Biology subjects

Welsh, M.

Publications and source records attributed to Welsh, M..

5 recordsLinked to original sources

bioRxiv2026

Termite-Like Phylogenetic Conservation of Gut Microbiota in Passalid Beetles Suggests Evolutionary Convergence in Decaying Wood Degradation

Beetles of the family Passalidae are an important group of subsocial wood-decomposing (saproxylophagous) insects, spending their entire life cycle living inside and feeding on rotten logs. Decaying wood is an abundant resource, but nutritionally poor and challenging to digest. As in other saproxylophagous insects, digestion for passalids is only possible through symbiotic relationships with their gut microbiota, highlighting the critical roles of these symbionts in the evolution of wood-decomposing insects. Despite its importance, this critical symbiotic relationship remains poorly understood, hindering our ability to identify the factors shaping this association and assessing its role in the evolution of Passalidae. Using high-throughput sequencing of the microbial 16S rRNA we comparatively analyzed the composition and abundance of symbiont lineages in the anterior hindguts of passalid beetles from two independent mountain systems in the Colombian Caribbean region. We tested the influence of geographical co-occurrence and phylogenetic relatedness on gut microbial composition and structure. We posit that if shared locality was the primary driver of gut microbiome assembly, then sympatric species should harbor more similar microbiomes regardless of their phylogenetic relationships. Alternatively, if host evolutionary history is the dominant factor, congeneric species should exhibit more similar microbiomes regardless of geographic co-occurrence. Our results support that phylogenetic relatedness explained substantially more variation in both bacterial and archaeal communities than geographic locality, indicating that gut microbiome composition is primarily structured by host evolutionary history. Furthermore, bacterial and archaeal communities exhibited evidence of phylosymbiosis, although at different phylogenetic scales: bacterial communities more closely reflected relationships among genus level clades, whereas archaeal communities retained congruence mainly at deeper host lineages. We also uncovered extensive bacterial taxonomic novelty, with nearly half of bacterial reads remaining unclassified at the genus level, highlighting the large undescribed microbial diversity associated with Passalidae. These findings lead to establishing Passalidae as a promising comparative model for investigating the evolutionary processes governing gut microbiome assembly and the evolution of lignocellulose-feeding symbioses.

evolutionary biology↗

bioRxiv2026

Vascular normalization in breast cancer confers an endothelial immune suppressive gene expression profile

Tumors commonly exhibit a leaky vasculature and innate and/or adaptive immune cell infiltration. A recent study showed that a non-leaky and normalized mouse breast cancer vasculature after conditional deletion of the Shb gene in endothelial cells (EC) displayed a suppressive immune cell profile. To extend those findings further, regulation of immunomodulatory gene expression of normalized breast cancer EC was related to healthy human breast and human breast cancer EC gene expression. A considerable correlation was observed in the gene expression profiles of immunoregulatory genes between normal breast arterial EC and normalized (Shb-deficient) EC, and these changes did not primarily seem to involve gene products forming conduits for tumor leukocyte transendothelial migration but other aspects of immune suppression as for example cytokine expression. In addition to breast cancer, healthy lung and colonic EC exhibited an immune suppressive phenotype compared to their lung cancer and colorectal cancer counterparts. It is concluded that normalized EC may confer a non-leaky and immune suppressed phenotype, an insight that may be exploited to enhance the efficacy of immunotherapy.

Cancer Biology↗

bioRxiv2026

Rethinking yield stability through phenotypic plasticity and its link to modern statistical methods

In the context of advocacy for yield stability, trade-offs between yield and yield stability, the frequent lack of definitions, and the variation in methods when they are explicit, we connect two perspectives: phenotypic plasticity and factor analytics. Phenotypic plasticity brings over a century of research in developmental biology, ecology and evolution, and is gaining traction in crop science. Factor analytics is an advanced linear mixed model of multi-environment data with factor analytic variance structures for the variety-by-environment interaction effect. We (1) review phenotypic plasticity, define agronomically adaptive plasticity where varieties (or practices) consistently return superior yield (or other traits) across environments with no trade-off, and describe percentile-plasticity plots to assess the agronomic value of plasticity; (2) outline factor analytic models, and (3) link plasticity and factor analytic models mathematically and empirically. We show that phenotypic plasticity of cereal yield correlates positively with overall performance obtained from factor analytic models when plasticity is adaptive and negatively when it is maladaptive. We conclude that phenotypic plasticity contributes biological meaning to opaque analytical approaches, and that biologically grounded statistics are needed to challenge weak agronomic narratives, such as advocacy for stability that might reflect decision biases rather than critical consideration of its benefits. HighlightUncritical advocacy for crop yield stability is common. Here we advance a biological-statistical synthesis of yield stability and test theoretical predictions with actual wheat and oat yield data.

plant biology↗

bioRxiv2019

Using Machine Learning to Facilitate Classification of Somatic Variants from Next-Generation Sequencing

BackgroundMolecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. Here we present a machine learning-based method to distinguish artifacts from bona fide Single Nucleotide Variants (SNVs) detected by NGS from tumor specimens.\n\nMethodsA cohort of 11,278 SNVs identified through clinical sequencing of tumor specimens were collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A three-class (real, artifact and uncertain) model was developed on the training set, fine-tuned using the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label \"uncertain\" variants.\n\nResultsThe optimized classifier demonstrated 100% specificity and 97% sensitivity over 5,587 SNVs of the test set. 1,252 out of 1,341 true positive variants were identified as real, 4,143 out of 4,246 false positive calls were deemed artifacts, while only 192(3.4%) SNVs were labeled as \"uncertain\" with zero misclassification between the true positives and artifacts in the test set.\n\nConclusionsWe presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received a definitive label and thus were exempt from manual review. This framework could improve quality and efficiency of variant review process in clinical labs.

bioinformatics↗

bioRxiv2019

RNA polymerase mutations cause cephalosporin resistance in clinical Neisseria gonorrhoeae isolates

Increasing Neisseria gonorrhoeae resistance to ceftriaxone, the last antibiotic recommended for empiric gonorrhea treatment, poses an urgent public health threat. However, the genetic basis of reduced susceptibility to ceftriaxone is not completely understood: while most ceftriaxone resistance in clinical isolates is caused by target site mutations in penA, others lack these mutations. Here, we show that penA-independent ceftriaxone resistance has evolved multiple times through distinct mutations in rpoB and rpoD. We identify five mutations in these genes that each increase resistance to ceftriaxone, including one mutation that arose independently in two lineages, and show that clinical isolates from multiple lineages are a single nucleotide change from ceftriaxone resistance. These RNA polymerase mutations result in large-scale transcriptional changes without altering susceptibility to other antibiotics, reducing growth rate, or deranging cell morphology. These results underscore the unexpected diversity of pathways to resistance and the importance of continued surveillance for novel resistance mutations.

microbiology↗