Microbial dysbiosis index for assessing colitis status in mouse models: A systematic review and meta-analysis.

Although countless gut microbiome studies on colitis using mouse models have been carried out, experiments with small sample sizes have encountered reproducibility limitations because of batch effects and statistical errors. In this study, dextran-sodium-sulfate-induced microbial dysbiosis index (DiMDI) was introduced as a reliable dysbiosis index that can be used to assess the state of microbial dysbiosis in DSS-induced mouse models. Meta-analysis of 189 datasets from 11 independent studies was performed to construct the DiMDI. Microbial dysbiosis biomarkers, Muribaculaceae, Alistipes, Turicibacter, and Bacteroides, were selected through four different feature selection methods and used to construct the DiMDI. This index demonstrated a high accuracy of 82.3% and showed strong robustness (88.9%) in the independent cohort. Therefore, DiMDI may be used as a standard for assessing microbial imbalance in DSS-induced mouse models and may contribute to the development of reliable colitis microbiome studies in mouse experiments.

Spinach (Spinacia oleracea) as green manure modifies the soil nutrients and microbiota structure for enhanced pepper productivity.

Spinach has been suggested as a potential rotation crop for increasing crop yield by enhancing beneficial fungal microbes in continuous monocropping. However, no research on the use of spinach as a green manure has been reported. Thus, we tested the effects of spinach and Korean mustard cultivars (green and red mustards) (10 g pot -1) as green manure on soil chemical properties, pepper productivity, and soil microbiome of long-year pepper-monocropped soil. Spinach improved the soil nutrition (e.g., pH, SOM, TN, NH4+, and K), weed suppression, and pepper growth. Spinach had by far the highest fruit yield, over 100% pepper fruit yield increment over the mustard green manures and control. Our study showed that the major influencing factors to cause a shift in both bacterial and fungal community assemblies were soil pH, TC TN, and K. Following green manure amendment Bacillota, especially Clostridium, Bacillus and Sedimentibacter, were enriched, whereas Chloroflexi and Acidobacteriota were reduced. In addition, spinach highly reduced the abundance of Leotiomycetes and Fusarium but enriched Papiliotrema. FAPROTAX and FUNGuild analysis revealed that predicted functional profiles of bacterial and fungal communities in spinach-amended soil were changed. Spinach-treated soil was differentially abundant in function related to hydrocarbon degradation and functional guilds of symbiotrophs and ectomycorrhizal. This study contributes significantly to our understanding of how the soil fertility and soil microbiome alteration via spinach green manure application as a pre-plant soil treatment might help alleviate continuous cropping obstacles.

Soil amendment with cow dung modifies the soil nutrition and microbiota to reduce the ginseng replanting problem.

Ginseng is a profitable crop worldwide; however, the ginseng replanting problem (GRP) is a major threat to its production. Soil amendment is a non-chemical method that is gaining popularity for alleviating continuous cropping obstacles, such as GRP. However, the impact of soil amendment with either cow dung or canola on GRP reduction and the associated soil microbiota remains unclear. In the present study, we evaluated the effect of soil amendment with cow dung, canola seed powder, and without amendment (control), on the survival of ginseng seedling transplants, the soil bacterial and fungal communities, and their associated metabolic functions. The results showed that cow dung increased ginseng seedling survival rate by 100 percent and had a remarkable positive effect on ginseng plant growth compared to control, whereas canola did not. Cow dung improved soil nutritional status in terms of pH, electrical conductivity, NO 3 - , total carbon, total phosphorus, and available phosphorus. The amplicon sequencing results using Illumina MiSeq showed that canola had the strongest negative effect in reducing soil bacterial and fungal diversity. On the other hand, cow dung stimulated beneficial soil microbes, including Bacillus, Rhodanobacter, Streptomyces, and Chaetomium, while suppressing Acidobacteriota. Community-level physiological profiling analysis using Biolog Ecoplates containing 31 different carbon sources showed that cow dung soil had a different metabolic activity with higher utilization rates of carbohydrates and polymer carbon sources, mainly Tween 40 and beta-methyl-d-glucoside. These carbon sources were most highly associated with Bacillota. Furthermore, predicted ecological function analyses of bacterial and fungal communities showed that cow dung had a higher predicted function of fermentation and fewer functions related to plant pathogens and fungal parasites, signifying its potential to enhance soil suppressiveness. Co-occurrence network analysis based on random matrix theory (RMT) revealed that cow dung transformed the soil microbial network into a highly connected and complex network. This study is the first to report the alleviation of GRP using cow dung as a soil amendment, and the study contributes significantly to our understanding of how the soil microbiota and metabolic alterations via cow dung can aid in GRP alleviation.

Comparative analysis of scalp and gut microbiome in androgenetic alopecia: A Korean cross-sectional study.

