish a framework for aligning stem-cell differentiation strategies with human complex trait genetics to guide model selection. This approach enables rational design of iPSC-based disease models and highlights how population-scale genetic data can inform experimental systems in stem cell biology.

systems biology↗

bioRxiv2026

Hierarchical control of cardiomyocyte maturation and ischaemia sensitivity by metabolic culture conditions

Ischaemic heart disease remains the leading cause of mortality worldwide, yet no therapies prevent cardiomyocyte death during acute ischaemia-reperfusion injury (IRI). Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a platform for modelling cardiac injury, but their immature phenotype limits the physiological fidelity of in vitro models. Here, we systematically evaluated how experimental variables used during preparation of hiPSC-CM endpoint assays influence cardiomyocyte maturation and susceptibility to IRI. Integrating literature mining, molecular profiling, statistical genetics, and functional assays, we examined the effects of replating conditions, backbone media, metabolic substrates, and signalling modulators. We define the relationship between culture conditions and metabolic supplements in determining contractile maturation and sensitivity to IRI. Notably, we show that metabolic composition of the backbone medium establishes the baseline maturation state and determines responsiveness to additional maturation cues. These findings identify metabolic environment as a dominant regulator of injury susceptibility and provide a framework for improving the physiological fidelity of hiPSC-CM models of cardiac ischaemia.

cell biology↗

bioRxiv2025

Rapid, accurate long- and short-read mapping to large pangenome graphs with vg Giraffe

1We previously introduced Giraffe, a short-read-to-pangenome graph mapper available in the vg pangenomics toolkit. Giraffe was fast and accurate for mapping short reads to human-scale pangenomes, but struggled with long reads. Long reads present a unique challenge to pangenome mapping algorithms due to their length and error profile, which allow them to take more topologically complex paths through the pangenome graph and increase the possible search space for the algorithm. We present updates to Giraffe that allow it to quickly and accurately map long reads to pangenome graphs. For both short and long reads, Giraffe mapping to a pangenome containing data from more than 450 human haplotypes, generated by the Human Pangenome Reference Consortium, is comparable in speed to linear mappers to human reference genomes; Giraffe is also over an order of magnitude faster than GraphAligner, the current state-of-the-art long-read-to-pangenome mapper. Its alignments produce similar or improved small and structural variant calling results, compared to those from commonly used graph-based and linear mappers. We additionally demonstrate using Giraffes long read alignments in a pangenome-guided assembly workflow, which is capable of producing more contiguous local assemblies than Hifiasm in our test regions.

bioinformatics↗

bioRxiv2025

Epithelial-intrinsic nitric oxide synthase 2 sustains host-microbiota dynamics that promote colitis

The microbiota influence disease pathogenesis and treatment, however we have limited ability to assess patient status in relation to the microbiota. Here we find that the nitric oxide generating enzyme, nitric oxide synthase 2 (Nos2), is transcriptionally primed in intestinal epithelial cells (IECs), as opposed to immune cells, in inflammatory bowel disease (IBD) patients. Generation of IEC-specific Nos2 knockout mice revealed that epithelial Nos2 activity promoted susceptibility to intestinal disease and sustained a colitogenic microbiota. Epithelial Nos2 increased levels of nitric oxide-derived nitrates and nitrate-metabolizing bacteria in the intestine. Unexpectedly, extra-intestinal nitrates also reflected IEC-intrinsic Nos2 expression, and systemic nitrate concentrations in patients paralleled intestinal Nos2 activation. In fact, temporally inhibiting epithelial Nos2 was sufficient to alter intestinal nitrate homeostasis and inflammation in mice, as well as restrict nitrate production by human intestinal organoids. These data reveal that epithelial nitric oxide metabolism directs host-microbiota dynamics that can alter disease and that monitoring and targeting this axis may benefit patients with IBD.

immunology↗

bioRxiv2025

A complete diploid human genome benchmark for personalized genomics

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.

genomics↗

bioRxiv2025

Neuralace: Manufacture, Parylene-C Coating, and Mechanical Properties

Subdural electrode arrays have traditionally been used for epilepsy monitoring and surgical planning. With the emergence of brain-computer interface (BCI) applications, these arrays are now being explored for chronic use, leveraging their ability to record subdural signals for neural decoding. Transitioning from short-term implantation in epilepsy monitoring to long-term use in BCIs requires advancements through consideration of the foreign body response to ensure long-term durability and functionality. Biocompatibility challenges, such as fibrotic encapsulation and reactive astrogliosis, highlight the need for conformal subdural implant designs that minimize mechanical stress on neural tissue. This study investigates the mechanical properties of the Neuralace, a novel ultra-thin, high-channel-count mesh-type surface grid. We characterized the stiffness of the silicon-based interposer electrode meshes and evaluated the effects of various geometric configurations and polymeric encapsulation layers on the mechanical performance. Using a full factorial design of experiments framework and a custom low-force four-point bending setup, we identified the design factors that impact the stiffness of Neuralace structures. The results showed highly consistent stiffness measurements. The stiffness values of Neuralace structures ranged from 2.99 N/m to 7.21 N/m, depending on the cell-wall thickness (CWT) of the lace, the orientation angle, and whether the structures were encapsulated with parylene-C (PPXC). Orientation and CWT had the largest impact on the stiffness of the structures, while the effects of PPXC encapsulation were statistically significant but more subtle. The stiffest Neuralace configuration is expected to exert forces approximately 10 to 100 times lower than commercially available subdural implants would when conforming to radius of the smallest anatomical gyrus. This work demonstrates the feasibility of tailoring the mechanical properties of Neuralace to improve its suitability for chronic neural implantation, providing insights for future design iterations and conformable implant development.

