Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by complex immune dysregulation and closely related to the gut microbiome. The present study investigated the microbiome-mediated effect of Sihocheonggan-Tang (SHCGT) on AD-like symptoms induced by 2,4-dinitrochlorobenzene (DNCB) in BALB/c mice. DNCB was applied regularly to the ear and dorsal skin of BALB/c mice, and SHCGT was administered orally daily for 2 weeks. The composition of the gut microbiota was analyzed using 16S rRNA sequencing, and the effect of gut microbiome-derived metabolites, specifically short-chain fatty acids (SCFAs), was evaluated in tumor necrosis factor-alpha (TNF-α)- and interferon-gamma (IFN-γ)-treated HaCaT cells. SHCGT alleviated DNCB-induced symptoms of AD and the immune response to AD by decreasing the plasma immunoglobulin E level and splenic interleukin-4, interleukin-10, TNF-α, and IFN-γ levels. The gut microbiome composition and the damaged gut epithelial barrier in mice with AD were also significantly altered by SHCGT, and the reduced SCFA levels therein were elevated. We found that SFCAs directly inhibited the mRNA expression of IL-6 and ICAM-1 in TNF-α- and INF-γ-treated HaCaT cells. The finding that SHCGT regulates the gut microbiome and improves DNCB-induced AD in mice suggests that this herbal medicine has therapeutic potential in patients with AD.

Lactobacillus delbrueckii comprises six subspecies, L. delbrueckii subsp. bulgaricus, L. delbrueckii subsp. lactis, L. delbrueckii subsp. jakobsenii, L. delbrueckii subsp. delbrueckii, L. delbrueckii subsp. sunkii, and L. delbrueckii subsp. indicus. We investigated the evolution of the six subspecies of L. delbrueckii using comparative genomics. While the defining feature of the species was the gene number increment driven by mobile elements and gene fragmentation, the repertoire of subspecies-specific gene gains and losses differed among the six subspecies. The horizontal gene transfer analyses indicated that frequent gene transfers between different subspecies had occurred when the six subspecies first diverged from the common ancestor, but recent gene exchange was confined to a subspecies implying independent evolution of the six subspecies. The subspecies bulgaricus is a homogeneous group that diverged from the other subspecies a long time ago and underwent convergent evolution. The subspecies lactis, jakobsenii, delbrueckii, and sunkii were more closely related to each other than to other subspecies. The four subspecies commonly show increasing genetic variability with increasing genome size. However, the four subspecies were distinguished by specific gene contents. The subspecies indicus forms a branch distant from the other subspecies and shows an independent evolutionary trend. These results could explain the differences in the habitat and nutritional requirements of the subspecies of L. delbrueckii.

Lactiplantibacillus plantarum, previously named “Lactobacillus plantarum,” is found in a wide variety of environments exhibiting a high level of intraspecies genetic diversity. To investigate the strain diversity, we performed comparative genomic analyses of the 54 complete genome sequences. The results revealed that L. plantarum subsp. plantarum was split into three lineages, A, B and C. Of the genes beneficial for probiotic activity, only those associated with the biosynthesis of plantaricin (Pln), an L. plantarum-specific bacteriocin, were found to be significantly different among the lineages. The genes related to the biosynthesis of plnE/F were conserved throughout the three lineages, whereas the outgroups did not possess any Pln-producing genes. In lineage C, the deepest and ancestral type branch, plnE/F genes, were well conserved. In lineage B, loss of gene function was observed due to mobile elements in the pln loci. In lineage A, most strains were predicted to produce more than one type of Pln by possessing diverse Pln-encoding genes. These results showed the presence of functional diversity arising from the trifurcating evolution in L. plantarum subsp. plantarum and demonstrated that Pln is an indicator for differentiating the three lineages.

Genomic information can be used to predict major pathogenic traits of pathogens without the need for laboratory experimentation. However, no Vibrio cholerae genome-based trait identification tools currently exist. The aim of this study was to develop a web-based prediction tool to identify Vibrio pathogenic traits using publicly available 796 whole-genome sequences of V. cholerae. Using this application, 68 structural O-antigen gene clusters belonging to 49 serogroups of V. cholerae were classified, and the composition of the genes within the O-antigen cluster of each serogroup was identified. The arrangement and location of the CTX prophage and related elements of the seventh cholera pandemic strains were also revealed. With the versatile tool, named VicPred, we analyzed the assemblage of various SXTs (sulfamethoxazole/trimethoprim resistance element) and major genomic islands (GIs) of V. cholerae, and the increasing trend in drug-resistance revealing high resistance of the V. cholerae strains to certain antibiotics. The pathogenic traits of newly sequenced V. cholerae strains could be analyzed based on these characteristics. The accumulation of further genome data will expedite the establishment of a more precise genome-based pathogenic traits analysis tool.

Background Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics. Methods Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed. Results In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified. Conclusions Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered