Biology subjects

Singh, J.

Publications and source records attributed to Singh, J..

At least 19 recordsLinked to original sources

bioRxiv2026

β-Carbonic anhydrase SlβCA4 is required for normal growth, carbon assimilation and stomatal plasticity in tomato

{beta}-Carbonic anhydrases ({beta}CAs) catalyze the reversible interconversion of CO2 and bicarbonate and contribute to plant carbon metabolism and environmental responses. However, the physiological functions of individual {beta}CA isoforms in crops remain poorly understood. Here, we investigated two chloroplast-localized {beta}CAs, Sl{beta}CA1 and Sl{beta}CA4, in tomato (Solanum lycopersicum). We combined subcellular localization, CRISPR/Cas9 mutagenesis, grafting, leaf gas-exchange measurements, and controlled environmental treatments to examine their roles in growth, photosynthesis, and stomatal developmental plasticity. Both proteins localized to chloroplasts but their disruption resulted in markedly different phenotypes. While Sl{beta}CA1 mutants showed largely normal growth and photosynthesis, Sl{beta}CA4 mutants exhibited severe root and shoot growth defects. Grafting mutant shoots onto wild-type rootstocks partially restored vegetative growth, indicating that impaired root function contributes to the growth defects. Sl{beta}CA4 mutants also showed reduced CO2 assimilation and stomatal conductance, while electron transport remained largely unaffected. Elevated CO2 partially improved leaflet growth but did not restore plant height or root elongation. Furthermore, unlike wild-type plants, Sl{beta}CA4 mutants failed to increase stomatal index in response to elevated temperature. These findings reveal distinct physiological roles for chloroplast-localized {beta}CAs in tomato and identify Sl{beta}CA4 as an important contributor to plant growth, photosynthesis, and temperature-dependent stomatal developmental plasticity.

plant biology↗

bioRxiv2026

Behavior-coupled single-cell transcriptomics reveals trafficking protein-dependent regulation of tumor cell migration

Aggressive cellular migration underlies cancer invasion and metastatic dissemination, yet the molecular programs defining highly migratory tumor cell states remain poorly characterized. In head and neck squamous cell carcinoma (HNSCC), metastatic progression is associated with poor clinical outcome, motivating method developments that link migratory behavior to underlying mechanisms. Here, we present behavior-coupled single-cell sequencing (BC-seq), a live-cell imaging-guided transcriptomic platform that enables real-time identification, isolation, and profiling of individual cells based on migratory behavior. Applying BC-seq to 10 HPV-negative oral squamous cell carcinoma (OSCC), the most common HNSCC subtype, we distinguished fast-migrating cells (FMCs) from slow-migrating cells (SMCs) and identified transcriptional programs associated with aggressive migration. Mechanistically, BC-seq uncovered cholesterol-regulated signaling pathways and associated trafficking proteins involving SREBP2, STARD4, Cavin1/3, and VEGFR2 that promotes actin cytoskeletal remodeling and cellular motility. Disruption of STARD4-mediated cholesterol trafficking or Cavin1/3-dependent VEGFR2 nuclear translocation significantly impaired cell migration, linking cholesterol metabolism to migratory machinery. We next examined whether molecular programs associated with aggressive migration are reflected in clinical outcomes. A 15-gene expression signature derived from FMCs predicted distant metastasis with 83.6% accuracy in an independent OSCC cohort, outperforming conventional clinicopathological risk stratification. Together, our study establishes BC-seq as an approach for integrating dynamic cellular behavior with single-cell transcriptomics to uncover mechanisms of aggressive migration and clinically relevant molecular signatures.

cancer biology↗

bioRxiv2026

AAV-Mediated Ribozyme Targeting of HSV-1 Latency-Associated Transcript RNAs Reveals Distinct Functions of the LAT Intron and Exon In Vivo

