3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

Carposphere microbiota alters grape volatiles and shapes the wine grape typicality

While specific environments are known to shape plant metabolomes and the makeup of their associated microbiome, it is as yet unclear whether carposphere microbiota contribute to the characteristics of grape fruit flavor of a particular wine region. Here, carposphere microbiomes and berry transcriptomes and metabolomes of three grape cultivars growing at six geographic sites were analyzed. The composition of the carposphere microbiome was determined mainly by environmental conditions, rather than grape genotype. Bacterial microbiota likely contributed to grape volatile profiles. Particularly, candidate operational taxonomic units (OTUs) in genus Sphingomonas were highly correlated with grape C6 aldehyde volatiles (also called green leaf volatiles, GLVs), which contribute to a fresh taste. Furthermore, a core set of expressed genes was enriched in lipid metabolism, which is responsible for bacterial colonization and C6 aldehyde volatile synthesis activation. Finally, a similar grape volatile profile was observed after inoculating the berry skin of two grape cultivars with Sphingomonas sp., thus providing evidence for the hypothetical microbe-metabolite relationship. These results provide novel insight into how the environment-microbiome-plant quality (E × Mi × Q) interaction may shape berry flavor and thereby typicality, serving as a foundation for decision-making in vineyard microbial management.

Linking Gut Microbiota, Oral Microbiota, and Serum Metabolites in Insomnia Disorder: A Preliminary Study

Purpose

Despite recent findings suggesting an altered gut microbiota in those suffering from insomnia disorder (ID), research into the gut microbiota, oral microbiota, serum metabolites, and their interactions in patients with ID is sparse.

Patients and Methods

We collected a total of 114 fecal samples, 133 oral cavity samples and 20 serum samples to characterize the gut microbiota, oral microbiota and serum metabolites in a cohort of 76 ID patients (IDs) and 59 well-matched healthy controls (HCs). We assessed the microbiota as potentially biomarkers for ID for ID by 16S rDNA sequencing and elucidated the interactions involving gut microbiota, oral microbiota and serum metabolites in ID in conjunction with untargeted metabolomics.

Results

Gut and oral microbiota of IDs were dysbiotic. Gut and oral microbial biomarkers could be used to differentiate IDs from HCs. Eleven significantly altered serum metabolites, including adenosine, phenol, and phenol sulfate, differed significantly between groups. In multi-omics analyses, adenosine showed a positive correlation with genus_Lachnospira (p=0.029) and total sleep time (p=0.016). Additionally, phenol and phenol sulphate had a negative correlation with genus_Coprococcus (p=0.0059; p=0.0059) and a positive correlation with Pittsburgh Sleep Quality Index (p=0.006; p=0.006) and Insomnia Severity Index (p=0.021; p=0.021).

Conclusion

Microbiota and serum metabolite changes in IDs are strongly correlated with clinical parameters, implying mechanistic links between altered bacteria, serum metabolites and ID. This study offers novel perspective into the interaction among gut microbiota, oral microbiota, and serum metabolites for ID.

A cross-omics data analysis strategy for metabolite-microbe pair identification

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids

A strong correlation between gut microbes and host health has been observed in numerous gut metagenomic cohort studies. However, the underlying mechanisms governing host-microbe interactions in the gut remain largely unknown. Here we report that the gut commensal Christensenella minuta modulates host metabolism by generating a previously undescribed class of secondary bile acids with 3-O-acylation substitution that inhibit the intestinal farnesoid X receptor. Administration of C. minuta alleviated features of metabolic disease in high fat diet-induced obese mice associated with a significant increase in these acylated bile acids, which we refer to as 3-O-acyl-cholic acids. Specific knockout of intestinal farnesoid X receptor in mice counteracted the beneficial effects observed in their wild-type counterparts. Finally, we showed that 3-O-acyl-CAs were prevalent in healthy humans but significantly depleted in patients with type 2 diabetes. Our findings indicate a role for C. minuta and acylated bile acids in metabolic diseases.

Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit

Hypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. Therefore, we develop a new model to identify AHTPs based on multiple features and deep learning. And the deep model is constructed by combining a convolutional neural network (CNN) and a gated recurrent unit (GRU). The unique convolution structure is used to reduce the feature dimension and running time. The data processed by CNN is input into the recurrent structure GRU, and important information is filtered out through the reset gate and update gate. Finally, the output layer adopts Sigmoid activation function. Firstly, we use Kmer, the deviation between the dipeptide frequency and the expected mean (DDE), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) and dipeptide binary profile and frequency (DBPF) to extract features. For Kmer, DDE, EBGW and EGAAC, it is widely used in the field of protein research. DBPF is a new feature representation method designed by us. It corresponds dipeptides to binary numbers, and finally obtains a binary coding file and a frequency file. Then these features are spliced together and input into our proposed model for prediction and analysis. After a tenfold cross-validation test, this model has a better competitive advantage than the previous methods, and the accuracy is 96.23% and 99.10%, respectively. From the results, compared with the previous methods, it has been greatly improved. It shows that the combination of convolution calculation and recurrent structure has a positive impact on the classification of AHTPs. The results show that this method is a feasible, efficient and competitive sequence analysis tool for AHTPs. Meanwhile, we design a friendly online prediction tool and it is freely accessible at http://ahtps.zhanglab.site/ .

Beta-Glucan Alters Gut Microbiota and Plasma Metabolites in Pre-Weaning Dairy Calves

The present study aims to evaluate the alterations in gut microbiome and plasma metabolites of dairy calves with β-glucan (BG) supplementation. Fourteen healthy newborn dairy calves with similar body weight were randomly divided into control (n = 7) and BG (n = 7) groups. All the calves were fed on the basal diet, while calves in the BG group were supplemented with oat BG on d 8 for 14 days. Serum markers, fecal microbiome, and plasma metabolites at d 21 were analyzed. The calves were weaned on d 60 and weighed. The mean weaning weight of the BG group was 4.29 kg heavier than that of the control group. Compared with the control group, the levels of serum globulin, albumin, and superoxide dismutase were increased in the BG group. Oat BG intake increased the gut microbiota richness and decreased the Firmicutes-to-Bacteroidetes ratio. Changes in serum markers were found to be correlated with the plasma metabolites, including sphingosine, trehalose, and 3-methoxy-4-hydroxyphenylglycol sulfate, and gut microbiota such as Ruminococcaceae_NK4A214, Alistipes, and Bacteroides. Overall, these results suggest that the BG promotes growth and health of pre-weaning dairy calves by affecting the interaction between the host and gut microbiota.