Biology subjects

Gao, J.-X.

Publications and source records attributed to Gao, J.-X..

3 recordsLinked to original sources

bioRxiv2026

Evolutionary and multi-omic divergences associated with the extreme sexual size dimorphism in spiders

Extreme sexual size dimorphism (SSD) represents one of the most striking forms of phenotypic divergence between the sexes, yet the genomic basis of extreme body-size dimorphism remains poorly understood. Orb-weaving spiders exhibit some of the most pronounced female-biased SSD among terrestrial arthropods, providing an exceptional system for investigating the evolutionary and genomic changes associated with extreme SSD. Here, we combined comparative genomics across 51 spider species with sex-resolved transcriptomic and chromatin profiling in the highly dimorphic Joro spider, Trichonephila clavata. Across spiders, we identified SSD-associated evolutionary-rate shifts in genes involved in chromatin regulation, growth, and cuticle structural organization. We further found a significant positive association between JHAMT copy number and SSD, with larger gene repertoires occurring in more extremely dimorphic lineages. In T. clavata, expanded JHAMT paralogs showed sex- and tissue-biased expression together with sex-specific differences in chromatin accessibility and local chromatin interactions. Integrated multi-omic analyses further revealed coordinated sex-specific differences in transcription, chromatin accessibility, and chromatin interactions across genes involved in ecdysteroid signaling, insulin and growth regulation, and epidermal and cuticular processes. Altogether, these findings provide insight into the evolutionary and regulatory changes associated with extreme female-biased SSD in spiders.

zoology↗

bioRxiv2024

Relaxed selection and horizontal gene transfer fuel underwater adaptation in a water spider

Spiders have primarily adapted to terrestrial life, yet a number of species have made evolutionary transitions to marine and freshwater environments. While its physiological and behavioral adaptations have been characterized, the genetic basis of semi-aquatic and aquatic adaptation in spiders remains poorly understood. Here, we provide a high-quality, chromosome-level genome assembly for the aquatic spider Argyroneta aquatica, alongside a reference transcriptome for the semi-aquatic spider Desis martensi. We performed comparative genomes analyses of 22 spider species, including a unique aquatic spider, two semi-aquatic spiders and 19 terrestrial spider species, with a focus on those in the marronoid clade. By integrating morphological, phylogenomic, comparative genomic, transcriptomic, and metabolomic analyses, we explored the genomic adaptations of aquatic and semi-aquatic spiders. Phylogenomic analysis suggests that aquatic and semi-aquatic spiders have independently evolved from their terrestrial ancestors and represent divergent evolutionary routes We found hundreds of genes tend to experience relaxed selection, positive selection, and evidence of horizontal gene transfer (HGT) associated with the transition to aquatic and semi-aquatic life in spiders. These genes are associated with respiratory, osmoregulatory, fat metabolism and digestion, hypoxia, and thermal functions, putatively facilitate the adaptations to diverse underwater life. Altogether, our findings highlight the divergent evolutionary mechanisms enabling spiders to thrive in diverse aquatic environments, providing insights into the genomic basis of adaptations to semi-aquatic and aquatic habitats.

genomics↗

bioRxiv2021

A data-driven method to learn a jump diffusion process from aggregate biological gene expression data

Dynamic models of gene expression are urgently required. Different from trajectory inference and RNA velocity, our method reveals gene dynamics by learning a jump diffusion process for modeling the biological process directly. The algorithm needs aggregate gene expression data as input and outputs the parameters of the jump diffusion process. The learned jump diffusion process can predict population distributions of gene expression at any developmental stage, achieve long-time trajectories for individual cells, and offer a novel approach to computing RNA velocity. Moreover, it studies biological systems from a stochastic dynamics perspective. Gene expression data at a time point, which is a snapshot of a cellular process, is treated as an empirical marginal distribution of a stochastic process. The Wasserstein distance between the empirical distribution and predicted distribution by the jump diffusion process is minimized to learn the dynamics. For the learned jump diffusion equation, its trajectories correspond to the development process of cells and stochasticity determines the heterogeneity of cells. Its instantaneous rate of state change can be taken as "RNA velocity", and the changes in scales and orientations of clusters can be noticed too. We demonstrate that our method can recover the underlying nonlinear dynamics better compared to parametric models and diffusion processes driven by Brownian motion for both synthetic and real world datasets. Our method is also robust to perturbations of data because it only involves population expectations.

bioinformatics↗