Assessment of beneficial effects and identification of host adaptation-associated genes of Ligilactobacillus salivarius isolated from badgers.

BACKGROUND: Ligilactobacillus salivarius has been frequently isolated from the gut microbiota of humans and domesticated animals and has been studied as a candidate probiotic. Badger (Meles meles) is known as a "generalist" species that consumes complex foods and exhibits tolerance and resistance to certain pathogens, which can be partly attributed to the beneficial microbes such as L. salivarius in the gut microbiota. However, our understanding of the beneficial traits and genomic features of badger-originated L. salivarius remains elusive. RESULTS: In this study, nine L. salivarius strains were isolated from wild badgers' feces, one of which exhibited good probiotic properties. Complete genomes of the nine L. salivarius strains were generated, and comparative genomic analysis was performed with the publicly available complete genomes of L. salivarius obtained from humans and domesticated animals. The strains originating from badgers harbored a larger genome, a higher number of protein-coding sequences, and functionally annotated genes than those originating from humans and chickens. The pan-genome phylogenetic tree demonstrated that the strains originating from badgers formed a separate clade, and totally 412 gene families (12.6% of the total gene families in the pan-genome) were identified as genes gained by the last common ancestor of the badger group. The badger group harbored significantly more gene families responsible for the degradation of complex carbohydrate substrates and production of polysaccharides than strains from other hosts; many of these were acquired by gene gain events. CONCLUSIONS: A candidate probiotic and nine L. salivarius complete genomes were obtained from the badgers' gut microbiome, and several beneficial genes were identified to be specifically present in the badger-originated strains that were gained in the evolution. Our study provides novel insights into the adaptation of L. salivarius to the intestinal habitat of wild badgers and provides valuable strain and genome resources for the development of L. salivarius as a probiotic.

Comparison of the colonization ability of Burkholderia strain B23 in the citrus rhizoplane and rhizosphere and assessment of the underlying mechanisms using full-length 16S rDNA amplicon and metatranscriptomic analyses.

The characterization of bacterial strains with efficient root colonization ability and the mechanisms responsible for their efficient colonization is critical for the identification and application of beneficial bacteria. In this study, we found that Burkholderia strain B23 exhibited a strong niche differentiation between the rhizosphere and rhizoplane (a niche with more abundant easy-to-use nutrients but stronger selective pressures compared with the tightly adjacent rhizosphere) when inoculated into the field-grown citrus trees. Full-length 16S rDNA amplicon analysis demonstrated that the relative abundance of B23 in the rhizoplane microbiome at 3, 5, and 9 days post-inoculation (dpi) was always higher than that at 1 dpi, whereas its relative abundance in the rhizosphere microbiome was decreased continuously, as demonstrated by a 3.18-fold decrease at 9 dpi compared to 1 dpi. Time-series comparative expression profiling of B23 between the rhizoplane and rhizosphere was performed at representative time points (1, 3, and 9 dpi) through metatranscriptomic analysis, and the results demonstrated that multiple genes involved in the uptake and utilization of easy-to-use carbohydrates and amino acids and those involved in metabolism, energy production, replication, and translation were upregulated in the rhizoplane compared with the rhizosphere at 1 dpi and 3 dpi. Several genes involved in resistance to plant- and microbial competitor-derived stresses exhibited higher expression activities in the rhizoplane compared with the rhizosphere. Furthermore, gene loci responsible for the biosynthesis of the key antifungal and antibacterial metabolites occidiofungin and ornibactin were induced, and their expression levels remained relatively stable from 3 dpi to 9 dpi in the rhizoplane but not in the rhizosphere. Collectively, our findings provide novel lights into the mechanisms underlying the root colonization of the inoculated bacterial strains and serve as a basis for the identification of strains with efficient colonization ability, thus contributing to the development of beneficial bacteria applications.

Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.

BACKGROUND: Clinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness. METHODS: A retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investigate the clinical utility of the proposed SSNet. RESULTS: In the differentiation of invasive adenocarcinoma (IA), SSNet achieved a similar area under the curve [AUC; 0.914, 95% confidence interval (CI): 0.813-0.987] with that of the 6 doctors (0.900, 95% CI: 0.867-0.922). When interpreting with the assistance of SSNet, the sensitivity of junior doctors, specificity of senior doctor, and their accuracy were significantly improved. In the external test, SSNet (AUC: 0.949, 95% CI: 0.884-1.000) achieved a better AUC than doctors (AUC: 0.883, 95% CI: 0.826-0.939) whose AUC increased (AUC: 0.908, 95% CI: 0.847-0.982) with SSNet assistance. In the histological subtype classifications, SSNet achieved better performance than practicing doctors. The AUCs of doctors were significantly improved with the assistance of SSNet in both 4-category and 3-category classifications to 0.836 (95% CI: 0.811-0.862) and 0.852 (95% CI: 0.825-0.882), respectively. CONCLUSIONS: The AI diagnostic system achieved non-inferior performance to doctors, and will potentially improve diagnostic performance and efficiency in SSN evaluation.