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.

BACKGROUND: Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.
METHODS: A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman\'s rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model.
RESULTS: In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone.
CONCLUSIONS: T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Based on the widespread application and under-research of mechanized preparation Cantonese soy sauce koji (MP), absolute quantitative approaches were utilized to systematically analyze the flavor formation mechanism in MP. The results indicated that the enzyme activities increased greatly during MP fermentation, and 4 organic acids, 15 amino acids, and 2 volatiles were identified as significantly different flavor actives. The flavor parameters of MP4 were basically identical to those of MP5. Furthermore, microorganisms were dominated by Staphylococcus, Weissella, and Aspergillus in MP, and their biomass demonstrated an increasing trend. A precise enumeration of microorganisms exposed the inaccuracy of relative quantitative data. Concurrently, Staphylococcus and Aspergillus were positively correlated with numerous enzymes and flavor compounds, and targeted strains for enhancing MP quality. The flavor formation network comprises pathways including carbohydrate metabolism, lipid metabolism and oxidation, and protein degradation and amino acid metabolism. In summary, the fermentation period of MP can be substantially shortened without compromising the product quality. These findings lay the groundwork for refining parameters in modern production processes.

BACKGROUND: Inflammatory Bowel Disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract with an unknown etiology. Although dysbiosis is implicated in its pathogenesis, deep sequencing and oral microbiota study in Chinese IBD patients is absent.
OBJECTIVE: To explore the role of oral / intestinal microbiota in patients with IBD and the potential associations therein.
METHODS: Clinical data, fecal and saliva samples were harvested from 80 patients with IBD (Crohn\'s disease, CD, n = 69; Ulcerative colitis, UC, n = 11) and 24 normal controls. Microbiomics (16S rRNA sequencing and 16S rRNA full-length sequencing) were used to detect and analyze the difference between IBD patients and normal control.
RESULTS: Compared with normal controls, a higher abundance of the intestinal Shigella spp. (Shigella flexneri and Shigella sonnei, which were positively relate to the severity of IBD), lower abundance of intestinal probiotics (Prevotella, Faecalibacterium and Roseburia), and higher abundance of oral Neisseria were present in IBD patients with microbiome. The higher inflammation-related markers, impaired hepatic and renal function, and dyslipidaemia were present in patients with IBD. A higher intake of red meat and increased abundance of Clostridium in the gut were found in CD patients, while the elevated abundance of Ruminococcus in the gut was showed in UC ones. The bacterial composition of saliva and fecal samples was completely different, yet there was some correlation in the distribution of dominant probiotics.
CONCLUSIONS: Enteric dysbacteriosis and the infections of pathogenic bacteria (Shigella) may associate with the occurrence or development of IBD.

BACKGROUND: The fruit of Tribulus terrestris L. (TT) is extensively documented in the Tibetan medical literature \'Si Bu Yi Dian\', has been used to treat diabetes mellitus for more than a thousand years. However, the underlying mechanisms and comprehensive effects of TT on diabetes have yet to be investigated.
OBJECTIVE: The aim of the study was to systemically elucidate the potential mechanisms of TT in treating diabetes mellitus, and further investigate the therapeutic effects of the water extract, small molecular components and saccharides from TT.
METHODS: Fecal metabolomics was employed to draw the metabolic profile based on UHPLC-Q-TOF-MS/MS. The V3-V4 hypervariable regions of the bacteria 16S rRNA gene were amplified to explore the structural changes of the intestinal microbiome after TT intervention and to analyze the differential microbiota. The microbial metabolites SCFAs were determined by GC-MS, and the BAs and tryptophan metabolites were quantified by UPLC-TQ-MS. Spearman correlation analysis was carried out to comprehensively investigate the relationship among the endogenous metabolites profile, intestinal microbiota and their metabolites.
RESULTS: TT exhibited remarkably therapeutic effect on T2DM rats, as evidenced by improved glucolipid metabolism and intestinal barrier integrity, ameliorated inflammation and remission in insulin resistance. A total of 24 endogenous biomarkers were screened through fecal metabolomics studies, which were mainly related to tryptophan metabolism, fatty acid metabolism, bile acid metabolism, steroid hormone biosynthesis and arachidonic acid metabolism. Investigations on microbiomics revealed that TT significantly modulated 18 differential bacterial genera and reversed the disordered gut microbial in diabetes rats. Moreover, TT notably altered the content of gut microbiota metabolites, both in serum and fecal samples. Significant correlation among microbial community, metabolites and T2DM-related indicators was revealed.
CONCLUSIONS: The multiple components of TT regulate the metabolic homeostasis of the organism and the balance of intestinal microbiota and its metabolites, which might mediate the anti-diabetic capacity of TT.

With the widespread use of controlled-release nanopesticides in field conditions, the interactions between these nanopesticides and biological systems are complex and highly uncertain. The toxicity of iron-based metal organic frameworks (CF@MIL-101-SL) loaded with chlorfenapyr (CF) to terrestrial invertebrate earthworms in filter paper and soil environments and the potential mechanisms of interactions in the nanopesticide-earthworm-cornfield soil microorganism system were investigated for the first time. The results showed that CF@MIL-101-SL was more poisonous to earthworms in the contact filter paper test than suspension concentrate of CF (CF-SC), and conversely, CF@MIL-101-SL was less poisonous to earthworms in the soil test. In the soil environment, the CF@MIL-101-SL treatment reduced oxidative stress and the inhibition of detoxifying enzymes, and reduced tissue and cellular substructural damage in earthworms compared to the CF-SC treatment. Long-term treatment with CF@MIL-101-SL altered the composition and abundance of microbial communities with degradative functions in the earthworm intestine and soil and affected the soil nitrogen cycle by modulating the composition and abundance of nitrifying and denitrifying bacterial communities in the earthworm intestine and soil, confirming that soil microorganisms play an important role in reducing the toxicity of CF@MIL-101-SL to earthworms. In conclusion, this study provides new insights into the ecological risks of nanopesticides to soil organisms.

