Microbiome Analysis Research Articles

Corrigendum: Microbiome and response surface methodology analyses reveal Acetobacter pasteurianus as the core bacteria responsible for aerobic spoilage of corn silage (Zea mays) in hot and humid areas

Corrigendum: Microbiome and response surface methodology analyses reveal Acetobacter pasteurianus as the core bacteria responsible for aerobic spoilage of corn silage (Zea mays) in hot and humid areas

Faecal sample storage without ethanol for up to 24h followed by freezing performs better than storage with ethanol for shotgun metagenomic microbiome analysis in patients with inflammatory and non-inflammatory intestinal diseases and healthy controls.

The influence of different faecal collection methods on metagenomic analyses remains under discussion, and there is no general agreement on which collection method is preferable for gut microbiome research. We compared faecal samples collected in tubes without preservatives with those containing 10mL of 96% ethanol for gut microbiome research when the timeframe from defecation to freezing at -80°C was up to 24h. We aimed to compare the collection methods on faeces from participants with inflammatory and non-inflammatory gastrointestinal disorders and healthy controls to investigate the most suitable method when considering data yield, human fraction of sequencing reads, and ease of use. We also examined the faecal sample homogeneity. Faeces collected in tubes without preservatives resulted in more sequencing reads compared to faeces collected in tubes with 96% ethanol and were also easier to handle. The human fraction of total reads in faeces collected in ethanol from participants with inflammatory bowel disease was higher than all other samples. DNA extraction and sequencing from two different locations in the same faecal sample gave similar results and showed sample homogeneity.

A step-by-step procedure for analysing the 16S rRNA-based microbiome diversity using QIIME 2 and comprehensive PICRUSt2 illustration for functional prediction.

Short-read sequencing technology has emerged as a preferred tool to analyse the bacterial composition of a niche by targeting hypervariable regions of the 16S rRNA gene. It targets the short hypervariable regions of the 16S rRNA gene and uncovers the taxonomic profile and their associated pathways. QIIME 2 is preferred and ready-to-use pipelines that perform stepwise analysis of massive short reads of 16S rRNA genes. This wrapper comprises several tools that include quality checking, denoising, taxonomic classification, alignment, and diversity analysis. However, it demands huge bioinformatic analysis practices which are quite challenging to many microbiologists working in the field of traditional microbiology. This paper, therefore, aimsto make microbiologists familiar with the steps of computational analysis for processing 16S rRNA-based sequences. Here, we are presenting stepwise processing of NGS sequences using the QIIME 2 platform along with their analyses, which include installing QIIME 2, importing and processing data, quality checks, taxonomy assignments, and diversity analysis. Besides, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) has also been illustrated to understand the correlation between metabolic and physiological footprints of the different species observed during microbiome analysis. Therefore, this paper can be used as a handy toolkit for those researchers who are less familiar with its associated bioinformatic analysis.

Longitudinal analyses of infants' microbiome and metabolome reveal microbes and metabolites with seemingly coordinated dynamics.

Population studies have shown that the infant's microbiome and metabolome undergo significant changes in early childhood. However, no previous study has investigated how diverse these changes are across subjects and whether the subject-specific dynamics of some microbes correlate with the over-time dynamics of specific metabolites. Using mixed-effects models, and data from the ABC study, we investigated the early childhood dynamics of fecal microbiome and metabolome and identified 83 amplicon sequence variants (ASVs) and 753 metabolites with seemingly coordinated trajectories. Enrichment analysis of these microbes and molecules revealed eight ASV families and 23 metabolite groups involving 1032 ASV-metabolite pairs with their presence-absence changing in a coordinated fashion. Members of the Lachnospiraceae (464/1032) and metabolites related to cholestane steroids (309/1032) dominated proportional shifts within the fecal microbiome and metabolome as infants aged.