Androgenetic alopecia (AGA) is a non-scarring and progressive form of hair loss occurring in both men and women. Although genetic predisposition and sex steroid hormones are the main causes, many factors remain unknown, and various extrinsic factors can negatively affect the lifespan of hair. We investigated skin-gut axis microorganisms as potential exogenous factors causing AGA, through comparative analyses of the scalp and gut microbiome in individuals with and without AGA in a Korean cohort. Using 16S rRNA gene sequencing, we characterized the scalp and gut microbiomes of 141 individuals divided into groups by sex and presence of AGA. Alpha diversity indices in the scalp microbiome were generally higher in individuals with AGA than in healthy controls. These indices showed a strong negative correlation with scalp-inhabitant bacteria (Cutibacterium and Staphylococcus), indicating that the appearance of non-inhabitant bacteria increases as hair loss progresses. No significant differences in diversity were observed between the gut microbiomes. However, bacterial functional differences, such as bile acid synthesis and bacterial invasion of epithelial cells, which are related to intestinal homeostasis, were observed. The networks of the scalp and gut microbiome were more complex and denser with higher values of the network topology statistic coefficient values (i.e., transitivity, density, and degree centrality) and more unique associations in individuals with AGA than in healthy controls. Our findings reveal a link between skin-gut microorganisms and AGA, indicating the former's potential involvement in the latter's development. Additionally, these results provide evidence for the development of cosmetics and therapeutics using microorganisms and metabolites involved in AGA.

Changes in Microbial Community Structure in Response to Gummosis in Peach Tree Bark.

Peach gummosis disease has been identified as a serious challenge in Korean agriculture and has developed to become a major cause of agricultural productivity losses. However, treatments for gummosis have not been systemically established and studies of the microbiome closely related to this plant disease are lacking. Therefore, we analyzed the bacterial and fungal communities in the bark and rhizosphere soil of healthy peach trees and those with gummosis. Through high-throughput sequencing, we obtained unprecedented insights into the bacterial and fungal dynamics of each group, including their diversity and taxonomic classification, as well as network analyses. We found that the presence of gummosis drives a significantly higher alpha diversity in the bark bacterial community. Peach gummosis bark mycobiomes included greater numbers of opportunistic pathogens such as Ascochyta, Botryosphaeria, Saccharomyces, Nectriaceae_NA, Trametes, and Valsaceae_NA. However, the microbiome also included bacteria beneficial to plant growth and the production of polysaccharides-namely, 1174-901-12, Catenibacterium, Cutibacterium, Friedmanniella, Methylobacterium-Methylorubrum, Pseudomonas, Rhodobacter, and Sphingomonas. Furthermore, we confirmed that gummosis induced a more complex structure in the bark microbiome network. We conclude that the findings of this study provide a valuable aid in profiling the overall peach tree microbial ecosystem, which can be utilized to develop precise biomarkers for the early diagnosis of gummosis.

Complete Genome Sequence of Bacillus amyloliquefaciens KNU-28 Isolated from Peach Leaves (Prunus Persica [L.] Batsch).

This study presented the complete genome sequence of B. amyloliquefaciens KNU-28 isolated from the leaves of the peach (Prunus persica [L.] Batsch). The genome of this strain comprised one chromosome with 4,238,926 bp and 45.9% of GC content.

Potential Association between Vaginal Microbiota and Cervical Carcinogenesis in Korean Women: A Cohort Study.

Convincing studies demonstrated that vaginal flora is one of the most impactful key components for the well-being of the genital tract in women. Nevertheless, the potential capability of vaginal-derived bacterial communities as biomarkers to monitor cervical carcinogenesis (CC) has yet to be studied actively compared to those of bacterial vaginosis (BV). We hypothesized that vaginal microbiota might be associated with the progression of CC. In this study, we enrolled 23 participants, including healthy controls (HC group; n = 7), patients with cervical intraepithelial neoplasia (CIN) 2 and 3 (CIN group, n = 8), and patients with invasive cervical cancer (CAN group; n = 8). Amplicon sequencing was performed using the Ion Torrent PGM to characterize the vaginal microbiota. Patients with CIN and CAN presented vaginal microbiota dysbiosis compared with HC. The alpha diversity analysis revealed that CC has a trend to be increased in terms of diversity indexes. Moreover, CC was associated with the abundance of specific microbes, of which Lactobacillus and Gardnerella were the most significantly different between HC and CIN, whereas Streptococcus was differentially abundant in CAN compared with CIN. We then evaluated their diagnostic abilities. Testing in terms of diagnostic ability using the three genera revealed considerably high performance with an area under the receiver-operating characteristic curve of 0.982, 0.953, and 0.922. The current study suggests that the presence of Gardnerella and Streptococcus may be involved in the advancment of CC.

Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis.

The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon's diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; Prevotella was significantly more abundant in the CAN group and Clostridium in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.

Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia.

Although emerging evidence revealed that the gut microbiome served as a tool and as biomarkers for predicting and detecting specific cancer or illness, it is yet unknown if vaginal microbiome-derived bacterial markers can be used as a predictive model to predict the severity of CIN. In this study, we sequenced V3 region of 16S rRNA gene on vaginal swab samples from 66 participants (24 CIN 1-, 42 CIN 2+ patients) and investigated the taxonomic composition. The vaginal microbial diversity was not significantly different between the CIN 1- and CIN 2+ groups. However, we observed Lactobacillus amylovorus dominant type (16.7%), which does not belong to conventional community state type (CST). Moreover, a minimal set of 33 bacterial species was identified to maximally differentiate CIN 2+ from CIN 1- in a random forest model, which can distinguish CIN 2+ from CIN 1- (area under the curve (AUC) = 0.952). Among the 33 bacterial species, Lactobacillus iners was selected as the most impactful predictor in our model. This finding suggests that the random forest model is able to predict the severity of CIN and vaginal microbiome may play a role as biomarker.