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The identification of microbial markers adequate to delineate the disease‐related microbiome alterations from the complex human gut microbiota is of great interest. Here, we develop a framework combining neural network (NN) and random forest, resulting in 40 marker species and 90 marker genes identified from the metagenomic data set (185 healthy and 183 type 2 diabetes [T2D] samples), respectively. In terms of these markers, the NN model obtained higher accuracy in classifying the T2D‐related samples than other methods; the interaction network analyses identified the key species and functional modules; the regression analysis determined that fasting blood glucose is the most significant factor (p < 0.05) in the T2D‐related alteration of the human gut microbiome. We also observed that those marker species varied little across the case and control samples greatly shift in the different stages of the T2D development, suggestive of their important roles in the T2D‐related microbiome alteration. Our study provides a new way of identifying the disease‐related biomarkers and analyzing the role they may play in the development of the disease. A framework combining neural network and random forest for identifying the type 2 diabetes (T2D)‐related biomarkers. Constructing the directed interaction networks of the biomarkers for analyzing the potential drivers of the microbial community associated with T2D. Analyzing the covary of the biomarkers with the dynamic change of fasting blood glucose in the development of T2D. Highlights A framework combining neural network and random forest for identifying the type 2 diabetes (T2D)‐related biomarkers. Constructing the directed interaction networks of the biomarkers for analyzing the potential drivers of the microbial community associated with T2D. Analyzing the covary of the biomarkers with the dynamic change of fasting blood glucose in the development of T2D.

Background Myasthenia gravis (MG) is an acquired immune-mediated disorder of the neuromuscular junction that causes fluctuating skeletal muscle weakness and fatigue. Pediatric MG and adult MG have many different characteristics, and current MG diagnostic methods for children are not quite fit. Previous studies indicate that alterations in the gut microbiota may be associated with adult MG. However, it has not been determined whether the gut microbiota are altered in pediatric MG patients. Methods Our study recruited 53 pediatric MG patients and 46 age- and gender-matched healthy controls (HC). We sequenced the fecal samples of recruited individuals using whole-genome shotgun sequencing and analyzed the data with in-house bioinformatics pipeline. Results We built an MG disease classifier based on the abundance of five species, Fusobacterium mortiferum , Prevotella stercorea , Prevotella copri , Megamonas funiformis , and Megamonas hypermegale . The classifier obtained 94% area under the curve (AUC) in cross-validation and 84% AUC in the independent validation cohort. Gut microbiome analysis revealed the presence of human adenovirus F/D in 10 MG patients. Significantly different pathways and gene families between MG patients and HC belonged to P. copri, Clostridium bartlettii , and Bacteroides massiliensis . Based on functional annotation, we found that the gut microbiome affects the production of short-chain fatty acids (SCFAs), and we confirmed the decrease in SCFA levels in pediatric MG patients via serum tests. Conclusions The study indicated that altered fecal microbiota might play vital roles in pediatric MG’s pathogenesis by reducing SCFAs. The microbial markers might serve as novel diagnostic methods for pediatric MG.

Gut microbiota make up the largest microecosystem in the human body and are closely related to chronic metabolic diseases. Herein, 520 fecal samples are collected from different regions of China, the gut microbiome in chronic kidney disease (CKD) is characterized, and CKD classifiers based on microbial markers are constructed. Compared with healthy controls (HC, n = 210), gut microbial diversity is significantly decreased in CKD (n = 110), and the microbial community is remarkably distinguished from HC. Genera Klebsiella and Enterobacteriaceae are enriched, while Blautia and Roseburia are reduced in CKD. Fifty predicted microbial functions including tryptophan and phenylalanine metabolisms increase, while 36 functions including arginine and proline metabolisms decrease in CKD. Notably, five optimal microbial markers are identified using the random forest model. The area under the curve (AUC) reaches 0.9887 in the discovery cohort and 0.9512 in the validation cohort (49 CKD vs 63 HC). Importantly, the AUC reaches 0.8986 in the extra diagnosis cohort from Hangzhou. Moreover, Thalassospira and Akkermansia are increased with CKD progression. Thirteen operational taxonomy units are correlated with six clinical indicators of CKD. In conclusion, this study comprehensively characterizes gut microbiome in non‐dialysis CKD and demonstrates the potential of microbial markers as non‐invasive diagnostic tools for CKD in different regions of China. Compared with healthy controls, gut microbial diversity in CKD is significantly reduced, Klebsiella and Akkermansia are significantly increased, Roseburia and Faecalibacterium are significantly reduced, and the predictive function of gut microbiota such as ascorbate metabolism and lipopolysaccharide biosynthesis is significantly enhanced. Akkermansia increases along with the progression of CKD, which is positively correlated with serum creatinine and blood urea nitrogen, and negatively correlated with estimated glomerular filtration rate, and could be used as a therapeutic target to improve the prognosis of CKD. Importantly, gut microbial markers have strong diagnostic potential for CKD and achieve cross‐regional validation, which can be used as a non‐invasive diagnostic tool for CKD.

Oral microbiota is related to the severity and recovery of SARS-CoV-2 infection. This study aims to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection. Herein, we collected tongue-coating samples before infection and then monitored clinical information after infection. Oral microbiota was detected by MiSeq sequencing. We randomly assigned participants from Zhengzhou into discovery and validation cohorts to develop a predictive model and conducted cross-region verification using Xinyang and Hangzhou cohorts. Sixteen asymptomatic patients (AP), 257 mild patients (MP), 106 common patients (CP), and 7 severe patients (SP) were enrolled. Oral microbiota diversity was decreased in CP versus MP. At genus level, 11 microorganisms, including Rothia and Gemella , were increased, while 5 microorganisms, including Selenomonas and Lachnoanaerobaculum , were decreased in CP versus MP. Moreover, the classifier based on 15 optimal markers showed high prediction efficiency in discovery cohort (area under the curve [AUC]: 98.35%), validation cohort (AUC: 81.91%), Xinyang cohort (AUC: 74.34%), and Hangzhou cohort (AUC: 94.44%). Interestingly, a higher abundance of Selenomonas was associated with milder clinical symptoms. In conclusion, our study established a good model to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection, providing a novel strategy for precise prevention and treatment.

The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non- commensals, which are crucial in both -negative and -positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified and as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of and as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

Seminal stains acquired from fabric surfaces stand as pivotal biological evidence of utmost significance for elucidating sexual assault cases. The ability to determine the temporal aspect of a forensic incident via the analysis of a biological specimen found at the crime scene is crucial in resolving most cases. This study aimed to investigate the time-dependent change in the microbiota structure of human seminal stains exposed to indoor environmental conditions. Stains on polyester fabric generated using semen samples from five male volunteers were kept indoors for varying durations of up to 20 days, followed by sequencing of the V1–V9 regions of the 16S rRNA gene of the microbial DNA extracted from the stains. The acquired data provided the taxonomic composition, and microbial alterations across different days were examined. The most abundantly detected phyla in all samples were Firmicutes , Proteobacteria , and Bacteroidetes , and the relative abundances of bacteria were observed to change over time. Statistically significant changes at the species level were found for Treponema medium , Corynebacterium tuberculostearicum , Faecalibacterium prausnitzii , and Anaerostipes hadrus . Alterations observed in the samples between the analyzed time periods were investigated. The changes during the specified time periods were examined, identifying rare bacterial species that were initially present on certain days but later ceased to exist in the environment. Conversely, bacterial species that were absent before exposure but emerged at a later stage were also identified. The findings of this study demonstrate that species-level evaluations, in particular, can provide crucial insights into semen stain age.

The relationship between the oral microbiota and type 1 diabetes (T1D) remains unclear. We aimed to evaluate the variations in the oral microbiome in T1D and identify potentially associated bacterial factors. We performed high-throughput sequencing of the V3-V4 area of the 16S rRNA gene to profile the oral bacterial composition of 47 healthy children (CON group), 46 children with new-onset T1D in the acute phase (NT1D group), and 10 children with T1D in the chronic phase receiving insulin treatment (CT1D group). Multivariate statistical analysis of sequencing data was performed. Compared to the CON group, the NT1D group was characterized by decreased diversity and increased abundance of genera harboring opportunistic pathogens, while this trend was partially reversed in the CT1D group. Differential genera between groups could distinguish the NT1D group from the CON group (AUC = 0.933) and CT1D group (AUC = 0.846), respectively. Moreover, T1D-enriched genera were closely correlated with HbA1c, FBG and WBCs levels. Our results showed that the acute phase of T1D was characterized by oral microbiota dysbiosis, which could be partially ameliorated via glycemic control. The possible role of oral microbiota dysbiosis on oral health and systemic metabolic status in T1D warrants further mechanistic investigation.

Gastric cancer (GC) is a major cause of cancer mortality worldwide. We evaluated whether oral microbiota could be sensitive, specific, and non-invasive markers for early GC detection. Saliva samples were analyzed using rRNA sequencing, and oral microbial markers were validated using an internal validation dataset. Machine learning was used to identify key genera, and functional associations were inferred using Kyoto Encyclopedia of Genes and Genomes pathway and ortholog analyses. Blood samples were also collected, and plasma cytokines were quantified by enzyme-linked immunosorbent assay (ELISA) for pathway-level interpretations. Eight genera- , , , , , , , and -were validated as diagnostic microbial markers (area under the receiver operating characteristic curve [AUC] = 0.91). and were enriched in GC, whereas was depleted and associated with reduced risk. These genera may be functionally linked to pathways involved in GC progression, including NF-κB, IL-6, STAT3, TGF-β1, and Smad2/3. The proposed classification method effectively identified early-stage and tumor-marker-negative GCs, underscoring its clinical translation potential. Oral microbial markers, including , , and , may serve as non-invasive diagnostic markers for GC and may be related to carcinogenic signaling activity.

Background: Spontaneous bacterial peritonitis (SBP) is a severe infection in cirrhotic patients that requires early diagnosis to improve the long-term outcome. Alterations in the gut microbiota have been shown to correlate with the development and progression of liver cirrhosis. However, the relationship between SBP and gut microbiota remains unknown.Methods: In this study, we applied 16S rRNA pyrosequencing of feces to ascertain possible links between the gut microbiota and SBP. We recruited 30 SBP patients, 30 decompensated cirrhotic patients without SBP (NSBP) and 30 healthy controls. Metagenomic functional prediction of bacterial taxa was achieved using PICRUSt.Results: The composition of the gut microbiota in the SBP patients differed remarkably from that in the NSBP patients and healthy individuals. The microbial richness was significantly decreased, while the diversity was increased in the SBP patients. Thirty-four bacterial taxa containing 15 species, mainly pathogens such as Klebsiella pneumoniae, Serratia marcescens and Prevotella oris, were dominant in the SBP group, while 42 bacterial taxa containing 16 species, especially beneficial species such as Faecalibacterium prausnitzii, Methanobrevibacter smithii and Lactobacillus reuteri, were enriched in the NSBP group. Notably, we found that 18 gene functions of gut microbiota were different between SBP patients and NSBP patients, which were associated with energy metabolism and functional substance metabolism. Five optimal microbial markers were determined using a random forest model, and the combination of Lactobacillus reuteri, Rothia mucilaginosa, Serratia marcescens, Ruminococcus callidus and Neisseria mucosa achieved an area under the curve (AUC) value of 0.8383 to distinguish SBP from decompensated cirrhosis.Conclusions: We described the obvious dysbiosis of gut microbiota in SBP patients and demonstrated the potential of microbial markers as noninvasive diagnostic tools for SBP at an early stage.

Disruptions in vaginal microbiota and metabolites during pregnancy may be the most important risk factor for preterm delivery, thus the difference in vaginal microbiota and metabolites between women who subsequently delivered at term and who eventually experienced preterm birth. In this study, 63 participants were enrolled before the cervical cerclage surgery (namely pre-cerclage), comprising women who subsequently delivered at term and who eventually experienced preterm birth. The cervical-vaginal fluid (CVF) was collected two days prior to the cervical cerclage surgery. Compared with the term birth groups (PrTG), the proportion of beneficial bacteria ( Lactobacillus , Prevotella , Trichococcus , Neisseria and Gemella ) in the preterm birth group (PrPG) were significantly reduced ( p < 0.05), while the proportion of harmful bacteria ( Thauera , Ochrobactrum , Gardnerella , Massilia , Phyllobacteriaceae and Atopobium ) were significantly increased ( p < 0.05). In addition, vaginal metabolomics-based LC-Orbitrap-MS/MS revealed that the contents of 2-Piperidone, Melphalan, N-acetylputrescine, Obatoclax, Eurostoside, Pregnanediol 3-O-glucuronide, O-Phospho-L-serine, 1-Kestose and N-arachidonylglycine were significantly decreased in the PrPG group compared with the PrTG group, while Acenocoumarol, Isopyrazam, Pentosidine, hexose, 7-Hydroxymitragynine, PE, Tamoxifen and 1-Deoxynojirimycin contents were significantly increased. These results suggest that specific bacterial species and metabolites may serve as potential biomarkers for preterm birth prediction, and approve the theoretical basis for the intervention of preterm birth.