Partial replacement of soybean with local alternative sources: effects on behavior, cecal microbiota, and intestinal histomorphometry of local chickens

Interest in partially replacing soybean meal in poultry diets with alternative protein sources such as agri-industrial by-products and black soldier fly (BSF, Hermetia illucens) has gained significant attention due to sustainability concerns. This study aimed to evaluate the effects of broiler diets in which soybean meal was partially substituted with agri-industrial by-products with or without BSF larvae meal, on the behavior, intestinal histomorphometry, and microbiome profile of a local broiler chicken strain. There were three dietary treatments. (1) A corn-soybean-based diet (Control), (2) a diet in which soybean was partly replaced (SPR) with local agri-industrial by-products, namely sunflower meal, brewers' dried grain, and wheat middlings, and (3) a diet in which BSF (5%) meal was added to SPR (SPR+BSF). Behavior was recorded on days 14, 35, and 49 at the pen level. On day 55, intestinal segments and cecal contents were collected from eight chickens per pen for histomorphometry and microbiome analysis. Dietary manipulations did not affect the behavior of broiler chickens (P > 0.05) suggesting that the experimental diets had no influence on behavior. A significant interaction between the intestinal segment and diets revealed that the SPR and SPR+BSF diets decreased duodenal villus height (VH) compared to the control diet (P < 0.05). However, this effect was not consistent across all of intestinal segments. Diet did not affect villus height to crypt depth ratio (VH/CD; P > 0.05), indicating no significant impact on the absorptive capacity of the digestive system. Firmicutes and Bacteroidetes were the dominant phyla in the cecal samples. Colidextribacter and Oscillibacter spp. were more abundant in chickens fed the SPR diet compared to those fed the control diet. The SPR+BSF diet resulted in higher abundance of Rikenella and Colidextribacter spp. compared to the control diet, while Desulfovibrio, Ruminococcus torques group, and Lachnoclostridium were more abundant in the ceca of birds fed the SPR diet than those fed SPR+BSF. In conclusion, replacement of soybean with agri-industrial by-products and BSF larvae meal could regulate the cecal microbiota composition without negatively affecting the behavior and intestinal histomorphometry of the local chickens.

Comparative analysis of oral saliva microbiomes and metabolites in Han population at different altitudes

ObjectiveThis study investigated the differences in oral saliva microbiota composition and metabolic products among Han Chinese populations living at different altitudes, as well as their correlations.MethodThe analysis was conducted using the 16S rRNA gene sequencing method and untargeted metabolomics.Results16S gene sequencing results showed significant differences in bacterial diversity and composition between HH (High altitude Han) group and LH (Low altitude Han) group. LEfSe analysis showed that Selenomonas, Leptotrichia, Veillonella, Prevotella relatively abundant are higher in HH group, Haemophilus, Neisseria, Actinobacillus, Aggregatibacter are higher in LH group (p<0.05). Furthermore, as depicted in the phylogenetic tree, there are differences observed between the two groups at all taxonomic levels: 4 phyla, 6 classes, 6 orders, 9 families, 9 genera and 8 species (p<0.05). After conducting PICRUSt functional prediction analysis, we identified 11 significantly different KEGG categories (level 2) between the two groups. These categories primarily encompass energy metabolism, amino acid metabolism, and carbohydrate metabolism. Furthermore, non-targeted metabolomics analysis revealed a total of 997 distinct metabolites in the two groups. These differentiated metabolites can be classified into 13 Class I categories including amino acids and their metabolites, benzene and its derivatives, organic acids and their derivatives, heterocyclic compounds, aldehydes, ketones and esters, nucleotides and their metabolites among others. Additionally, fatty acyl compounds, alcohols and amines as well as glycerophospholipids are present along with carbohydrates and other physiologically active components such as hormones. Finally, Pearson correlation analysis of the top 20 differential metabolites with microorganisms demonstrated an interaction between them; however further experimental verification is required to elucidate the specific mechanism of action.ConclusionTherefore, this study revealed the effect of altitude on oral saliva microbes and metabolites, as well as their correlations.