Herpes Simplex Virus 1 (HSV-1) establishes lifelong latency in sensory neurons of the trigeminal ganglia (TG), from which periodic reactivation causes recurrent disease. The latency-associated transcript (LAT) has been implicated in latency establishment, neuronal survival, and reactivation; however, most functional studies have relied on viral deletion mutants that alter genomic architecture and disrupt overlapping noncoding RNAs, making transcript-specific functions difficult to define. To investigate LAT RNA function independent of viral genome modification, we used recombinant adeno-associated virus (rAAV) vectors to deliver hammerhead ribozymes targeting either the LAT exon or the stable LAT intron in a rabbit model of HSV-1 infection. We found that knocking down the LAT intron prior to HSV-1 infection significantly increased acute disease morbidity and reduced latent viral genome loads in the TG, whereas depletion of the LAT 5' exon had no detectable effect on latency establishment. LAT intron targeting was also associated with reduced abundance of both the intron and exon LAT transcripts during latency. In contrast, post-establishment depletion of either the LAT exon or intron significantly reduced reactivation-associated virus shedding following induced reactivation. These findings provide direct evidence that LAT-derived RNAs contribute to multiple stages of the HSV-1 life cycle. Furthermore, the differential effects of LAT exon and intron depletion suggest that functions required for efficient latency establishment localize more closely to the LAT intron, whereas both LAT exon and intron transcripts contribute to efficient reactivation. More broadly, this study demonstrates the utility of transcript-targeting approaches for dissecting the functions of complex herpesvirus noncoding RNA loci.

microbiology↗

bioRxiv2026

MtbAIM: AI/ML-guided endpoint-specific prediction of small-molecule activity across drug-susceptible and drug-resistant Mycobacterium tuberculosis

The emergence of drug resistance in Mycobacterium tuberculosis (M.tb) presents a major challenge to drug discovery, as compounds active against drug-susceptible (DS) isolates may not retain comparable inhibitory activity against drug-resistant (DR) isolates. Here, we developed an AI/ML workflow to independently prioritize small molecules separately against DS and DR isolates of M.tb and predict MIC, MIC90, and MIC99 as distinct quantitative endpoints. Following curation of data retrieved from the ChEMBL repository, molecular-activity datasets comprised 25,645 and 3,749 compounds tested against whole cell DS and DR isolates respectively. Molecular representations were generated using RDKit descriptors and Morgan fingerprints, along with embeddings from ChemBERTa, MolCLR, and Uni-Mol, and evaluated for binary activity classification and endpoint-specific regression. The best-performing DS classifier combined RDKit/Morgan features with ExtraTrees (balanced accuracy 0.782; AUROC 0.864; AUPRC 0.870), while histogram gradient boosting using the same features performed best for DR classification (balanced accuracy 0.762; AUROC 0.836; AUPRC 0.880). Similarly, RDKit/Morgan features combined with tree-based regressors achieved the best overall performance across all six DS/DR regression endpoints, with pooled out-of-fold RMSE values ranging from 0.604 to 0.784 pMIC units. To facilitate broader application, we developed MtbAIM (https://github.com/jasdeep002/MtbAIM), a Google Colab-based prediction platform that enables users to assess and prioritize small molecules against DS and DR isolates of M.tb.

bioinformatics↗

bioRxiv2026

AmyloCore-ML: AI/Machine Learning Enabled Identification of Amyloid Fibril Core Regions Using Protein Language Models

Identification of amyloid forming regions in proteins remains central to understanding of their aggregation behaviour, which are central to various neurodegenerative and systemic disorders. Many predictors have been designed to identify amyloid forming regions, however they may not necessarily identify total residues found in actual disease associated fibril structures. To address this, we formed a sequence-based based predictor-AmyloCore-ML to ascertain structural amyloid-core propensity. The predictor was developed using experimentally determined fibril structures obtained from Amyloid Atlas. Ordered residues in the experimental structures were designated as core, and unresolved sampled from the same proteins as matched controls as non-core. Core and non-core regions were encoded using various physicochemical descriptors and protein language model (PLM) embeddings (ESM-2, ANKH, ProtT5) and evaluated using protein-grouped cross-validation. PLMs consistently outperformed physicochemical descriptors, with the best models reaching AUROC values of ~0.88 and AUPRC values of ~0.85. Locked full-length protein scans further localized experimental cores, with ESM-2/ExtraTrees achieving AUROC 0.833, AUPRC 0.751, and a mean peak distance of 12.4 residues. In comparative benchmarking, our predictor showed better performance metrics than CrossBeta and AggrescanAI. The framework therefore provides residue-resolved prediction of structurally incorporated amyloid-core regions directly from sequence.

