Biology subjects

Smith, R. N.

Publications and source records attributed to Smith, R. N..

2 recordsLinked to original sources

bioRxiv2025

Mink by mink: stitching together signatures of subspecies adaptation through a pangenome of threatened mustelids

The American mink (Neogale vison), a semi-aquatic Mustelidae carnivoran with broad ecological range across North America, includes several putative subspecies of conservation concern. To investigate the evolutionary history and adaptive signatures of mink subspecies, chromosome-scale genome assemblies were generated for six individuals representing three southern subspecies: N. vison evergladensis, N. vison vulgivaga, and N. vison lutensis. Genomes were assembled using Illumina short reads, scaffolded with Oxford Nanopore long reads, and aligned to the phased N. vison reference genome. Assemblies ranged from 75.9% to 97.8% completeness, with five meeting thresholds for pangenome construction. A reference-free pangenome revealed an open architecture, highlighting considerable subspecies diversity. Subspecies-specific gene enrichment reflected adaptation: N. vison evergladensis showed enrichment in traits related to reproduction and sensory function; N. vison vulgivaga in cytoskeletal remodeling and oxidative stress; and N. vison lutensis in neuronal development, synaptic plasticity and cellular stress pathways. Assessment of the mitogenomes resolved N. vison lutensis as a distinct lineage, while nuclear data supported broader subspecies divergence but lacked fine scale resolution. N. vison evergladensis showed multiple signatures of small population size, including inbreeding coefficients (FROH) above 0.5, and displayed consistent population decline over time via demographic inference. Our findings support evergladensis as a distinct subspecies, supported by both the mitogenome phylogeny, and significant functional differentiation. As the first pangenome for Mustelidae, this study demonstrates the power of integrating cross-platform sequencing with natural history specimens to improve the resolution on signatures of adaptation and inform conservation policy and management of threatened populations.

genomics↗

bioRxiv2021

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

BackgroundInterstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility. MethodsWe constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. ResultsOur model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage. ConclusionsThis deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of posttransplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

pathology↗