A Comparative Analysis of the Ocular Microbiome: Insights into Healthy Eyes and Anophthalmic Sockets

The purpose of this study is to characterize the ocular surface microbiota of patients with an anophthalmic cavity. An eNAT with 1 mL of Liquid Amies Medium was used to collect samples. Microbial DNA from anophthalmic socket and healthy fellow control eye samples was isolated and sequenced. Raw reads were analyzed with GAIA (v 2.02). The richness and Shannon alpha diversity metrics, as well as Bray–Curtis beta diversity and Wilcoxon signed-rank test values, were computed with R packages such as phyloseq, mia, or DESeq2 to allow for microbiome analysis. Principal coordinate analysis (PCoA) was performed using the function plotReducedDim from the R package scater. The different taxonomic profiles were described under the concept of eye community state type (ECST). The microbiomes of both eyes from 25 patients with an anophthalmic cavity were analyzed in this study. While the microbial communities of paired eyes from the same patients showed notable dissimilarity, no consistent patterns emerged when comparing healthy eyes to anophthalmic sockets. Alpha diversity values did not significantly differ between healthy eyes and anophthalmic socket samples, though there was considerable variability within each group. Notably, anophthalmic socket samples generally exhibited lower abundances of genera such as Staphylococcus, Enterococcus, Paenibacillus, and Sediminibacterium compared to their healthy counterparts. Microbial variability between healthy eyes and anophthalmic sockets may be due to anatomical differences. Further research is needed to determine whether patients without anophthalmic sockets exhibit similar microbiome patterns in both eyes.

Optimisation of the sampling method for skin microbiome studies in healthy children: a pilot cohort study

IntroductionRecent interest in the diverse ecosystem of bacteria, fungi and viruses that make up the skin microbiome has led to numerous studies investigating the skin microbiome in healthy skin and in dermatological conditions. However, skin microbiome analysis is challenging due to relatively low numbers of skin microorganisms compared to mucosal sites, such as the respiratory or gastrointestinal tracts. Microbiome results are heavily influenced by sampling methods. Previous sampling methods include that of cotton swabs, tape stripping, patch sampling and punch biopsies. It is essential to have a standardised sampling method for microbiome studies to have comparable results between studies. Two non-invasive methods of sampling the skin microbiome; a skin scraping versus a flocked swab were chosen as methodologies likely to be efficient, effective, and easy to access for future skin microbiome studies in children. Here we compare the two sampling methods to describe the composition of the skin microbiome in healthy children.MethodSamples were collected from six healthy children aged three to nine years from the skin overlying the cubital fossa, cheek and axilla using (i) flocked swabs and (ii) skin scrapings with a glass slide. Samples were collected from the left and right sides of the body at two separate time points, one week apart. Quantitative PCR of the gene encoding 16S ribosomal ribonucleic acid (rRNA) was performed to compare the bacterial load collected by each sampling method. Full-length 16S rRNA gene amplicon sequencing was performed to compare the relationship of sampling method and time with the diversity and ecology of bacteria between different body sites.ResultsFrom six children, 78 flocked swabs and 78 skin scraping samples were collected, along with details of their overall health and skin care practices. qPCR results indicated higher total bacterial load from flocked swabs compared with skin scrapings. Flocked swabs and skin scraping methods had very similar bacterial compositional profiles. The skin microbiome was diverse between individuals and remained relatively stable within individuals over time.DiscussionOverall, results were similar between sample types, however bacterial DNA yield was higher for flocked swab samples (compared to skin scraping methods) and with a simpler protocol is the preferred sampling method for future studies.

Stability in fecal metabolites amid a diverse gut microbiome composition: a one-month longitudinal study of variability in healthy individuals

ABSTRACT An extensive network of microbial-host interactions exists in the gut, making the gut microbiome a complex ecosystem to untangle. The microbial composition and the fecal metabolites are important readouts to investigate intricate microbiota-diet-host interplay. However, this ecosystem is dynamic, and it is of interest to understand the degree and timescale of changes occurring in the gut microbiota, during disease as well as in healthy individuals. Cross-sectional study design is often used to investigate the microbiome, but this design provides a static snapshot and cannot provide evidence on the dynamic nature of the gut microbiome. Longitudinal studies are better suited to extrapolate causation in a study or assess changes over time. This study investigates longitudinal change in the gut microbiome and fecal metabolites in 14 healthy individuals with weekly sampling over a period of one-month (four time points), to elucidate the temporal changes occurring in the gut microbiome composition and fecal metabolites. Utilizing 16S rRNA amplicon sequencing for microbiome analysis and NMR spectroscopy for fecal metabolite characterization, we assessed the stability of these two types of measurable parameters in fecal samples during the period of one month. Our results show that the gut microbiome display large variations between healthy individuals, but relatively lower within-individual variations, which makes it possible to uniquely identify individuals. The fecal metabolites showed higher stability over time compared to the microbiome and exhibited consistently smaller variations both within and between individuals. This relative higher stability of the fecal metabolites suggests a balanced, consistent output even amid individual’s differences in microbial composition and they can provide a viable complementary readout to better understand the microbiome activity.

Large-scale metagenomic analysis of oral microbiomes reveals markers for autism spectrum disorders.

The link between the oral microbiome and neurodevelopmental disorders remains a compelling hypothesis, still requiring confirmation in large-scale datasets. Leveraging over 7000 whole-genome sequenced salivary samples from 2025 US families with children diagnosed with autism spectrum disorders (ASD), our cross-sectional study shows that the oral microbiome composition can discriminate ASD subjects from neurotypical siblings (NTs, AUC = 0.66), with 108 differentiating species (q < 0.005). The relative abundance of these species is highly correlated with cognitive impairment as measured by Full-Scale Intelligence Quotient (IQ). ASD children with IQ < 70 also exhibit lower microbiome strain sharing with parents (p < 10-6) with respect to NTs. A two-pronged functional enrichment analysis suggests the contribution of enzymes from the serotonin, GABA, and dopamine degradation pathways to the distinct microbial community compositions observed between ASD and NT samples. Although measures of restrictive eating diet and proxies of oral hygiene show relatively minor effects on the microbiome composition, the observed associations with ASD and IQ may still represent unaccounted-for underlying differences in lifestyleamong groups. While causal relationships could not be established, our study provides substantial support to the investigation of oral microbiome biomarkers in ASD.

Bacterial diversity in rice field soil and sludge soil samples: A comparative metagenomics-based study

Abstract Soil contains several organic and inorganic substances. It also has a large number of microbial diversity including bacteria. This microbial community affects the physicochemical properties of soil. This bacterial bacteria diversity is also involved in biomass degradation and plant growth promotion. Metagenomic sequencing is used to analyze microbial diversity in environmental samples. The metagenomic profiling of bacterial populations in the sludge and rice field soil was done at phylum, class, order, family, genus, and species levels. The metagenomic study revealed that Proteobacteria, Bacteroidetes, Chloroflexi, Acidobacteria and other unknown bacteria were reported in 16S rRNA Illumina MiSeq in the sludge and rice soil samples. We compared the taxonomic classification of bacterial diversity found in sewage soils with that of rice field soils using metagenomic sequencing analysis of the V3-V4 region of the 16S rRNA. In rice soil, the most abundant group was Proteobacteria followed by Chloroflexi, Acidobacteria, Actinobacteria, and Bacteroidetes which increase the soil nutrient and influence the production of the plant. Bacteroidetes are the most dominant group in sludge soil than rice soil. Consequently, the core microbiome analysis indicated that Sorangium, Geobacter, Azoarcus, and Anaeromyxobacter bacterial genera were more abundant in both soil samples.&amp;#xD;

Preliminary Functional Analysis of the Gut Microbiome in Colic Horses

The gut microbiome plays a critical role in maintaining horse health, influencing digestion, immunity, and overall well-being. However, in certain conditions like colic, there is evidence of significant alterations in the microbial community. To analyze the composition of the fecal microbiome and the enriched predicted metabolic functions of horses with colic compared to a control group, 14 horses with colic and 14 control horses were recruited. From a stool sample, DNA extraction was carried out for subsequent 16S rRNA metagenomic analysis. The composition of the microbiome was analyzed from the sequences of each sample using the QIIME version 1.8.0 and DADA2 version 1.22 programs. PICRUSt2 was used to predict metabolic functions. Statistical analyses were performed with the Mann–Whitney U test from the Python scipy v1 package. The gut microbiomes of both groups were dominated by Firmicuteota, Bacteroidota, and Pseudomonadota phyla. Colic in horses was associated with reduced diversity, reduced abundance of Fibrobacter, and an increase in Streptococcus. The abundance of Firmicuteota was negatively correlated with Pseudomonadota and Actinobacteriota. The equine colic microbiome was predicted to be enriched in aerobic respiration pathways and fatty acid and amino acid degradation. These observations indicate discrete but important differences in the gut microbiome of colic horses.

Potential role of alginate in marine bacteria-yeast interactions.

The ability of microorganisms to decompose brown algae has attracted attention. This study aims to clarify the characteristics of marine microbial communities in which prokaryotic and eukaryotic microorganisms interact via the metabolism of brown algae carbohydrates. Amplicon-based microbiome analysis revealed the predominance of the genera Marinomonas and Vibrio in seawater and seaweed samples mixed with alginate and mannitol, which are the primary carbohydrates in brown algae. Three Vibrio species and Candida intermedia were isolated via alginate enrichment culture. Although C. intermedia did not utilize alginate as a nutrient source, the yeast grew in the spent alginate medium in which Vibrio algivorus had been cultured. Coculture with C. intermedia and the Vibrio isolates, especially V. algivorus, also enhanced the growth of the yeast on alginate. These results suggested that C. intermedia grew because of the supply of nutrients via alginate metabolism by Vibrio species. In the coculture medium, the amount of phosphatidylserine increased in the early phase but decreased with the growth of C. intermedia, indicating that phosphatidylserine secreted by Vibrio is involved in the putative mutualistic interaction. We examined whether such interaction is applicable to the production of useful substances and succeeded in lipid production by oleaginous marine yeast Yarrowia lipolytica through coculture with V. algivorus. Our study suggested the potential of mutualistic interaction via degradation of alginate by marine Vibrio for production of industrially useful substances in yeast cells.IMPORTANCEIn this study, we analyzed the microbiome of seawater and seaweed in the presence of brown algae carbohydrates and reconstructed the putative mutualistic relationship of marine Vibrio and Candida intermedia mediated by metabolism of brown algae in the ocean.

Exploring multi-omics and clinical characteristics linked to accelerated biological aging in Asian women of reproductive age: insights from the S-PRESTO study

BackgroundPhenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging.MethodsPhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors.ResultsHigher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel.ConclusionsOur findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

The microbiome analysis of ripen grape berries supports the complex etiology of sour rot

The microbiome analysis of ripen grape berries supports the complex etiology of sour rot

