Associations between the gut microbiome and colorectal cancer (CRC) have been uncovered, but larger and more diverse studies are needed to assess their potential clinical use. We expanded upon 12 metagenomic datasets of patients with CRC (n = 930), adenomas (n = 210) and healthy control individuals (n = 976; total n = 2,116) with 6 new cohorts (n = 1,625) providing granular information on cancer stage and the anatomic location of tumors. We improved CRC prediction accuracy based solely on gut metagenomics (average area under the curve = 0.85) and highlighted the contribution of 19 newly profiled species and distinct Fusobacterium nucleatum clades. Specific gut species distinguish left-sided versus right-sided CRC (area under the curve = 0.66) with an enrichment of oral-typical microbes. We identified strain-specific CRC signatures with the commensal Ruminococcus bicirculans and Faecalibacterium prausnitzii showing subclades associated with late-stage CRC. Our analysis confirms that the microbiome can be a clinical target for CRC screening and characterizes it as a biomarker for CRC progression.

• Publication type
• Journal Article MeSH

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

• Keywords
• compositionality, data preprocessing, human microbiome, machine learning, metagenomics data, normalization,
• Publication type
• Journal Article MeSH
• Review MeSH

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

• MeSH
• Genome MeSH
• Genomics MeSH
• Precision Medicine MeSH
• Humans MeSH
• Microbiota * MeSH
• Gastrointestinal Microbiome * genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

The frequent occurrence of E. coli positive for cyclomodulins such as colibactin (CLB), the cytotoxic necrotizing factor (CNF), and the cytolethal distending factor (CDT) in colorectal cancer (CRC) patients published so far provides the opportunity to use them as CRC screening markers. We examined the practicability and performance of a low-cost detection approach that relied on culture followed by simplified DNA extraction and PCR in E. coli isolates recovered from 130 CRC patients and 111 controls. Our results showed a statistically significant association between CRC and the presence of colibactin genes clbB and clbN, the cnf gene, and newly, the hemolytic phenotype of E. coli isolates. We also observed a significant increase in the mean number of morphologically distinct E. coli isolates per patient in the CRC cohort compared to controls, indicating that the cyclomodulin-producing E. coli strains may represent potentially preventable harmful newcomers in CRC patients. A colibactin gene assay showed the highest detection rate (45.4%), and males would benefit from the screening more than females. However, because of the high number of false positives, practical use of this marker must be explored. In our opinion, it may serve as an auxiliary marker to increase the specificity and/or sensitivity of the well-established fecal immunochemical test (FIT) in CRC screening.

• Keywords
• colibactin, colorectal cancer, cytotoxic necrotizing factor, genotoxin, screening,
• Publication type
• Journal Article MeSH

Common variable immunodeficiency (CVID) is a clinically and genetically heterogeneous disorder with inadequate antibody responses and low levels of immunoglobulins including IgA that is involved in the maintenance of the intestinal homeostasis. In this study, we analyzed the taxonomical and functional metagenome of the fecal microbiota and stool metabolome in a cohort of six CVID patients without gastroenterological symptomatology and their healthy housemates. The fecal microbiome of CVID patients contained higher numbers of bacterial species and altered abundance of thirty-four species. Hungatella hathewayi was frequent in CVID microbiome and absent in controls. Moreover, the CVID metagenome was enriched for low-abundance genes likely encoding nonessential functions, such as bacterial motility and metabolism of aromatic compounds. Metabolomics revealed dysregulation in several metabolic pathways, mostly associated with decreased levels of adenosine in CVID patients. Identified features have been consistently associated with CVID diagnosis across the patients with various immunological characteristics, length of treatment, and age. Taken together, this initial study revealed expansion of bacterial diversity in the host immunodeficient conditions and suggested several bacterial species and metabolites, which have potential to be diagnostic and/or prognostic CVID markers in the future.

• Keywords
• CVID, Hungatella hathewayi, common variable immunodeficiency, metabolome, metagenome, microbiome,
• MeSH
• Adenosine metabolism   MeSH
• Common Variable Immunodeficiency genetics   microbiology   MeSH
• Biodiversity MeSH
• Clostridiaceae physiology   MeSH
• Dysbiosis genetics   microbiology   MeSH
• Feces microbiology   MeSH
• Homeostasis MeSH
• Humans MeSH
• Metabolomics MeSH
• Metagenome MeSH
• RNA, Ribosomal, 16S genetics   MeSH
• Gastrointestinal Microbiome genetics   MeSH
• Computational Biology methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Adenosine MeSH
• RNA, Ribosomal, 16S MeSH

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

• Keywords
• biomarker identification, disease prediction, feature selection, machine learning, microbiome,
• Publication type
• Journal Article MeSH
• Review MeSH

Dysbiotic configurations of the human gut microbiota have been linked to colorectal cancer (CRC). Human small noncoding RNAs are also implicated in CRC, and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis, but their role has been less extensively explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens from patients with CRC or with adenomas and from healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We observed considerable overlap and a correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. We identified a combined predictive signature composed of 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC samples separately from healthy and adenoma samples (area under the curve [AUC] = 0.87). In the present study, we report evidence that host-microbiome dysbiosis in CRC can also be observed by examination of altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more-accurate tools for diagnostic purposes.IMPORTANCE The characteristics of microbial small RNA transcription are largely unknown, while it is of primary importance for a better identification of molecules with functional activities in the gut niche under both healthy and disease conditions. By performing combined analyses of metagenomic and small RNA sequencing (sRNA-Seq) data, we characterized both the human and microbial small RNA contents of stool samples from healthy individuals and from patients with colorectal carcinoma or adenoma. With the integrative analyses of metagenomic and sRNA-Seq data, we identified a human and microbial small RNA signature which can be used to improve diagnosis of the disease. Our analysis of human and gut microbiome small RNA expression is relevant to generation of the first hypotheses about the potential molecular interactions occurring in the gut of CRC patients, and it can be the basis for further mechanistic studies and clinical tests.

• Keywords
• gut microbiome, human stool samples, microRNAs, small RNAs,
• Publication type
• Journal Article MeSH

Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.

• MeSH
• Choline metabolism   MeSH
• Databases, Genetic MeSH
• Species Specificity MeSH
• Cohort Studies MeSH
• Colorectal Neoplasms diagnosis   metabolism   microbiology   MeSH
• Humans MeSH
• Lyases genetics   metabolism   MeSH
• Metagenomics * MeSH
• Biomarkers, Tumor metabolism   MeSH
• Gastrointestinal Microbiome MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• Choline MeSH
• Lyases MeSH
• Biomarkers, Tumor MeSH