bioinformatics↗

bioRxiv2026

Standardized and rapid protocol for drought tolerance screening at seedling stage in small grain cereals and their wild relatives

Drought stress severely threatens the stability of production of cereals worldwide. Improving genetic tolerance to drought stress requires detailed characterization of global germplasm collections and their wild relatives under drought conditions. However, the lack of rapid screening methods remains a major obstacle in achieving this goal. Here, we present a rapid, reliable, and highly reproducible soil-based screening protocol for evaluating large germplasm collections under drought stress at the seedling stage. This protocol efficiently discriminates drought tolerant and sensitive genotypes with minimal environmental variability and is broadly applicable across various small grain cereals, enabling the evaluation of 1200 genotypes of wheat, oat, barley, and their wild relatives such as Aegilops tauschii and Ae. umbellulata. Additionally, evaluation of two contrasting wheat genotypes identified using this protocol under drought stress imposed at anthesis and grain filling stages provided evidence that their contrasting drought responses were also consistently maintained at the later developmental stages. Given its efficiency, reproducibility, and cross-species applicability, this protocol offers a practical approach for preliminary screening of large germplasm collections as well as for identifying contrasting genotypes to be included in subsequent breeding, physiological, and genetic studies.

physiology↗

bioRxiv2026

Distinct nanoscale dynamics of growth receptor complexes link hormone perception to rapid cell wall remodeling

Cell elongation is a fundamental process allowing plants to change size and shape, a process governed by several growth promoting hormones. While hormones such as brassinosteroids (BRs) and phytosulfokines (PSKs) have been shown to play important roles in elongation growth, the early steps of signal perception remains elusive, especially with regard to PSK. Here we report a rapid mechanism by which PSK alter cell wall mechanical properties in elongating hypocotyls. Notably, this mode of action differs from that of BR. Making use of atomic force microscopy and fluorescence lifetime imaging microscopy we demonstrate how hallmarks of growing plant cells such as mechanical wall properties, porosity and apoplastic pH are differentially affected by PSK compared to BR. Using super-resolution microscopy, we show that the receptor complex components for BR and PSK display individual spatiotemporal movement and organization patterns. The BR receptor BRI1 transitions to a faster diffusive state upon ligand perception, while the PSK receptor PSKR1 associates in tighter clusters. The shared co-receptor BAK1 displays a selective decrease in cluster density after PSK treatment. We found that the putative cell wall state sensor RLP44 is required for the observed changes, however the spatiotemporal dynamics of RLP44 are not altered during signaling. We propose a model of how cell walls are specifically tuned by BRI1- and PSKR1-centered signaling hubs as potential prerequisites for and during cell elongation initiation.

plant biology↗

bioRxiv2026

Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling

Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5=77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.

bioinformatics↗

bioRxiv2026

A human monoclonal antibody from hybrid immunity exhibits exceptional neutralization breadth against antigenically divergent contemporary Omicron JN.1 lineage variants

Hybrid immunity offers stronger and more durable antibody-mediated protection against symptomatic SARS-CoV-2 infection. In the present study, we report the development of a durable antibody response in an individual with hybrid immunity who also received three doses of the prototype COVID-19 vaccine. Polyclonal plasma antibody obtained from this donor also showed extraordinary neutralization breadth against contemporary Omicron variants. One of the functional monoclonal antibodies (ATHS-C30) isolated from this individual, representing the IGHV4-30 lineage-specific B cell with strong binding affinity to JN.1 spike protein, showed extraordinary neutralization breadth, including variants of Omicron lineages that emerged beyond JN.1, such as KP.2, KP.3.1.1, KP.3.2, KP.3.3, LB.1 and XEC. ATHS-C30 was found to bind RBD with high affinity and showed distinct epitope specificity to the other neutralizing mAbs isolated from the same donor through the epitope binning assay. Molecular modelling of the CDHR3 sequence using existing structures indicated that ATHSC-30 belongs to the class 4 antibody, a feature that contributes to breadth, while epitope conservation analysis indicated that the majority of RBD-interacting residues of ATHSC-30 are evolutionarily conserved. Taken together, our study indicate that ATHS-C30 forms the basis of development of a robust and broadly neutralizing antibody response in this individual with hybrid immunity, which overcomes the antigenic variation by targeting highly conserved and cryptic epitopes in destabilizing the spike structure. ImportanceSARS-CoV-2 continues to pose a significant public health threat, particularly to immunocompromised individuals and older adults with underlying comorbidities. Hybrid immunity to SARS-CoV-2 in vaccinated individuals leads to the development of B cells that are qualitatively superior to those that are expected to develop in only vaccinated individuals. In the present study, we found that among individuals with hybrid immunity who developed robust, durable antigen-specific antibody responses, one developed antibody response capable of broadly cross-neutralising contemporary Omicron variants. This was correlated with development of antigen-specific B cell lineage, such as IGHV4-30, that produced antibodies with potent and extraordinary neutralisation breadth against contemporary Omicron lineages, such as KP.2, KP.3.1.1, KP.3.2, KP.3.3, LB.1 and XEC. This is believed to be due to heterologous antigen exposures driving the development of an antigen-specific B cell repertoire that, in turn, facilitates immune imprinting capable of overcoming the ineffectiveness of antibodies to effectively neutralize newly emerging Omicron variants.