Differentiation complex sputum microbiome in patients suspected TB pulmonary

&lt;b&gt;Purpose: &lt;/b&gt;This is the first study to attempt microbiome diversity using metagenomic full-length 16S rRNA from respiratory specimens suspected of chronic pulmonary TB patients.&lt;br /&gt; &lt;b&gt;Materials and methods:&lt;/b&gt; A 33 patients with suspected chronic pulmonary TB were included. Sputum specimens were cultured to detect &lt;i&gt;mycobacterium sp.&lt;/i&gt; and extracted using QiAmp DNA mini kit modification and 16S rRNA metagenomic sequencing by nanopore grid ion sequencer. Microbiome analysis was performed using Pavian and Krona tools.&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;9 patients were diagnosed with TB based on GeneXpert MTB/RIF assay, and 3 patients were detected with NTM pulmonary infection.The common genera identified from TB culture positive patients were &lt;i&gt;streptococcus sp.&lt;/i&gt;, &lt;i&gt;prevotella sp., &lt;/i&gt;and &lt;i&gt;veilonella sp. &lt;/i&gt;However, less was detected in two NTM infection patients&lt;i&gt;. &lt;/i&gt;Metagenomic analysis revealed community bacteria species, including mycobacterium tuberculosis and NTM species, with the lowest number of unique reads. The abundance of &lt;i&gt;streptococcus sp.&lt;/i&gt; were less than 30% in 4 patient with comorbid diabetes mellitus.&lt;br /&gt; &lt;b&gt;Conclusions:&lt;/b&gt; Metagenomic targeted 16SrRNA full-length sequencing in the clinical respiratory specimen can provide diagnostic insight beyond standard microbiologic cultures and detailed profiling of microbial communities at the species level.

Ellagic Acid Protects against Alcohol-Related Liver Disease by Modulating the Hepatic Circadian Rhythm Signaling through the Gut Microbiota-NPAS2 Axis.

Alcohol-related liver disease (ALD) encompasses a spectrum of hepatic disorders resulting from alcohol abuse, which constitutes the predominant etiology of morbidity and mortality associated with hepatic pathologies globally. Excessive alcohol consumption disrupts the integrity of the intestinal barrier and perturbs the balance of gut microbiota, thereby facilitating the progression of ALD. Ellagic acid (EA) has been extensively reported to be an effective intervention for alleviating liver symptoms. However, the target molecules of EA in improving ALD and its underlying mechanism remain elusive. First, our study indicates that EA ameliorated ALD through the hepatic circadian rhythm signaling by up-regulating neuronal PAS domain protein 2 (NPAS2). Furthermore, analysis of the intestinal microbiome showed that EA significantly enhanced the abundance of beneficial bacteria, which was positively correlated with NPAS2 expression and negatively correlated with liver injury. Finally, antibiotic treatment and fecal microbiota transplantation (FMT) experiments established a causal relationship between the reshaped microbiota and NPAS2 in the amelioration of ALD. In summary, our study demonstrates novel evidence that EA attenuated ALD by modulating the hepatic circadian rhythm signaling pathway via the gut microbiota-NPAS2 axis, providing valuable insights for EA and microbiome-targeted interventions against ALD.

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning

BackgroundThis study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in diabetes progression.MethodsParticipants included 30 Pre-DM patients, 37 individuals with T2DM, and 28 healthy controls. Tongue images and oral/fecal samples were analyzed using image processing and 16S rRNA sequencing. Machine learning techniques, including support vector machine (SVM), random forest, gradient boosting, adaptive boosting, and K-nearest neighbors, were applied to integrate tongue image data with microbiota profiles to construct predictive models for Pre-DM and T2DM classification.ResultsSignificant shifts in tongue characteristics were identified during the progression from Pre-DM to T2DM. Elevated Firmicutes levels along the oral-gut axis were associated with white greasy fur, indicative of underlying metabolic changes. An SVM-based predictive model demonstrated an accuracy of 78.9%, with an AUC of 86.9%. Notably, tongue image parameters (TB-a, perALL) and specific microbiota (Escherichia, Porphyromonas-A) emerged as prominent diagnostic markers for Pre-DM and T2DM.ConclusionThe integration of tongue diagnosis with microbiome analysis reveals distinct tongue features and microbial markers. This approach significantly improves the diagnostic capability for Pre-DM and T2DM.

