Biology subjects

S, P.

Publications and source records attributed to S, P..

3 recordsLinked to original sources

bioRxiv2026

Three Plasmid Strategies, One Intermediate Convergence State: Lineage-Specific Resistance, Virulence Architecture in Dominant Indian Carbapenem-Resistant Klebsiella pneumoniae Clones

Carbapenem-resistant Klebsiella pneumoniae (CRKp) is a critical global healthcare threat driven by high-risk multidrug-resistant (MDR) clones that acquire hypervirulence genes. Although resistance-virulence co-occurrence is extensively documented, the plasmid-level mechanisms facilitating this convergence remain unclear. In this study, we utilized hybrid short- and long-read whole-genome sequencing of 376 clinical CRKp strains to define the evolutionary trajectories and structural plasmid dynamics of three predominant high-risk clones: ST147 (n=157), ST231 (n=108), and ST2096 (n=111). Carbapenemase genes were present in 90% of isolates, predominantly blaOXA-48-like and blaNDM-5 co-harbored with blaCTX-M-15. Virulence profiling indicated high aerobactin (iuc) prevalence (62.7%), while salmochelin and colibactin were undetected. Hypermucoviscosity occurred infrequently (6.6%) and was independent of rmpA/rmpA2, confirming a clear genotype-phenotype discordance. Comparative plasmid mapping revealed three distinct, lineage-specific plasmid configurations underlying this intermediate convergent pathotype: ST147 exhibited dynamic, mosaic hybrid IncFIB-IncHI1B plasmids; ST2096 showed structurally stabilized hybrids; and ST231 retained virulence and resistance determinants on separate, segregated plasmids. These findings show that convergence is regulated by multiple, clone-specific evolutionary routes rather than a single path, highlighting the critical need for more in-depth genomic surveillance capable of identifying convergent plasmids along with high-risk lineages

microbiology↗

bioRxiv2026

Cross-Modal Training Using Xenium Spatial Transcriptomics Enables DINO-DETR Based Detection of Vascular Niches in H&E Whole-Slide Images

BackgroundTumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. MethodsWe leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. ResultsBinary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). ConclusionCross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

pathology↗

bioRxiv2022

Multispecies autocatalytic RNA reaction networks in coacervates

Robust and dynamic localization of self-reproducing autocatalytic chemistries is a key step in the realization of heritable and evolvable chemical systems. While autocatalytic chemical reaction networks already possess attributes such as heritable self-reproduction and evolvability, localizing functional multispecies networks within complex primitive phases, such as coacervates, has remained unexplored. Here, we show the self-reproduction of an RNA system within charge-rich coacervates where catalytic RNAs are produced by the autocatalytic assembly of constituent smaller RNA fragments. We systematically demonstrate the catalytic assembly of active ribozymes within phase-separated coacervates -- both in micron sized droplets as well as a coalesced macrophase, underscoring the facility of the complex, charge-rich phase to support these reactions in multiple configurations. By constructing multispecies reaction networks, we show that these newly assembled molecules are active, participating both in self- and cross-catalysis within the coacervates. Finally, these collectively autocatalytic reaction networks endow unique compositional identities to the coacervates which in turn transiently protect the identity against external perturbations, due to differential molecular transport and reaction rates. Our results establish a compartmentalised chemical system possessing a compositional identity possessing a balance between robustness and variability required for chemical evolution.

evolutionary biology↗