immunology↗

bioRxiv2026

Low-abundance αSyn-112 promotes αSyn-140 aggregation in vitro and forms immunoreactive deposits in Parkinson's disease brain tissue

The aggregation of -synuclein (Syn) is a molecular hallmark of Parkinsons disease (PD) and other synucleinopathies. Understanding the molecular mechanisms that determine the aggregation of this protein may thus facilitate the development of disease-modifying therapies. While Syn is most commonly expressed as a 140-residue protein (Syn-140), recent evidence suggests an involvement of alternatively spliced Syn isoforms in disease onset and progression. Here, we report and characterise the interaction between Syn-140 and the aggregation-prone Syn-112 variant, one of the most abundant Syn splice isoforms. We found that amounts as low as 1% of Syn-112 accelerate the nucleation and aggregation of Syn-140. To further investigate this phenomenon, we employed MALDI-MS and NMR spectroscopy, confirming that Syn-140 and Syn-112 monomers interact strongly with one another. Furthermore, to assess the association of Syn-112 with disease pathology, we performed immunohistochemical staining combined with confocal microscopy on PD brain samples. Thereby, we found an increase in the number as well as the area of Syn-112 immunoreactive aggregates compared to healthy controls. These results illustrate how low-abundance Syn splice isoforms can modulate the aggregation landscape of Syn-140 and in turn contribute to the molecular heterogeneity of synucleinopathies.

biophysics↗

bioRxiv2026

Identical Dormancy Gene Mutations Reveal Unanticipated Relatedness Among Low-Chill Apples

Apples (Malus x domestica) are popular fruits grown in temperate regions of the world. The various genotypes must meet a specific threshold amount of cold exposure before they are competent to break dormancy, a quantity approximated as "chill hours". Several varieties have been identified that exhibit an ultra-low-chill requirement, or more precisely shallow dormancy, breaking vegetative and floral buds early in spring in response to minimal cold exposure. These ultra-low-chill genotypes originated from the Bahamas ( Dorsett Golden,1960s), Israel ( Anna, 1950s) and Alabama, USA ( Shell of Alabama, 1880s). The separation in time and space implies that each would feature distinct genetic lesions that govern dormancy control, providing discrete mechanisms to incorporate a low-chill trait in variety improvement. However, analysis of microsatellites and ultimately genome sequence indicates that Dorsett Golden and Anna share strong concordance with the Shell of Alabama genotype, as well as other ultra-low-chill varieties. Kinship analysis confirms that all are closely related, despite differences in year and place of origin. All three low-chill genotypes share common mutations in the DORMANCY ASSOCIATED MADS-BOX1(DAM1) gene, a known repressor of vegetative growth during dormancy. Genomic sequence diversity is observed among Shell of Alabama individuals, including differences in DAM1 that match differences in flowering time. The results of this study call into question the pedigrees of the ultra-low-chill apple germplasm and indicate variation in an otherwise narrow genetic base for use in future breeding efforts.

plant biology↗

bioRxiv2026

Structure of a Stand-Alone Homodimeric NRPS Condensation Domain Reveals Occlusion of the Canonical Carrier-Protein Interface

