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Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data.
G3 (Bethesda)
January 2025
School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA.
The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection.
View Article and Find Full Text PDFGenomes reveal pervasive distant hybridization in nature among cyprinid fishes.
Gigascience
January 2025
State Key Laboratory of Developmental Biology of Freshwater Fish, Engineering Research Center of Polyploid Fish Reproduction and Breeding of the State Education Ministry, College of Life Sciences, Hunan Normal University, Changsha 410081, China.
Background: Genomic data have unveiled a fascinating aspect of the evolutionary past, showing that the mingling of different species through hybridization has left its mark on the histories of numerous life forms. However, the relationship between hybridization events and the origins of cyprinid fishes remains unclear.
Results: In this study, we generated de novo assembled genomes of 8 cyprinid fishes and conducted phylogenetic analyses on 24 species.
Quantifying the configurational complexity of biological systems in multivariate 'complexity space'.
J R Soc Interface
January 2025
Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath BA2 7AZ, UK.
An increasing number of evolutionary studies seek to quantify the morphological complexity of organisms, particularly those comprising serially homologous elements at different hierarchical levels of organization. Numerous operational frameworks have been proposed for doing this, but most focus on one or multiple conflated aspects of what is really a multidimensional concept. Here, we advocate the use of 'complexity spaces': multidimensional spaces defined by different vectors of complexity.
View Article and Find Full Text PDFInnovative Applications of Bacteria and Their Derivatives in Targeted Tumor Therapy.
ACS Nano
January 2025
Institute of Nanobiomaterials and Immunology & Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, School of Life Sciences, Taizhou University, Zhejiang Taizhou 318000, China.
Despite significant progress in cancer treatment, traditional therapies still face considerable challenges, including poor targeting, severe toxic side effects, and the development of resistance. Recent advances in biotechnology have revealed the potential of bacteria and their derivatives as drug delivery systems for tumor therapy by leveraging their biological properties. Engineered bacteria, including , , and , along with their derivatives─outer membrane vesicles (OMVs), bacterial ghosts (BGs), and bacterial spores (BSPs)─can be loaded with a variety of antitumor agents, enabling precise targeting and sustained drug release within the tumor microenvironment (TME).
View Article and Find Full Text PDFCancer phylogenetic inference using copy number alterations detected from DNA sequencing data.
Cancer Pathog Ther
January 2025
School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK.
Cancer is an evolutionary process involving the accumulation of diverse somatic mutations and clonal evolution over time. Phylogenetic inference from samples obtained from an individual patient offers a powerful approach to unraveling the intricate evolutionary history of cancer and provides insights that can inform cancer treatment. Somatic copy number alterations (CNAs) are important in cancer evolution and are often used as markers, alone or with other somatic mutations, for phylogenetic inferences, particularly in low-coverage DNA sequencing data.
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