Exploring Gut Microbiome Composition and Circulating Microbial DNA Fragments in Patients with Stage II/III Colorectal Cancer: A Comprehensive Analysis.

BACKGROUND: Colorectal cancer (CRC) significantly contributes to cancer-related mortality, necessitating the exploration of prognostic factors beyond TNM staging. This study investigates the composition of the gut microbiome and microbial DNA fragments in stage II/III CRC. METHODS: A cohort of 142 patients with stage II/III CRC and 91 healthy controls underwent comprehensive microbiome analysis. Fecal samples were collected for 16S rRNA sequencing, and blood samples were tested for the presence of microbial DNA fragments. De novo clustering analysis categorized individuals based on their microbial profiles. Alpha and beta diversity metrics were calculated, and taxonomic profiling was conducted. RESULTS: Patients with CRC exhibited distinct microbial composition compared to controls. Beta diversity analysis confirmed CRC-specific microbial profiles. Taxonomic profiling revealed unique taxonomies in the patient cohort. De novo clustering separated individuals into distinct groups, with specific microbial DNA fragment detection associated with certain patient clusters. CONCLUSIONS: The gut microbiota can differentiate patients with CRC from healthy individuals. Detecting microbial DNA fragments in the bloodstream may be linked to CRC prognosis. These findings suggest that the gut microbiome could serve as a prognostic factor in stage II/III CRC. Identifying specific microbial markers associated with CRC prognosis has potential clinical implications, including personalized treatment strategies and reduced healthcare costs. Further research is needed to validate these findings and uncover underlying mechanisms.

Early life gut microbiota profiles linked to synbiotic formula effects: a randomized clinical trial in European infants.

BACKGROUND: Microbial colonization of the gastrointestinal tract after birth is an essential event that influences infant health with life-long consequences. Therefore, it is important to investigate strategies to positively modulate colonization in early life. OBJECTIVES: This randomized, controlled intervention study included 540 infants to investigate the effects of a synbiotic intervention formula (IF) containing Limosilactobacillus fermentum CECT5716 and galacto-oligosaccharides on the fecal microbiome. METHODS: The fecal microbiota from infants was analyzed by 16S rRNA amplicon sequencing at 4, 12, and 24 months of age. Metabolites (e.g., short-chain fatty acids) and other milieu parameters (e.g., pH, humidity, and IgA) were also measured in stool samples. RESULTS: Microbiota profiles changed with age, with major differences in diversity and composition. Significant effects of the synbiotic IF compared with control formula (CF) were visible at month 4, including higher occurrence of Bifidobacterium spp. and Lactobacillaceae and lower occurrence of Blautia spp., as well as Ruminoccocus gnavus and relatives. This was accompanied by lower fecal pH and concentrations of butyrate. After de novo clustering at 4 months of age, overall phylogenetic profiles of the infants receiving IF were closer to reference profiles of those fed with human milk than infants fed CF. The changes owing to IF were associated with fecal microbiota states characterized by lower occurrence of Bacteroides compared with higher levels of Firmicutes (valid name Bacillota), Proteobacteria (valid name Pseudomonadota), and Bifidobacterium at 4 months of age. These microbiota states were linked to higher prevalence of infants born by Cesarean section. CONCLUSIONS: The synbiotic intervention influenced fecal microbiota and milieu parameters at an early age depending on the overall microbiota profiles of the infants, sharing a few similarities with breastfed infants. This trial was registered at clinicaltrials.gov as NCT02221687.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time.

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos' inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples' fate under various effects. We applied Cronos to available data from growing infants' gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.

DivCom: A Tool for Systematic Partition of Groups of Microbial Profiles Into Intrinsic Subclusters and Distance-Based Subgroup Comparisons.

When analyzing microbiome data, one of the main objectives is to effectively compare the microbial profiles of samples belonging to different groups. Beta diversity measures the level of similarity among samples, usually in the form of dissimilarity matrices. The use of suitable statistical tests in conjunction with those matrices typically provides us with all the necessary information to evaluate the overall similarity of groups of microbial communities. However, in some cases, this approach can lead us to deceptive conclusions, mainly due to the uneven dispersions of the groups and the existence of unique or unexpected substructures in the dataset. To address these issues, we developed divide and compare (DivCom), an automated tool for advanced beta diversity analysis. DivCom reveals the inner structure of groups by dividing their samples into the appropriate number of clusters and then compares the distances of every profile to the centers of these clusters. This information can be used for determining the existing interrelation of the groups. The proposed methodology and the developed tool were assessed by comparing the response of anemic patients with or without inflammatory bowel disease to different iron replacement therapies. DivCom generated results that revealed the inner structure of the dataset, evaluated the relationship among the clusters, and assessed the effect of the treatments. The DivCom tool is freely available at: https://github.com/Lagkouvardos/DivCom.