Despite their enormous impact on the environment and humans, the distribution and variety of the biggest natural secondary metabolite producers, the genus Streptomyces, have not been adequately investigated. We developed representative maps from public EMP 16S rRNA amplicon sequences microbiomics data. Streptomyces ASVs were extracted from the EMP overall bacterial community, demonstrating Streptomyces diversity and identifying crucial diversity patterns. Our findings revealed that while the EMP primarily distinguished bacterial communities as host-associated or free-living (EMPO level 1), the Streptomyces community showed no significant difference but exhibited distinctions between categories in EMPO level 2 (animal, plant, non-saline, and saline). Multiple linear regression analysis demonstrated that pH, temperature, and salinity significantly predicted Streptomyces richness, with richness decreasing as these factors increased. However, latitude and longitude do not predict Streptomyces richness. Our Streptomyces maps revealed that additional samplings in Africa and Southeast Asia are needed. Additionally, our findings indicated that a greater number of samples did not always result in greater Streptomyces richness; future surveys may not necessitate extensive sampling from a single location. Broader sampling, rather than local/regional sampling, may be more critical in answering microbial biogeograph questions. Lastly, using 16S rRNA gene sequencing data has some limitations, which should be interpreted cautiously.

Microbial community succession in raw milk determines its quality and storage period. In this study, carbon dioxide (CO2) at 2000 ppm was used to treat raw milk to investigate the mechanism of extending the shelf life of raw milk by CO2 treatment from the viewpoint of microbial colonies and metabolites. The results showed that the shelf life of CO2-treated raw milk was extended to 16 days at 4 °C, while that of the control raw milk was only 6 days. Microbiomics analysis identified 221 amplicon sequence variants (ASVs) in raw milk, and the alpha diversity of microbial communities increased (p < 0.05) with the extension of storage time. Among them, Pseudomonas, Actinobacteria and Serratia were the major microbial genera responsible for the deterioration of raw milk, with a percentage of 85.7%. A combined metagenomics and metabolomics analysis revealed that microorganisms altered the levels of metabolites, such as pyruvic acid, glutamic acid, 5\'-cmp, arginine, 2-propenoic acid and phenylalanine, in the raw milk through metabolic activities, such as ABC transporters, pyrimidine metabolism, arginine and proline metabolism and phenylalanine metabolism, and reduced the shelf life of raw milk. CO2 treatment prolonged the shelf life of raw milk by inhibiting the growth of Gram-negative aerobic bacteria, such as Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae.

Necrotizing enterocolitis (NEC) represents the most common and lethal acute gastrointestinal emergency of newborns, mainly affecting those born prematurely. It can lead to severe long-term sequelae and the mortality rate is approximately 25%. Furthermore, the diagnosis is difficult, especially in the early stages, due to multifactorial pathogenesis and complex clinical pictures with mild and non-specific symptoms. In addition, the existing tests have poor diagnostic value. Thus, the scientific community has been focusing its attention on the identification of non-invasive biomarkers capable of prediction, early diagnosis and discriminating NEC from other intestinal diseases in order to intervene early and block the progression of the pathology. In this regard, the use of \"omics\" technologies, especially metabolomics and microbiomics, could be a fundamental synergistic strategy to study the pathophysiology of NEC. In addition, a deeper knowledge of the microbiota-host cross-talk can clarify the metabolic pathways potentially involved in the pathology, allowing for the identification of specific biomarkers. In this article, the authors analyze the state-of-the-art concerning the application of metabolomics and microbiota analysis to investigate this pathology and discuss the future possibility of the metabolomic fingerprint of patients for diagnostic purposes.

The integrated analysis of host metabolome and intestinal microbiome is an opportunity to explore the complex therapeutic mechanisms of traditional Chinese medicines. Currently, researchers mainly employ various statistical correlation analytical methods to investigate metabolome-microbiome correlations. However, these conventional correlation techniques often focus on statistical correlations and their biological meanings are always ignored, especially the functional relevance between them. Here, we developed a novel enzyme-based functional correlation (EBFC) algorithm to further improve the interpretability and the identified scope of microbe-metabolite correlations based on the conventional Spearman\'s analysis. The proposed EBFC algorithm is successfully utilized to reveal the therapeutic mechanisms of Jian-Pi-Yi-Shen (JPYS) formula on the treatment of adenine-induced chronic kidney disease (CKD) rats. JPYS, a TCM formula for treating CKD, has beneficial clinical effects. We tentatively revealed the potential mechanism of JPYS for treating CKD rats from the perspective of the serum metabolome, gut microbiome, and their interactions. Specifically, 11 metabolites and 19 bacterial genera in the CKD rats were significantly regulated to approaching normal status after JPYS treatment, suggesting that JPYS could ameliorate the pathological symptoms of CKD rats by reshaping the disturbed metabolome and gut microbiota. Further correlation analysis between the significantly perturbed metabolites, microbiota, and the related enzymes provided more strong evidence for the study of host metabolism-microbiota interactions and the therapeutic mechanism of JPYS on CKD rats. In conclusion, these findings will help us to deeply understand the pathogenesis of CKD and provide new insights into the therapeutic mechanism of JPYS.