Xiangxiang Wang | Search PubMed

PubMed2026

Evolutionary patterns and repeated adaptive strategies of deep-sea anemones.

Sea anemones occupy the full depth range of the oceans, yet their evolutionary patterns and adaptive strategies to the enigmatic deep sea have remained contentious and poorly resolved. Here, we assemble genomes (n = 13) and transcriptomes for 15 species collected between 432 and 6,000 m and integrate them with publicly available actiniarian data. We find support for a shallow-water origin of Actiniaria through a framework that emphasizes genome-scale changes associated with habitat transitions. Most strikingly, these changes include repeated dismantling of the circadian toolkit across deep-sea lineages. In addition to convergent gene losses in photo- and temperature-regulatory genes, we find that some deep-sea lineages have experienced recurrent loss or pseudogenization of key meiotic genes (e.g., Meiosin, Ythdc2, Spo11, and Mlh3), suggesting reduced meiotic capacity in some lineages. Despite this extensive genomic erosion, deep-sea anemones exhibit molecular tuning: specific amino acid substitutions improve enzyme performance under low-temperature conditions relevant to the deep sea, while selective expansions of gene families related to neural excitability, membrane systems, and other functions may help maintain physiological performance in this environment. Functional assays in yeast indicate enhanced performance of the deep-sea variants at 4°C. These results define a "loss-optimization-innovation" triad that underlies bathymetric adaptations and may apply to other deep-sea fauna worldwide.

Actiniaria↗

PubMed2026

ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.

MOTIVATION: Spatial multi-omics technologies jointly profile transcriptomes, proteins and chromatin accessibility in situ, enabling integrative analysis of tissue organization across molecular layers. However, most existing graph-based integration methods rely on independently constructed modality-specific k-nearest-neighbor graphs. When auxiliary modalities are sparse or noisy, these graphs can become topologically discordant, propagate spurious edges, weaken cross-modal alignment, and reduce spatial domain resolution. RESULTS: We present Anchored RNA for Integrated Spatial Embedding (ARISE), an RNA expression anchored framework for spatial multi-omics integration. ARISE defines a shared-edge topology by intersecting RNA feature-similarity and spatial-proximity graphs, encodes auxiliary modalities on this common scaffold, and integrates them through inside-out hierarchical fusion. We further show theoretically that graph intersection minimizes false-positive edges within a broad class of k-of-r graph fusion rules, providing a principled basis for topology anchoring. Across various spatial multi-omics benchmarks spanning simulated and real datasets in bi-modal and tri-modal settings, ARISE improves spatial domain identification, cross-modal consistency, and preservation of tissue structure relative to existing methods. Furthermore, the learned representation supports biologically meaningful downstream analyses, including marker-based domain annotation, pathway enrichment, and cis-regulatory inference, indicating that ARISE yields a robust and interpretable framework for spatial multi-omics integration. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XiangxiangWang-code/ARISE. The archived version used in this study is available at https://doi.org/10.6084/m9.figshare.32686137.v2.

Multiomics↗