Fatty acid amides (FAAs) produced by gut-resident bacteria act as potent modulators of host G protein-coupled receptor signaling, yet the enzymatic mechanisms underlying their biosynthesis remain poorly understood. In many bacteria from the gut microbiome, including Coprococcus eutactus, FAA production is mediated by a nonribosomal peptide synthetase-like pathway that includes OaaC, a free-standing condensation domain that catalyzes amide bond formation between acyl carrier protein (ACP) tethered fatty acids and small-molecule amine acceptors. Here, we combine structural, biophysical, biochemical, and evolutionary analyses to interrogate the molecular basis of OaaC function. Solution scattering and X-ray crystallography reveal that OaaC adopts an atypical homodimeric architecture that occludes the canonical ACP-binding surface and donor access pathways. Mass photometry demonstrates that this homodimer is stable in the absence of substrates and is insensitive to free fatty acids, free amines, and apo-ACP. In contrast, holo or acyl-loaded OaaACP selectively destabilizes the homodimer forming the OaaC-OaaACP complex population. LC-MS reconstitution assays confirm that OaaC catalyzes fatty acid amide formation in vitro and can utilize acyl donors spanning multiple chain lengths and saturation states. Phylogenetic and sequence analyses place FAA-associated condensation domains in a distinct clade most closely related to starter condensation domains and reveal a conserved noncanonical active site motif that differentiates them from PCP-dependent NRPS condensation domains. Together, these findings support a model in which OaaC activity is regulated through substrate-dependent modulation of oligomeric state, providing a model framework for understanding FAA biosynthesis in gut microbes and expanding the known functional diversity of NRPS condensation domains.

biochemistry↗

bioRxiv2026

"Editing the conserved IPA1-TB1 regulatory module reshapes plant architecture and enhances tillering in wheat

Plant architecture is a major determinant of yield potential in cereal crops, where tiller number directly influences spike production and grain yield. The transcriptional regulators Ideal Plant Architecture 1 (IPA1) and Teosinte Branched1 (TB1) constitute a conserved genetic module controlling axillary bud activity and branching in grasses; however, their functional contribution to wheat architecture remains largely unexplored. Here, we employed CRISPR/Cas9-mediated genome editing to investigate the roles of TaIPA1 and TaTB1 in regulating tillering in hexaploid wheat (Triticum aestivum L.). Comparative genomic analysis identified conserved IPA1 orthologs across the wheat A, B, and D sub-genomes, with strong conservation of the SQUAMOSA-binding protein domain. Sequencing analysis confirmed targeted mutations, including nucleotide substitutions and insertions that generated frameshift mutations and premature stop codons. Genome-edited lines exhibited enhanced tillering compared with wild-type plants. Several TaIPA1 mutant lines produced up to two-fold higher tiller numbers, while TaTB1 knockout lines showed earlier tiller initiation and [~]50% increased tillering. Notably, enhanced tillering was associated with increased grain weight without affecting spikelet number per spike. Together, these results demonstrate that the conserved TaIPA1-TaTB1 regulatory module plays a pivotal role in shaping wheat plant architecture. Targeted manipulation of this pathway using CRISPR/Cas9 provides a promising strategy for optimizing tillering and developing high-yielding wheat ideotypes.

molecular biology↗

bioRxiv2026

Stress Granules Buffers Inflammation by Restricting dsRNA-led Mitochondrial Fragmentation

Stress granules (SGs) are dynamic RNA-RBP condensates that form during stress and inflammation, yet how they modulate inflammatory signalling remains unclear. We uncover a rapid, protective SG-mediated mechanism that preserves mitochondrial integrity. During stress and translation inhibition, mitochondrial fragmentation releases double-stranded RNA (dsRNA), which activates PKR and its downstream effector DRP1, generating a self-amplifying loop of mitochondrial fragmentation and inflammation. We find that released dsRNA nucleates nanoSGs within minutes at ER-mitochondria contact sites--the very sites of mitochondrial division. These nanoSGs grow into macroSGs, effectively sequestering PKR-activating dsRNA from the cytosol. By depleting dsRNA, SGs suppress PKR-DRP1-driven positive-feedback inflammation and maintain mitochondrial integrity and function. Our findings reveal SGs as key guardians of mitochondrial homeostasis and position condensate biology at the centre of chronic mitochondrial-driven inflammation relevant to autoimmunity, ageing, and neurodegenerative disease.

