SEARCH · PubMed Health

Results for “multi-species approach”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

7 recordsLinked to original sources

PubMed2026

Hurdles to horizontal gene transfer: species-specific effects of synonymous variation and plasmid copy number determine antibiotic resistance phenotype.

Could codon composition condition the immediate success and the orientation of horizontal gene transfer? Horizontal gene transfer represents a change in the genome of expression of the transferred gene, and experimental evidence has accumulated indicating that the codon composition of a sequence is an important determinant of its compatibility with the translation machinery of the genome in which it is expressed. This suggests that codon composition influences the phenotype and the fitness conferred by a transferred gene and thus the immediate success of the transfer. To directly test this hypothesis, we characterized the resistance conferred by synonymous variants of a gentamicin resistance gene in three bacterial species: Escherichia coli, Acinetobacter baylyi and Pseudomonas aeruginosa. The strongest determinant of the resistance level conferred was the species in which the resistance gene was transferred, very likely because of important differences in the copy number of the plasmid carrying the gene. Significant differences in resistance were also found between synonymous variants within each of the three species, but more importantly, there was a strong interaction between species and variant: variants conferring high resistance in one species confer low resistance in another. However, the similarity in codon usage between the synonymous variants and the host genome only explained part of the phenotypic differences between variants in one species, P. aeruginosa. Further investigation of alternative explanations did not reveal common universal mechanisms across our three bacterial species. We conclude that codon composition can be a determinant of post-horizontal gene transfer success. However, there are multiple paths leading from synonymous sequence to phenotype, and sensitivity to these different paths is species-specific.

Gene Transfer, Horizontal↗

PubMed2026

Ovarian expression and function of neuropeptide systems in teleosts and anurans.

The hypothalamic-pituitary-gonadal axis regulates reproduction, sexual maturation, and spawning behaviours. Its evolutionary origins trace back to primitive jawless fish and has been well characterized in teleosts. Recent advances in multi-species genome sequencing, annotation, and experimental approaches for identifying and characterizing key regulators have advanced understanding of neuroendocrine regulation in teleost reproduction, reshaping existing models. Early studies in amphibians established that steroids are critical regulators of final oocyte maturation. Subsequent work in anurans revealed complex interactions among theca cells, follicular cells, and oocytes, supporting a three-cell model in which oocytes contribute to their own steroidogenic environment, challenging the traditional two-cell view of ovarian steroidogenesis. In teleosts, however, direct evidence that oocytes support steroid precursor delivery to theca and follicular cells is limited, and whether a comparable three-cell model applies remains an open hypothesis. Across both taxa, the roles of locally produced neuropeptides in coordinating interactions among theca cells, follicular cells, and oocytes remain largely uncharacterized. Here, we provide a short review of the localization and potential autocrine/paracrine functions of neuropeptides in teleost and amphibian ovaries and discuss existing knowledge gaps. We identify opportunities to leverage detailed localization studies that map neuropeptides to specific ovarian cell types and developmental stages, and discuss how integrating traditional and emerging experimental approaches can advance comparative studies in ovarian endocrinology. This work will improve our understanding of reproductive regulation in fishes and frogs, with applications in captive breeding, aquaculture, and endocrine disruption research.

Autocrine↗

PubMed2026

Contactless Co-Culture Assays for Morphometric Studies During Inter-Species Interactions in Fungi.

Cellular behaviour and morphology are usually influenced by various intra- and extracellular factors in a microbial community. Different approaches available to study microbial communication could be tedious and/or require specialised facilities and expertise. Here, two complementary contactless co-culture approaches are described, the membrane insert well plate co-culture assay and the Cell-Free Supernatant (CFS)-based assay, which are based on morphological transition as a measurable response to investigate the role of various factors in a given inter-species interaction. The membrane insert well plate system, while permitting diffusion of extracellular molecules, allows real-time interaction between physically separated species. In comparison, the CFS-based assay provides a simplified, scalable approach for evaluating responses to conditioned media. The protocol presented here includes standardized procedures for culture preparation, generation of conditioned supernatants, assay setup, microscopy, image acquisition, quantitative morphometric analysis using Fiji, and statistical evaluation. This has been demonstrated with an example of fungal morphological response to intrinsic and extrinsic factors. The methods presented here offer accessible and adaptable alternative tools for studying novel microbial interactions in a community, and could be readily extendable to investigate mechanisms underlying multi-species co-existence in a community.

Coculture Techniques↗

PubMed2026

Palaeoproteomic Deconvolution of Physical and Genetic Collagen Mixtures.