Integrated gut microbiome and UHPLC-MS metabolomics to reveal the prevention mechanism of pidanjiangtang granules on IGT Rats

IntroductionPidanjiangtang (PDJT) is a traditional Chinese medicine formula empirically used to treat impaired glucose tolerance (IGT) based on the "Pidan" theory from the classic ancient book Nei Jing. However, the mechanism of PDJT intervention for IGT remains to be studied. ObjectiveThis study aims to explore the mechanism of PDJT granules intervention in IGT by integrating gut microbiome and UHPLC-MS untargeted metabolomics. Materials and methodsThe IGT model was established in 6-week-old male Sprague-Dawley (SD) rats by feeding them a high-fat diet and using an STZ injection. The low, medium, and high doses of PDJT were used for six weeks. metformin (Glucophage) was used as the positive control drug. The efficacy of PDJT was evaluated using fasting blood glucose (FBG), blood glucose maximum (BGmax), blood lipid, and inflammatory factor levels. Finally, 16S rDNA gut microbiome sequencing with metabolomics analysis was used to explore the pharmacological mechanism of PDJT intervention in IGT. ResultsPDJT could reverse the phenotype of IGT rats, reduce blood glucose levels, improve lipid metabolism disorder, and reduce inflammatory response. Gut microbiome analysis found that PDJT can improve gut microbiota composition and abundance of three phyla (Firmicutes, Bacteroidota, Desulfobacterota) and four genera (unclassified_f__Lachnospiraceae, Ruminococcus, Allobaculum, Desulfovibrio), which play an important role in the process of PDJT intervention on glucose metabolism and lipid metabolism in IGT rats. UHPLC-MS untargeted metabolomics showed that PDJT could regulate the levels of 258 metabolites in lipid metabolism pathways, inflammatory response pathways, fat and protein digestion, and absorption. The combined analysis of the two omics showed that improving the body's metabolism by gut microbes may be the possible mechanism of PDJT in treating IGT. Thus, this study provides a new method to integrate gut microbiome and UHPLC-MS untargeted metabolomics to evaluate the pharmacodynamics and mechanism of PDJT intervention in IGT, providing valuable ideas and insights for future research on the treatment of IGT with traditional Chinese medicine.

Pecan-medicinal crops intercropping improved soil fertility and promoted interactions between soil microorganisms and metabolites

BackgroundPecan [Carya illinoinensis (Wangenh.) Koch] is a widely cultivated dried fruit and woody oil tree with high economic value. Continuous sole planting of pecan caused some land to lie idle and deterioration of soil conditions. Tree and medicinal crops intercropping represents an environmental-friendly and economically feasible solution to these issues. Thus, we aimed to explore the underlying mechanism by which intercropping improved soil condition by regulating the interactions of the soil microbiome and metabolome. In this study, pecans were intercropped with medicinal crops caper spurge and honeysuckle on a tree farm in China. A combined analysis of soil microbiomes and metabolomes was performed to discover the effects of intercropping on bulk and rhizosphere soils.ResultsThe results showed that intercropping improved the edaphic properties of bulk soil and promoted the growth of pecan and caper spurge. Intercropping also significantly altered the structures of both bacterial and fungal communities in bulk soil, stabilised the enrichment of nitrogen-cyclic bacteria, for instance, Bacillus, and decreased the relative abundances of plant–pathogenic fungi, for instance, Fusarium. In addition, the result of metabolomic analysis showed that intercropping promoted the synthesis of functional compounds, such as trehalose and ethanolamine, which enhanced plant disease resistance in bulk soils. Moreover, the co-occurrence networks of microbiomes and metabolomes of bulk soils revealed that Bacillus was significantly correlated with Fusarium, Alternaria, and trehalose under intercropping patterns. Furthermore, analysis of microbiomes and metabolomes in the rhizosphere soil of caper spurge and honeysuckle revealed that Penicillium and Rhizomicrobium were significantly increased by intercropping and showed more dynamic connections with other genera and metabolites compared with single planting.ConclusionsOverall, intercropping pecans with caper spurge and honeysuckle can improve soil conditions and promote plant growth through microbiological and metabolomics integrated analyses. This study provides valuable information and theoretical basis for optimizing land resource utilisation and improving soil conditions in tree fields like pecan fields via intercropping, thereby reducing production losses and ensuring economic benefits.Graphical