cell biology↗

bioRxiv2026

From Single-Cell Emergent Behaviors to Clinical Outcome: PTEN-driven Migratory Efficiency as a Potential New Vulnerability in Glioblastoma

BackgroundGlioblastoma (GB) is a highly aggressive brain tumor with a median survival of approximately 14 months, primarily due to its ability to infiltrate healthy brain tissue both as single cells and in collectives. A deeper understanding of GB cell motility, both individual and collective, is crucial for developing patient-specific therapies. We aimed to characterize migration in patient-derived GB cells using advanced modeling to identify stratification markers and therapeutic vulnerabilities. MethodsWe developed Single-Cell Behavior Live Imaging (ScBLI), an approach integrating live imaging with computational analysis, applied to 30 GB primary cell cultures. Trajectories and morphological features were tracked and analyzed. Diffusion Entropy Analysis (DEA) was applied to classify trajectories based on the Delta Scaling parameter ({delta} scaling). We evaluated functional responses correlating all findings with clinical outcomes and transcriptomic profiles. ResultsWe analyzed 4,279 cell trajectories. Based on {delta} scaling (range 0.28-0.837), we defined three distinct motility groups: Low (L, {delta} scaling [≤]0.5), Medium (M, 0.5 < {delta} scaling [≤] 0.7), and High (H, {delta} scaling >0.7). Functional assays demonstrated that Group H cells are more performant in both positive and negative chemotaxis. Clinically, the three groups showed a clear linear progression with patient survival: High {delta} scaling correlated with the shortest survival (poorer prognosis), while Low {delta} correlated with the longest survival, suggesting that structured motility drives invasiveness. Integrative multi-omic analysis, encompassing both exome and transcriptome profiling, demonstrated that these groups are defined by distinct molecular landscapes rather than poor behavioral traits. Moreover, exome data revealed that Group H is significantly enriched in PTEN alterations (75% vs. 8% in Group L), with PTEN gain-of-function (GoF) mutations exclusively restricted to this group (100% vs 0% in Group L). Notably, within our extended cohort (n=51) currently characterized by whole-exome sequencing, we observed that specific PTEN GoF mutations were associated with a significantly shorter survival compared to PTEN wild-type cases (median OS 6.4 vs 16.6 months; p=0.02), which typically harbor the canonical loss of chromosome 10q. A similar clinical trend was observed when comparing directly GoF carriers to patients with truncating (Ter) alterations (median OS 6.4 vs 14.3 months; p=0.09). Conversely, no survival difference was found between truncating (Ter) mutations and wild-type cases. ConclusionOur findings demonstrate for the first time that migratory efficiency, quantified through DEA, represents a powerful predictor of glioblastoma aggressiveness. Tumor cells adopting highly efficient exploration strategies are strongly associated with poor clinical outcomes and are characterized by distinct molecular signatures, notably PTEN gain-of-function alterations. Statement of significanceOur multi-scale computational framework elucidates emergent behavioral phenotypes as pivotal drivers of glioblastoma progression. By demonstrating a correlation between enhanced migratory efficiency, PTEN gain-of-function, and significantly reduced overall survival, we establish a foundational paradigm for deciphering the emergent complexity governing tumor invasiveness.

cancer biology↗

bioRxiv2026

Watkins wheat landraces: a treasure of stripe rust resistance alleles identified using multi-model association analyses

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), remains a major global constraint to wheat production. Rapid pathogen evolution, exemplified by the recent breakdown of Yr15 in Europe, underscores the need to identify diverse and durable resistance loci. The A.E. Watkins landrace collection represents a globally diverse pre-breeding resource with substantial untapped variation for stripe rust resistance. In this study, 297 Watkins landraces were evaluated against six diverse Pst isolates (representing six races and three North American lineages) and subjected to genome-wide association analysis using high-density whole-genome resequencing data. Continuous phenotypic variation was observed across isolates, with several accessions displaying stable resistance across all lineages. A total of 87 QTLs were identified across all 21 wheat chromosomes. Ten loci co-localized with designated or cloned Yr genes, including Yr84, Yr85, Yrq1, Yr71, Yr60, Yr62, Yr50, Yr68, Yr34, and Lr34/Yr18/Sr57. An additional 34 loci overlapped previously reported stripe rust QTL, whereas the majority did not coincide with known loci, suggesting potential novel resistance regions. Eighteen QTLs were supported by multiple isolates, and fourteen showed supports across statistical models, indicating robust genomic signals. Several Watkins accessions carried favorable alleles that co-localized with multiple Yr-aligned loci, identifying promising donor candidates for validation and pre-breeding. Key MessageGenome-wide association mapping of 297 Watkins wheat landraces across diverse stripe rust races & genetic lineages identified 87 QTL, including 10 formally designated Yr genes and 46 novel loci, highlighting Watkins landraces as valuable pre-breeding donors for novel all-stage stripe rust resistance.

