Identification and evolution of a diterpenoid phytoalexin oryzalactone biosynthetic gene in the genus Oryza.

The natural variation of plant-specialized metabolites represents the evolutionary adaptation of plants to their environments. However, the molecular mechanisms that account for the diversification of the metabolic pathways have not been fully clarified. Rice plants resist attacks from pathogens by accumulating diterpenoid phytoalexins. It has been confirmed that the composition of rice phytoalexins exhibits numerous natural variations. Major rice phytoalexins (momilactones and phytocassanes) are accumulated in most cultivars, although oryzalactone is a cultivar-specific compound. Here, we attempted to reveal the evolutionary trajectory of the diversification of phytoalexins by analyzing the oryzalactone biosynthetic gene in Oryza species. The candidate gene, KSLX-OL, which accounts for oryzalactone biosynthesis, was found around the single-nucleotide polymorphisms specific to the oryzalactone-accumulating cultivars in the long arm of chromosome 11. The metabolite analyses in Nicotiana benthamiana and rice plants overexpressing KSLX-OL indicated that KSLX-OL is responsible for the oryzalactone biosynthesis. KSLX-OL is an allele of KSL8 that is involved in the biosynthesis of another diterpenoid phytoalexin, oryzalexin S and is specifically distributed in the AA genome species. KSLX-NOL and KSLX-bar, which encode similar enzymes but are not involved in oryzalactone biosynthesis, were also found in AA genome species. The phylogenetic analyses of KSLXs, KSL8s, and related pseudogenes (KSL9s) indicated that KSLX-OL was generated from a common ancestor with KSL8 and KSL9 via gene duplication, functional differentiation, and gene fusion. The wide distributions of KSLX-OL and KSL8 in AA genome species demonstrate their long-term coexistence beyond species differentiation, suggesting a balancing selection between the genes.

The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

OBJECTIVE: To examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer. METHODS: This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index). RESULTS: Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808). CONCLUSION: ML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer. ADVANCES IN KNOWLEDGE: ML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.