RESUMEN
Resolving evolutionary relationships among closely related species with interspecific gene flow is challenging. Genome-scale data provide opportunities to clarify complex evolutionary relationships in closely related species and to observe variations in species relationships across the genomes of such species. The Himalayan-Hengduan subalpine oaks have a nearly completely sympatric distribution in southwest China and probably constitute a syngameon. In this study, we mapped resequencing data from different species in this group to the Quercus aquifolioides reference genome to obtain a high-quality filtered single nucleotide polymorphism (SNP) dataset. We also assembled their plastomes. We reconstructed their phylogenetic relationships, explored the level and pattern of introgression among these species and investigated gene tree variation in the genomes of these species using sliding windows. The same or closely related plastomes were found to be shared extensively among different species within a specific geographical area. Phylogenomic analyses of genome-wide SNP data found that most oaks in the Himalayan-Hengduan subalpine clade showed genetic coherence, but several species were found to be connected by introgression. The gene trees obtained using sliding windows showed that the phylogenetic relationships in the genomes of oaks are highly heterogeneous and therefore highly obscured. Our study found that all the oaks of the Himalayan-Hengduan subalpine clade from southwest China form a syngameon. The obscured phylogenetic relationships observed empirically across the genome are best explained by interspecific gene flow in conjunction with incomplete lineage sorting.
RESUMEN
AIM: The aim of this study was to use a metabonomics approach to identify potential biomarkers of exhaled breath condensate (EBC) for predicting the prognosis of acute-on-chronic liver failure (ACLF). METHODS: Using liquid chromatography mass spectrometry, EBC metabolites of ACLF patients surviving without liver transplantation (n = 57) and those with worse outcomes (n = 45), and controls (n = 15) were profiled from a specialized liver disease center in Beijing. The metabolites were used to identify candidate biomarkers, and the predicted performance of potential biomarkers was tested. RESULTS: Forty-one metabolites, involving glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, and amino acid metabolism, as candidate biomarkers for discriminating the different outcomes of ACLF were selected. A prognostic model was constructed by a panel of four metabolites including phosphatidylinositol [20:4(5Z,8Z,11Z,14Z)/13:0], phosphatidyl ethanolamine (12:0/22:0), L-metanephrine and ethylbenzene, which could predict the worse prognosis in ACLF patients with sensitivity (84.4%) and specificity (89.5%) (area under the receiver operating characteristic curve [AUC] = 0.859, 95% confidence interval [CI] = 0.787-0.931). Compared with Model for End-Stage Liver Disease (MELD) score (AUC = 0.639, 95% CI = 0.526-0.753) and MELD-sodium (MELD-Na) score (AUC = 0.692, 95% CI = 0.582-0.803), EBC-associated metabolite signature model could better predict worse outcomes in patients with ACLF (p < 0.05). Using the MELD-Na score and EBC metabolite signatures, a decision tree model was built for predicting the prognosis of ACLF identified on logistic regression analyses (AUC = 0.906, 95% CI = 0.846-0.965). CONCLUSION: EBC metabolic signatures show promise as potential biomarkers for predicting worse prognosis of ACLF.