Potential of fecal microbiota for early-stage detection of colorectal cancer

Bioenergetic mapping of 'healthy microbiomes' via compound processing potential imprinted in gut and soil metagenomes

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of 'healthy microbiomes' remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, our objective was to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of in silico potential for human gut and environmental soil metagenomes to process a panel of major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping 'landscape' based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of 'compound processing potential' associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92 % of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role of microbiota in environmental- and human-health.

Fusobacterium species are distinctly associated with patients with Lynch syndrome colorectal cancer

Accumulating evidence demonstrates clear correlation between the gut microbiota and sporadic colorectal cancer (CRC). Despite this, there is limited understanding of the association between the gut microbiota and CRC in Lynch Syndrome (LS), a hereditary type of CRC. Here, we analyzed fecal shotgun metagenomic and targeted metabolomic of 71 Japanese LS subjects. A previously published Japanese sporadic CRC cohort, which includes non-LS controls, was utilized as a non-LS cohort (n = 437). LS subjects exhibited reduced microbial diversity and low-Faecalibacterium enterotypes compared to non-LS. Patients with LS-CRC had higher levels of Fusobacterium nucleatum and fap2. Differential fecal metabolites and functional genes suggest heightened degradation of lysine and arginine in LS-CRC. A comparison between LS and non-LS subjects prior to adenoma formation revealed distinct fecal metabolites of LS subjects. These findings suggest that the gut microbiota plays a more responsive role in CRC tumorigenesis in patients with LS than those without LS.

Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

Detection of colorectal-cancer-associated bacterial taxa in fecal samples using next-generation sequencing and 19 newly established qPCR assays

We have previously identified increased levels of distinct bacterial taxa within mucosal biopsies from colorectal cancer (CRC) patients. Following prior research, the aim of this study was to investigate the detection of the same CRC-associated bacteria in fecal samples and to evaluate the suitability of fecal samples as a non-invasive material for the detection of CRC-associated bacteria. Next-generation sequencing (NGS) of the 16S ribosomal RNA (rRNA) V4 region was performed to evaluate the detection of the CRC-associated bacteria in the fecal microbiota of cancer patients, patients with adenomatous polyp and healthy controls. Furthermore, 19 novel species-specific quantitative PCR (qPCR) assays were established to detect the CRC-associated bacteria. Approximately, 75% of the bacterial taxa identified in biopsies were reflected in fecal samples. NGS failed to detect low-abundance CRC-associated taxa in fecal samples, whereas qPCR exhibited high sensitivity and specificity in identifying all targeted taxa. Comparison of fecal microbial composition between the different patient groups showed enrichment of Fusobacterium nucleatum, Parvimonas micra, and Gemella morbillorum in cancer patients. Our findings suggest that low-abundance mucosa-associated bacteria can be detected in fecal samples using sensitive qPCR assays.

DeepPIG: deep neural network architecture with pairwise connected layers and stochastic gates using knockoff frameworks for feature selection

Selecting relevant feature subsets is essential for machine learning applications. Among the feature selection techniques, the knockoff filter procedure proposes a unique framework that minimizes false discovery rates (FDR). However, employing a deep neural network architecture for a knockoff filter framework requires higher detection power. Using the knockoff filter framework, we present a Deep neural network with PaIrwise connected layers integrated with stochastic Gates (DeepPIG) for the feature selection model. DeepPIG exhibited better detection power in synthetic data than the baseline and recent models such as Deep feature selection using Paired-Input Nonlinear Knockoffs (DeepPINK), Stochastic Gates (STG), and SHapley Additive exPlanations (SHAP) while not violating the preselected FDR level, especially when the signal of the features were weak. The selected features determined by DeepPIG demonstrated superior classification performance compared with the baseline model in real-world data analyses, including the prediction of certain cancer prognosis and classification tasks using microbiome and single-cell datasets. In conclusion, DeepPIG is a robust feature selection approach even when the signals of features are weak. Source code is available at https://github.com/DMCB-GIST/DeepPIG .

