Biology subjects

Varma, M.

Publications and source records attributed to Varma, M..

9 recordsLinked to original sources

bioRxiv2026

Breaking Pore Symmetry to Prolong DNA Residence in Solid-State Nanopores

Rapid DNA transport limits the temporal resolution of solid-state nanopore sensing. Here we show that breaking pore symmetry through geometric inclination produces exceptionally prolonged DNA residence without surface functionalization or external trapping. For 500-base-pair double-stranded (ds) DNA ,13 nm diameter SiNx pores in tilted membranes exhibit a distinct long-lived population with a mean dwell time of 11.1 seconds at 600 mV bias, exceeding previously reported dsDNA dwell times by at-least 4 orders of magnitude. Controlled pore tilting reproduces the population of prolonged events, whose prevalence and duration depend on ionic strength. TEM-EELS based thickness measurements exclude substantial membrane-thickness differences, while continuum and atomistic simulations support a mechanism coupling electric-field redistribution to off-axis DNA motion, increased pore-wall contact and suppressed axial mobility. These findings establish geometric inclination as a passive design variable for engineering molecular residence in solid-state nanopores and provide a route towards longer observation windows for single-molecule sensing.

bioengineering↗

bioRxiv2026

Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature

BackgroundSevere dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. MethodsPlasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. ResultsIncreasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. ConclusionIntegrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.

systems biology↗

bioRxiv2026

Periodic DNA encoding enables error-tolerant multi-class molecular detection by nanopore sequencing

Biomarker analysis requires detecting analyte classes that span nucleic acids, proteins, small molecules, and metabolites, yet testing remains fragmented across target-specific assays and instruments. Here we report a molecular information-transduction strategy that converts target recognition across molecular classes into a common, error-tolerant DNA code readable by nanopore sequencing. Target recognition triggers a hybridization chain reaction that generates concatemers containing periodically repeated 10-nucleotide target-specific barcodes. A matched-filter decoder exploits this periodicity and the linear scaling of read length with match count to reject spurious matches by two to three orders of magnitude. Multi-class detection is demonstrated for a small molecule (ATP), two cardiovascular-associated microRNAs and thrombin in singleplex and multiplexed assays. By separating molecular recognition from sequence readout, this architecture provides a modular framework for converting heterogeneous analytes into a shared, redundancy-encoded signal for high-fidelity molecular sensing.

bioengineering↗

bioRxiv2026

Single-cell trajectories in metastatic urothelial carcinoma reveal tumor-immune reprogramming and macrophage-driven resistance to PD-(L)1 blockade

Immune checkpoint inhibitors (ICI) improved outcomes in metastatic urothelial carcinoma (mUC), but primary and acquired resistance remain poorly understood. We performed single-nuclei RNA sequencing on sequential metastatic biopsies from ICI-treated mUC patients. Tumor cells showed transcriptomic heterogeneity within individual lesions, basal cells being associated with increased immune infiltration and response. Myeloid and lymphoid compartments exhibited features of immune dysfunction in non-responders. Longitudinal analyses revealed convergent adaptive resistance mechanisms, dominated by polarization toward pro-tumoral macrophage states, but also including downregulation of the antigen presentation machinery in tumor cells, increased checkpoint expression with loss of cytotoxicity in T cells. Individual trajectories point to distinct evolutionary routes under ICI pressure. Across pivotal ICI trials, bulk expression of the M2-like macrophage marker HES1 predicted ICI resistance. Our study provides the first single-cell longitudinal atlas of ICI-treated mUC, revealing macrophage reprogramming as a dominant driver of resistance, establishing a framework for individualized immunotherapy strategies.

cancer biology↗

bioRxiv2026

PTM-Driven Reshaping of the Peptide Translocation Landscape in Bilayer Graphene Nanopores

Post-translational modifications (PTMs) underpin much of protein regulation, yet their single-molecule readout remains a challenge in nanopore proteomics. While biological nanopores have shown exquisite PTM sensitivity, the microscopic mechanisms by which PTMs perturb signals in solid-state nanopores are largely unexplored. Here, we use all-atom molecular dynamics to investigate how three common PTMs, acetylation, phosphorylation, and methylation, modulate the translocation of a cancer-relevant p53 peptide fragment through a bilayer graphene nanopore. We find that PTMs remodel the translocation landscape far more strongly at the level of dwell-time statistics than at the level of mean current blockade. Acetylation enhances peptide-graphene adhesion and substantially slows transport, with adjacent acetylations producing the longest residence times due to cooperative interfacial interactions, while remotely spaced acetylations yield broader, heterogeneous dynamics. Phosphorylation introduces a negative charge that increases dwell time through an electrostatic tug-of-war, while also generating the largest current blockade among the PTMs studied. In contrast, methylation minimally perturbs translocation due to weak pore interactions and preserved charge. Combining dwell time with relative blockade features enables a simple linear SVM classifier to reliably distinguish unmodified, acetylated, and phosphorylated states. These results establish mechanistic design principles for PTM detection using solid-state nanopores and delineate which classes of PTMs are the most amenable to single-molecule detection with these devices.