neuroscience↗

bioRxiv2025

Quantitative AI-based DNA fiber workflow to study replication stress

Replication stress (RS) is a prominent source of genome instability and human diseases. Understanding its molecular mechanism through various quantitative and unbiased methodologies is essential for the advancement of treatment strategies. One of the powerful methods to study DNA replication dynamics and its alterations at the single-molecule resolution is the DNA fiber assay. However, this method relies exclusively on manual image acquisition and analysis, making it time-consuming and susceptible to user bias. Here, we present a quantitative AI-based DNA fiber (qAID) workflow enabling imaging and multiparameter analysis of thousands of DNA fibers within several dozen minutes. Our workflow quantifies key parameters, including DNA fiber frequency, length, and symmetry, while also allowing visual inspection of individual DNA fibers using unbiased image galleries. The robustness of the workflow is demonstrated by comprehensive datasets of biologically relevant experiments performed by three independent laboratories. Overall, qAID workflow provides a fast and effective examination of replication dynamics and its alterations at the single-molecule resolution.

cell biology↗

bioRxiv2024

Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation

Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants; however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs. Here, we leverage a new scalable wet-lab protocol and computational pipeline for whole-genome Oxford Nanopore Technologies sequencing and apply it to neurologically normal control samples from the North American Brain Expression Consortium (NABEC) (European ancestry) and Human Brain Collection Core (HBCC) (African or African admixed ancestry) cohorts. Through this work, we present a publicly available long-read resource from 351 human brain samples (median N50: 27 Kbp and at an average depth of ~40x genome coverage). We discover approximately 234,905 SVs and produce locally phased assemblies that cover 95% of all protein-coding genes in GRCh38. To resolve cis-regulatory effects, we develop ASM-LR, a method for allele-specific methylation analysis from long-read data, revealing both strong and subtle regulatory effects, including numerous novel methylation QTLs masked in unphased models. Our results highlight the power of haplotype-resolved methylation to uncover regulatory mechanisms and establish a foundational resource for exploring how genetic variation shapes gene expression and epigenetic architecture across diverse ancestries.

genomics↗

bioRxiv2024

CRL4DCAF12 regulation of MCMBP ensures optimal licensing of DNA replication

The minichromosome maintenance (MCM2-7) protein complexes are central drivers of genome duplication. Distinct protein pools, parental and nascent MCMs, and their precise equilibrium are essential to sustain error-free DNA replication1. However, the mechanism responsible for generating these pools and maintaining their equilibrium remains largely unexplored. Here, we identified CRL4DCAF12 as a new factor controlling the assembly of nascent MCM complexes. During MCM biogenesis, MCMBP facilitates the assembly and transport of newly synthesized MCM3-7 subcomplexes into the nucleus2,3. Once in the nucleus, the MCM2 subunit must be incorporated into the MCM3-7 subcomplex, while MCMBP needs to be removed. CRL4DCAF12 facilitates the degradation of MCMBP and thereby regulates the assembly of MCM2-7 complexes. The absence of CRL4DCAF12 adversely affects the level of chromatin-bound nascent MCMs, resulting in accelerated replication forks and genome instability. Collectively, our findings uncovered the molecular mechanism underlying nascent MCM production essential to counteract genome instability and tumor formation.

cell biology↗

bioRxiv2024

A community-oriented, data-driven resource to improve protocol design for cardiac modelling from human pluripotent stem cells

Protocol design and benchmarking is central to optimising model development using human pluripotent stem cell derived cardiomyocytes (hPSC-CMs). By applying data mining to decades of research and hundreds of peer reviewed studies, we evaluate how protocol variables associate with common properties of cardiac functional and physiological maturation. This resource is publicly accessible through CMPortal, a community-oriented website that provides data-driven tools for researchers to navigate leverage decades of knowledge for benchmarking protocol designs and outcomes for their dedicated applications in developmental biology, disease modelling, and drug screening.

developmental biology↗

bioRxiv2021

CellDepot: A unified repository for scRNA-seq data and visual exploration

CellDepot serves as an integrated web application to assist users in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical tools. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the advanced query function incorporated in MySQL database system and its conditional filtering, allows users to quickly query and compare the expression of gene(s) across the datasets of interest. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types. In summary, the web portal allows large scale single cell data sharing, analysis and visualization for supporting decision-making, and encouraging scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software to motivate crowd contribution, broad adoption, and local deployment for private data.

bioinformatics↗

bioRxiv2021

OmicsView: omics data analysis through interactive visual analytics

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohns disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

bioinformatics↗