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Editorial summary: We provide a conceptual framework to establish a causal link between diet, gut microbiota, and health. Identifying the key strains that mediate microbe–host interactions and understanding the mechanisms involved and the ecology of these strains are critical to translating gut microbiome research into clinical applications and to advancing a new concept of "microbiome nutrition". [ABSTRACT FROM AUTHOR]

To demonstrate the causative role of gut microbiome in human health and diseases, we first need to identify, via next-generation sequencing, potentially important functional members associated with specific health outcomes and disease phenotypes. However, due to the strain-level genetic complexity of the gut microbiota, microbiome datasets are highly dimensional and highly sparse in nature, making it challenging to identify putative causative agents of a particular disease phenotype. Members of an ecosystem seldomly live independently from each other. Instead, they develop local interactions and form inter-member organizations to influence the ecosystem's higher-level patterns and functions. In the ecological study of macro-organisms, members are defined as belonging to the same "guild" if they exploit the same class of resources in a similar way or work together as a coherent functional group. Translating the concept of "guild" to the study of gut microbiota, we redefine guild as a group of bacteria that show consistent co-abundant behavior and likely to work together to contribute to the same ecological function. In this opinion article, we discuss how to use guilds as the aggregation unit to reduce dimensionality and sparsity in microbiome-wide association studies for identifying candidate gut bacteria that may causatively contribute to human health and diseases. [ABSTRACT FROM AUTHOR]

Objective and design: An accumulating body of evidence has shown that gut microbiota is involved in regulating inflammation; however, it remains undetermined if and how gut microbiota plays an important role in modulating deep venous thrombosis (DVT), which is an inflammation-involved thrombotic event. Subjects: Mice under different treatments were used in this study. Methods and treatment: We induced stenosis DVT in mice by partially ligating the inferior vena cava. Mice were treated with antibiotics, prebiotics, probiotics, or inflammatory reagents to modulate inflammatory states, and their effects on the levels of circulating LPS and DVT were examined. Results: Antibiotic-treated mice or germ-free mice exhibited compromised DVT. Treatment of mice with either prebiotics or probiotics effectively suppressed DVT, which was accompanied with the downregulation of circulating LPS. Restoration of circulating LPS in these mice with a low dose of LPS was able to restore DVT. LPS-induced DVT was blocked by a TLR4 antagonist. By performing proteomic analysis, we identified TSP1 as one of the downstream effectors of circulating LPS in DVT. Conclusion: These results suggest that gut microbiota may play a nonnegligible role in modulating DVT by leveraging the levels of LPS in circulation, thus shedding light on the development of gut microbiota-based strategies for preventing and treating DVT. [ABSTRACT FROM AUTHOR]

Backgrounds: The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them. Methods: Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below − 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/ and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed. Results: Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/ than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA. Conclusions: Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers. [ABSTRACT FROM AUTHOR]

Background: Calorie restriction (CR) and intermittent fasting (IF) can promote metabolic health through a process that is partially mediated by gut microbiota modulation. To compare the effects of CR and IF with different dietary structures on metabolic health and the gut microbiota, we performed an experiment in which mice were subjected to a CR or IF regimen and an additional IF control (IFCtrl) group whose total energy intake was not different from that of the CR group was included. Each regimen was included for normal chow and high-fat diet. Results: We showed that in normal-chow mice, the IFCtrl regimen had similar positive effects on glucose and lipid metabolism as the CR regimen, but the IF regimen showed almost no influence compared to the outcomes observed in the ad libitum group. IF also resulted in improvements, but the effects were less marked than those associate with CR and IFCtrl when the mice were fed a high-fat diet. Moreover, CR created a stable and unique gut microbial community, while the gut microbiota shaped by IF exhibited dynamic changes in fasting-refeeding cycles. At the end of each cycle, the gut microbiota of the IFCtrl mice was similar to that of the CR mice, and the gut microbiota of the IF mice was similar to that of the ad libitum group. When the abundance of Lactobacillus murinus OTU2 was high, the corresponding metabolic phenotype was improved regardless of eating pattern and dietary structure, which might be one of the key bacterial groups in the gut microbiota that is positively correlated with metabolic amelioration. Conclusion: There are interactions among the amount of food intake, the diet structure, and the fasting time on metabolic health. The structure and composition of gut microbiota modified by dietary regimens might contribute to the beneficial effects on the host metabolism. [ABSTRACT FROM AUTHOR]