Out of the Sichuan Basin: Rapid species diversification of the freshwater crabs in Sinopotamon (Decapoda: Brachyura: Potamidae) endemic to China.

Sinopotamon Bott, 1967 is the most speciose and widely distributed freshwater crab genus in East Asia. Our extensive sampling includes about 76% of the known Sinopotamon taxa, and nearly covers its entire distribution area. Based on mitochondrial cytochrome oxidase I (COI) and 16S rRNA, as well as nuclear 28S rRNA and histone H3, we reconstructed the Sinopotamon phylogeny using maximum likelihood and Bayesian approaches. The divergence time was estimated and multiple methods were used to conduct diversification analyses. The ancestral geographic distribution and character state were reconstructed. Three main clades (Clades I, II and III) that roughly correspond to their main geographic distribution ranges were recovered. Our results challenge the current view of the four major species groups based on the morphological differences in the male first gonopod (G1). The most recent common ancestor of Sinopotamon most likely originated from the Sichuan Basin and surrounding mountains (SBSM) and subsequently dispersed throughout central and eastern China. The exceptionally rapid, recent diversification was detected in Clade II. The high incidence of species-level non-monophyly found in Clade II can be explained by recent rapid radiation. Climatic changes, morphological innovations, range expansion and geographical heterogeneity may all contribute to the diversification in Sinopotamon. This study contributes to our knowledge on diversification of freshwater benthic macro-invertebrates in the East Asian inland ecosystem.

Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network.

BACKGROUND: Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC. METHODS: We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth. RESULTS: NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%). CONCLUSIONS: By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.

Gene reuse facilitates rapid radiation and independent adaptation to diverse habitats in the Asian honeybee.

Animals with recent shared ancestry frequently adapt in parallel to new but similar habitats, a process often underlined by repeated selection of the same genes. Yet, in contrast, few examples have demonstrated the significance of gene reuse in colonization of multiple disparate habitats. By analyzing 343 genomes of the widespread Asian honeybee, Apis cerana, we showed that multiple peripheral subspecies radiated from a central ancestral population and adapted independently to diverse habitats. We found strong evidence of gene reuse in the Leucokinin receptor (Lkr), which was repeatedly selected in almost all peripheral subspecies. Differential expression and RNA interference knockdown revealed the role of Lkr in influencing foraging labor division, suggesting that Lkr facilitates collective tendency for pollen/nectar collection as an adaptation to floral changes. Our results suggest that honeybees may accommodate diverse floral shifts during rapid radiation through fine-tuning individual foraging tendency, a seemingly complex process accomplished by gene reuse.