biophysics↗

bioRxiv2025

Caris-ComBat-seq: Directionally Harmonizing Large-Scale RNA-seq Datasets

BackgroundBulk RNA sequencing (RNA-seq) is an essential research and clinical diagnostics tool capable of uncovering biological insights across experimental conditions, sample types and diseases at scale. However, RNA-seq data is sensitive to batch effects introduced by technical variation. Harmonizing expression (or batch-correcting) is critical when analyzing measurements from different RNA-seq platforms. In the context of oncology, efficiently and accurately harmonizing expression at scale is important for harnessing massively large datasets (hundreds of thousands of samples) of tumor molecular profiles from different assay platforms. We aimed to develop a method for harmonizing expression in this challenging context. ResultsHere, we extend the widely used ComBat-seq method, as implemented in the pyComBat tool, to enable three key advances: (i) directionally adjusting counts from one batch towards a reference batch, rather than an average expression profile, (ii) separating the training and adjusting steps so that newly profiled samples not available at the time of initial model training can be harmonized, and (iii) flexibly handling outliers to improve the quality of harmonized counts. The resulting model correctly learns gene-specific differences between assay platforms and can near-instantaneously harmonize individual samples. We validated the use of the Caris-ComBat-seq tool to harmonize RNA-seq measurements on a benchmarking dataset of [~]10,000 TCGA tumor samples. Finally, we demonstrated its strength for very large datasets, by harmonizing RNA-seq data from nearly half a million tumor samples profiled by two different next-generation sequencing assays in Caris Life Sciences clinical laboratory. Source code, tutorials and manuscript data are available at: https://github.com/Caris-Life-Sciences/Caris-ComBat-seq and https://doi.org/10.5281/zenodo.17154014. ConclusionsWe present Caris-ComBat-seq, a new variant of the ComBat-seq algorithm designed to harmonize count-based expression data in the context of high-throughput profiling laboratories. It offers the same ability to ameliorate batch effects and retain biological signal as the original ComBat-seq, with additional operational flexibility and scaling that benefits its high-throughput application.

bioinformatics↗

bioRxiv2025

Orientation Dependence of Current Blockade in Single Amino Acid Translocation through a Graphene Nanopore

After successful commercialization of DNA sequencing with biological nanopores, the next frontier of the nanopore technology is protein sequencing which is far more daunting a task. Molecules passing through solid-state nanopores produce current blockades that correlate with their volume linearly in the simplest conceivable model. As thinner membranes provide better volume sensitivity, 2D materials such as Graphene, MoS2 membranes have been explored. Molecular dynamics studies, mostly of homogeneous polypeptide chains translocating through 2D membranes, have been reported. In this paper, we study the translocation of all the twenty single amino acids through monolayer and bilayer graphene nanopores using all-atom molecular dynamics. These studies were motivated by the fact that single amino-acids being the building blocks of peptide chains, can help us understand pore-molecule interactions during translocations at a more basic level, for instance, avoiding neighbor effects present in a chain. We show here that the correlation between the ionic current blockade and the volume of single amino acids is strongly affected by their orientation at the pore, especially when the molecule is static at pore. We explain this phenomenon by the fact that with increasing vdW volume, the amino acid in a particular orientation, has longer projection along the perpendicular direction of the pore plane. We demonstrate distinctive current and force signals for different amino-acid translocation. We observe that some of the smaller amino acids with low molecular volume produce disproportionately high current blockades in a particular orientation due to their low structural fluctuations during translocation. We investigate how dipole-moment (of the translocating amino-acids) and its alignment with electric field in the pore can be linked with our observations.

biophysics↗

bioRxiv2025

Phenotypic plasticity, heritability, and genotype-by-environment interactions in an insect dispersal polymorphism

Evolutionary fitness is determined by the match between an organisms phenotype and its local environment. When mismatched, individuals may disperse to more suitable habitats. For flightless insects, however, the range of dispersal is typically limited. Numerous flightless species have, therefore, evolved a dispersal dimorphism, that is, some individuals in otherwise short-winged populations develop long wings. Wing development may be genetically or environmentally determined, but these two drivers have rarely been analysed together. We studied the inheritance and density-dependent plasticity in the dispersal dimorphism of the meadow grasshopper Pseudochorthippus parallelus. Using a full-sib half-sib breeding design, we found that the development of long wings strongly depends on rearing density, with tactile stimulation being the most likely proximate cause. Additionally, we found heritable variation in the development of long wings, both in the propensity to produce long wings and in response to density (genotype-by-environment interactions). While at high and low densities, the environmental effect dominates, genetic variation takes effect mostly at intermediate densities. Our results have implications for the phenotype-environment match and ultimately the evolution of individualized niches. Induced dimorphisms represent a form of niche conformance (or adaptive phenotypic plasticity) and both genetic and induced dispersal dimorphisms facilitate niche choice in allowing individuals to sample a greater range of environments. Our study shows that niche-related polymorphisms can evolve via selection on the sensitivity threshold.

evolutionary biology↗

bioRxiv2020

Structural Variants in SARS-CoV-2 Occur at Template-Switching Hotspots

The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019, however, analysis has focused primarily on single nucleotide polymorphisms and largely ignored the role of structural variants (SVs) as well as recombination in SARS-CoV-2 evolution. Using sequences from the GISAID database, we catalogue over 100 insertions and deletions in the SARS-CoV-2 consensus sequences. We hypothesize that these indels are artifacts of imperfect homologous recombination between SARS-CoV-2 replicates, and provide four independent pieces of evidence. (1) The SVs from the GISAID consensus sequences are clustered at specific regions of the genome. (2) These regions are also enriched for 5 and 3 breakpoints in the transcription regulatory site (TRS) independent transcriptome, presumably sites of RNA-dependent RNA polymerase (RdRp) template-switching. (3) Within raw reads, these structural variant hotspots have cases of both high intra-host heterogeneity and intra-host homogeneity, suggesting that these structural variants are both consequences of de novo recombination events within a host and artifacts of previous recombination. (4) Within the RNA secondary structure, the indels occur in "arms" of the predicted folded RNA, suggesting that secondary structure may be a mechanism for TRS-independent template-switching in SARS-CoV-2 or other coronaviruses. These insights into the relationship between structural variation and recombination in SARS-CoV-2 can improve our reconstructions of the SARS-CoV-2 evolutionary history as well as our understanding of the process of RdRp template-switching in RNA viruses.

genetics↗