plant biology↗

bioRxiv2026

Enhanced strand transfer and mismatch extension by HIV-1C reverse transcriptase promote sequence motif duplication

Genetic diversification of HIV-1 is driven largely by the error-prone activity of reverse transcriptase (RT) and frequent template switching during reverse transcription. A rare outcome of nonhomologous recombination is sequence motif duplication, which can alter viral gene regulation and protein function. Previous studies have shown that such duplications occur at significantly higher frequencies in HIV-1 subtype C (HIV-1C), particularly within the long terminal repeat (LTR) and p6-Gag regions, where they can confer replication advantages. However, the mechanistic basis for this subtype-specific bias remains unclear. We therefore investigated whether intrinsic biochemical properties of HIV-1C RT contribute to its elevated duplication frequency. Bioinformatic analysis of 6,877 full-length HIV-1 genomes identified four duplication hotspots, with the highest frequencies in HIV-1C. Comparative sequence analysis of RT revealed several subtype-specific residues, including a highly conserved threonine at position 359 (T359) in the connection domain of HIV-1C RT. Structural modeling suggested that T359 can form an additional hydrogen bond with the nascent cDNA, potentially stabilizing the RT-template complex. Biochemical characterization of recombinant RT variants demonstrated that residue 359 modulates polymerase activity and maintains subtype-specific optimal catalytic function. Functional assays further revealed that HIV-1C RT exhibits enhanced template strand transfer compared with HIV-1B RT. In addition, next-generation sequencing-based primer extension assays showed that HIV-1C RT extends mismatched 3' termini more efficiently across multiple mismatch types. Together, these findings indicate that subtype-specific biochemical properties of HIV-1C RT, particularly enhanced strand transfer and mismatch extension mediated in part by T359, promote nonhomologous recombination events that generate sequence motif duplications. This work provides a mechanistic explanation for the elevated duplication frequency characteristic of HIV-1C and highlights how subtle RT polymorphisms can shape viral evolutionary trajectories.

microbiology↗

bioRxiv2026

ARUNA: Slice-based self-supervised imputation for upscaling DNA methylation sequencing assays

Whole-genome bisulfite sequencing (WGBS) can provide near-comprehensive, base-resolution maps of DNA methylation, transforming our understanding of epigenetic regulation in development and disease, but its cost is often prohibitive for many studies. Reduced representation bisulfite sequencing (RRBS) offers a cost-effective alternative that profiles a CpG-enriched subset of the genome at base resolution. Similar sequencing protocols for both assays pose an opportunity for cross-assay integration, presenting an opportunity for massively increasing sample sizes at whole-genome resolution. However, existing imputation methods are designed for within-assay scenarios and cannot handle the substantial CpG coverage differences between WGBS and RRBS. We introduce ARUNA, a self-supervised denoising convolutional autoencoder that predicts genome-wide CpG-level methylation using only a small subset of observed methylation values and CpG coordinates. By modeling methylation "slices," spatially stacked windows that preserve local correlation structure, ARUNA captures biologically meaningful covariation while avoiding representation collapse. In simulation studies using the GTEx dataset, ARUNA successfully upscales RRBS-scale sparse methylomes (80-95% missingness) to whole-genome resolution, consistently outperforming baselines and maintaining robust performance across donor and tissue holdouts. When applied to real RRBS data from the ENCODE dataset, ARUNA outperformed state-of-the-art methods, with performance validated by matching upscaled RRBS samples to isogenic WGBS replicates. Source code for ARUNA can be found at https://github.com/ylaboratory/ARUNA.

bioinformatics↗