Species identification in palaeoproteomics relies on genome-derived protein sequences which are often poor-quality, and lacks tools to cope with multi-species samples. Here, we address both challenges through the analysis of "physical and genetic mixtures". Species that are absent from our database are considered a "genetic mixture", i.e. a patchwork of peptides from closely related species. Inversely, various overlapping peptide stretches allow us to resolve complex "physical mixtures". This is benchmarked by analysing physical mixtures of modern bone fragments, including genetic mixtures. We illustrate the impact of our approach via a rapid and high-throughput analysis of >2500 bone fragments, revealing the Eemian-era faunal environment around Scladina Cave, including the first Palaeoloxodon antiquus identified at this site.

bioarchaeology↗

PubMed2026

Phlag: scalable detection of genomics regions with unexplained phylogenetic heterogeneity.

MOTIVATION: Phylogenetic analyses of entire genomes (phylogenomics) have revealed abundant heterogeneity of evolutionary histories. While much has been done to model this heterogeneity and to infer species trees despite it, the current toolkit has a limitation. Most methods assume that gene trees across the genome differ but are all sampled from the same distribution, defined by models such as the multi-species coalescent (MSC), and parametrized consistently across the genome. Empirical data strongly suggest this assumption is often violated because the species tree, its parameters, or the process generating the gene trees can all change across the genome. Errors in the data can further compound this heterogeneity. RESULTS: To address this challenge, we define the problem of detecting what segments of the genome are inconsistent with a putative species tree, even after allowing discordance according to MSC. We model gene trees not as a set, but rather as a series (a realization of a stochastic process) along genomic positions. We propose a Hidden Markov Model (HMM) approach applied to quartet statistics measured from gene trees and tie the model to MSC using simulations. The combined use of these three ideas leads to a scalable method called Phlag. On simulated and real data, we show that Phlag can detect many cases of change in underlying evolutionary processes, including reduced recombination rates, population size changes, and admixture, all using the same algorithm. AVAILABILITY AND IMPLEMENTATION: Phlag is available at github.com/bo1929/phlag. All results and scripts can be found at github.com/bo1929/shared.phlag.

Phylogeny↗

PubMed2026

GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.

MOTIVATION: Understanding the functional impact of genetic variants is a key problem for precision medicine. Tools like CADD, PhyloP, and PhastCons are useful, but they often look at each position in the genome in isolation. This means they can miss important information from the evolutionary history that connects different species. In this paper, we extend our previous model, Graphylo, to predict the effects of variants. Our new model, GraphyloVar, is built to directly utilize the phylogenetic tree that relates the species. RESULTS: GraphyloVar is a deep learning model that considers both DNA sequence and evolutionary patterns from many species. It uses two main components: Graph Convolutional Networks (GCNs) to process the phylogenetic tree, and Transformer encoders to extract features from the DNA sequences. Pre-trained to predict population-level allele frequencies on the TOPMed whole-genome sequencing cohort, GraphyloVar achieves an AUROC of 0.6246 zero-shot on ∼149M held-out variants, and an ensemble with CADD reaches 0.6442 (+0.020, P<10-15). Fine-tuned GraphyloVar achieves the highest AUROC across all 13 MPRA benchmark datasets. By integrating deep learning with explicit phylogenetic input, GraphyloVar offers a powerful and complementary approach to variant effect prediction that utilizes the full evolutionary history from many species to better identify and prioritize important non-coding variants. AVAILABILITY AND IMPLEMENTATION: Code and datasets are available at https://github.com/DongjoonLim/GraphyloVar under DOI: 10.5281/zenodo.20616818.

Phylogeny↗

PubMed2026

Phlag: Scalable detection of genomics regions with unexplained phylogenetic heterogeneity.

MOTIVATION: Phylogenetic analyses of entire genomes (phylogenomics) have revealed abundant heterogeneity of evolutionary histories. While much has been done to model this heterogeneity and to infer species trees despite it, the current toolkit has a limitation. Most methods assume that gene trees across the genome differ but are all sampled from the same distribution , defined by models such as the multi-species coalescent (MSC), and parametrized consistently across the genome. Empirical data strongly suggest this assumption is often violated because the species tree, its parameters, or the process generating the gene trees can all change across the genome. Errors in the data can further compound this heterogeneity. RESULTS: To address this challenge, we define the problem of detecting what segments of the genome are inconsistent with a putative species tree, even after allowing discordance according to MSC. We model gene trees not as a set, but rather as a series (a realization of a stochastic process) along genomic positions. We propose a Hidden Markov Model (HMM) approach applied to quartet statistics measured from gene trees and tie the model to MSC using simulations. The combined use of these three ideas leads to a scalable method called Phlag. On simulated and real data, we show that Phlag can detect many cases of change in underlying evolutionary processes, including reduced recombination rates, population size changes, and admixture, all using the same algorithm. AVAILABILITY AND IMPLEMENTATION: Phlag is available at github.com/bo1929/phlag . All results and scripts can be found at github.com/bo1929/shared.phlag .

Journal Article↗