Beyond Taxonomic Analysis of Microbiomes: A Functional Approach for Revisiting Microbiome Changes in Colorectal Cancer

Distinct Gut Microbial Enterotypes and Functional Dynamics in Wild Striped Field Mice (Apodemus agrarius) across Diverse Populations

Rodents, including the striped field mouse (Apodemus agrarius), play vital roles in ecosystem functioning, with their gut microbiota contributing significantly to various ecological processes. Here, we investigated the structure and function of 94 wild A. agrarius individuals from 7 geographic populations (45°57' N, 126°48' E; 45°87' N, 126°37' E; 45°50' N, 125°31' E; 45°59' N, 124°37' E; 46°01' N, 124°88' E; 46°01' N, 124°88' E; 46°01' N, 124°88' E), revealing two distinct enterotypes (Type1 and Type2) for the first time. Each enterotype showed unique microbial diversity, functions, and assembly processes. Firmicutes and Bacteroidetes dominated, with a significant presence of Lactobacillus and Muribaculaceae. Functional analysis highlighted metabolic differences, with Type1 emphasizing nutrient processing and Type2 showing higher energy production capacity. The analysis of the neutral model and the null model revealed a mix of stochastic (drift and homogenizing dispersal) and deterministic processes (homogenous selection) that shape the assembly of the microbiota, with subtle differences in the assembly processes between the two enterotypes. Correlation analysis showed that elevation and BMI were associated with the phylogenetic turnover of microbial communities, suggesting that variations in these factors may influence the composition and diversity of the gut microbiota in A. agrarius. Our study sheds light on gut microbial dynamics in wild A. agrarius populations, highlighting the importance of considering ecological and physiological factors in understanding host-microbiota interactions.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy

Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment. Understanding how microbiota affects individual responses is crucial for precision oncology. However, it is challenging to identify key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. Moreover, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response. DeepGeni not only improves microbiome-driven prediction of immune checkpoint inhibitor responsiveness but also suggests potential microbial targets for fecal microbiota transplant or probiotics improving the outcome of cancer immunotherapy.

A systematic review of microbiome-derived biomarkers for early colorectal cancer detection

Increasing evidence suggests a role of the gut microbiome in the development of colorectal cancer (CRC) and that it can serve as a biomarker for early diagnosis. This review aims to give an overview of the current status of published studies regarding the microbiome as a screening tool for early CRC detection. A literature search was conducted using PubMed and EMBASE in August 2022. Studies assessing the efficacy of microbiome-derived biomarkers based on noninvasive derived samples were included. Not relevant studies or studies not specifying the stage of CRC or grouping them together in the analysis were excluded. The risk of bias for screening tools was performed using the QUADAS-2 checklist. A total of 28 studies were included, ranging from 2 to 462 for CRC and 18 to 665 advanced adenoma patient inclusions, of which only two investigated the co-metabolome as biomarker. The diagnostic performance of faecal bacteria-derived biomarkers had an AUC ranging from 0.28-0.98 for precursor lesions such as advanced adenomas and 0.54-0.89 for early CRC. Diagnostic performance based on the co-metabolome showed an AUC ranging from 0.69 - 0.84 for precursor lesions and 0.65 - 0.93 for early CRC. All models improved when combined with established clinical early detection markers such as gFOBT. A high level of heterogeneity was seen in the number of inclusions and methodology used in the studies. The faecal and oral gut microbiome has the potential to complement existing CRC screening tools, however current evidence suggests that this is not yet ready for routine clinical use.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.

The Microbiome and Cancer: Creating Friendly Neighborhoods and Removing the Foes Within

The human body is colonized by the microbial cells that are estimated to be as abundant as human cells, yet their genome is roughly 100 times the human genome, providing significantly more genetic diversity. The past decade has observed an explosion of interest in examining the existence of microbiota in the human body and understanding its role in various diseases including inflammatory bowel disease, neurologic diseases, cardiovascular disorders, and cancer. Many studies have demonstrated differential community composition between normal tissue and cancerous tissue, paving the way for investigations focused on deciphering the cause-and-effect relationships between specific microbes and initiation and progression of various cancers. Also, evolving are the strategies to alter tumor-associated dysbiosis and move it toward eubiosis with holistic approaches to change the entire neighborhood or to neutralize pathogenic strains. In this review, we discuss important pathogenic bacteria and the underlying mechanisms by which they affect cancer progression. We summarize key microbiota alterations observed in multiple tumor niches, their association with clinical stages, and their potential use in cancer diagnosis and management. Finally, we discuss microbiota-based therapeutic approaches.

Avocado Consumption Alters Gastrointestinal Bacteria Abundance and Microbial Metabolite Concentrations among Adults with Overweight or Obesity: A Randomized Controlled Trial

BACKGROUND

Avocados are rich in dietary fiber and monounsaturated fatty acids (MUFAs), nutrients that have been independently connected to metabolic health benefits and the gastrointestinal microbiota.

OBJECTIVES

We aimed to evaluate the impact of avocado consumption on the gastrointestinal microbiota and microbial metabolites, secondary outcomes of the Persea americana for Total Health (PATH) study, and conduct exploratory analyses to assess relations between the fecal microbiota, fecal metabolites, and health markers.

METHODS

Adults [n = 163, 25-45 y, BMI (kg/m2) ≥ 25.0] were enrolled in the PATH study, a 12-wk investigator-blinded trial where participants were batch randomized to match the 2 groups by age, sex, visceral adiposity, and fasting glucose concentrations. Participants consumed isocaloric meals with or without avocado (175 g, men; 140 g, women) once daily for 12 wk. The fecal microbiota was assessed with 16S ribosomal RNA gene (V4 region) sequencing and analysis using DADA2 and QIIME2. Fecal fatty acid and bile acid concentrations were quantified using GC and LC-MS. Per-protocol (≥80% meal consumption) and intent-to-treat analyses were conducted using univariate ANOVA and Mann-Whitney U tests. Bivariate correlations were conducted between fecal microbiota, fecal metabolites, and health measures.

RESULTS

The avocado treatment increased ɑ diversity and enriched Faecalibacterium, Lachnospira, and Alistipes between 26% and 65% compared with the control group. The avocado group had 18% greater fecal acetate, 70% greater stearic acid, and 98% greater palmitic acid concentrations than the control group, while the concentrations of the bile acids cholic and chenodeoxycholic acid were 91% and 57% lower, respectively.

CONCLUSIONS

Daily avocado consumption resulted in lower fecal bile acid concentrations, greater fecal fatty acid and SCFAs, and greater relative abundances of bacteria capable of fiber fermentation, providing evidence that this nutrient-dense food affects digestive physiology, as well as the composition and metabolic functions of the intestinal microbiota. This trial was registered at www.clinicaltrials.gov as NCT02740439.