Interactions between diet and gut microbiota in cancer

Dietary patterns and specific dietary components, in concert with the gut microbiota, can jointly shape susceptibility, resistance and therapeutic response to cancer. Which diet-microbial interactions contribute to or mitigate carcinogenesis and how they work are important questions in this growing field. Here we interpret studies of diet-microbial interactions to assess dietary determinants of intestinal colonization by opportunistic and oncogenic bacteria. We explore how diet-induced expansion of specific gut bacteria might drive colonic epithelial tumorigenesis or create immuno-permissive tumour milieus and introduce recent findings that provide insight into these processes. Additionally, we describe available preclinical models that are widely used to study diet, microbiome and cancer interactions. Given the rising clinical interest in dietary modulations in cancer treatment, we highlight promising clinical trials that describe the effects of different dietary alterations on the microbiome and cancer outcomes.

Enhanced microbiota profiling in patients with quiescent Crohn's disease through comparison with paired healthy first-degree relatives

Prior studies indicate no correlation between the gut microbes of healthy first-degree relatives (HFDRs) of patients with Crohn's disease (CD) and the development of CD. Here, we utilize HFDRs as controls to examine the microbiota and metabolome in individuals with active (CD-A) and quiescent (CD-R) CD, thereby minimizing the influence of genetic and environmental factors. When compared to non-relative controls, the use of HFDR controls identifies fewer differential taxa. Faecalibacterium, Dorea, and Fusicatenibacter are decreased in CD-R, independent of inflammation, and correlated with fecal short-chain fatty acids (SCFAs). Validation with a large multi-center cohort confirms decreased Faecalibacterium and other SCFA-producing genera in CD-R. Classification models based on these genera distinguish CD from healthy individuals and demonstrate superior diagnostic power than models constructed with markers identified using unrelated controls. Furthermore, these markers exhibited limited discriminatory capabilities for other diseases. Finally, our results are validated across multiple cohorts, underscoring their robustness and potential for diagnostic and therapeutic applications.

Chronic PM2.5 exposure disrupts intestinal barrier integrity via microbial dysbiosis-triggered TLR2/5-MyD88-NLRP3 inflammasome activation

BACKGROUND

PM2.5, a known public health risk, is increasingly linked to intestinal disorders, however, the mechanisms of its impact are not fully understood.

PURPOSE

This study aimed to explore the impact of chronic PM2.5 exposure on intestinal barrier integrity and to uncover the underlying molecular mechanisms.

METHODS

C57BL/6 J mice were exposed to either concentrated ambient PM2.5 (CPM) or filtered air (FA) for six months to simulate urban pollution conditions. We evaluated intestinal barrier damage, microbial shifts, and metabolic changes through histopathology, metagenomics, and metabolomics. Analysis of the TLR signaling pathway was also conducted.

RESULTS

The mean concentration of PM2.5 in the CPM exposure chamber was consistently measured at 70.9 ± 26.8 μg/m³ throughout the study period. Our findings show that chronic CPM exposure significantly compromises intestinal barrier integrity, as indicated by reduced expression of the key tight junction proteins Occludin and Tjp1/Zo-1. Metagenomic sequencing revealed significant shifts in the microbial landscape, identifying 35 differentially abundant species. Notably, there was an increase in pro-inflammatory nongastric Helicobacter species and a decrease in beneficial bacteria, such as Lactobacillus intestinalis, Lactobacillus sp. ASF360, and Eubacterium rectale. Metabolomic analysis further identified 26 significantly altered metabolites commonly associated with intestinal diseases. A strong correlation between altered bacterial species and metabolites was also observed. For example, 4 Helicobacter species all showed positive correlations with 13 metabolites, including Lactate, Bile acids, Pyruvate and Glutamate. Additionally, increased expression levels of TLR2, TLR5, Myd88, and NLRP3 proteins were noted, and their expression patterns showed a strong correlation, suggesting a possible involvement of the TLR2/5-MyD88-NLRP3 signaling pathway.

CONCLUSIONS

Chronic CPM exposure induces intestinal barrier dysfunction, microbial dysbiosis, metabolic imbalance, and activation of the TLR2/5-MyD88-NLRP3 inflammasome. These findings highlight the urgent need for intervention strategies to mitigate the detrimental effects of air pollution on intestinal health and identify potential therapeutic targets.

Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome

Horizontal gene transfer (HGT) phenomena pervade the gut microbiome and significantly impact human health. Yet, no current method can accurately identify complete HGT events, including the transferred sequence and the associated deletion and insertion breakpoints from shotgun metagenomic data. Here, we develop LocalHGT, which facilitates the reliable and swift detection of complete HGT events from shotgun metagenomic data, delivering an accuracy of 99.4%-verified by Nanopore data-across 200 gut microbiome samples, and achieving an average F1 score of 0.99 on 100 simulated data. LocalHGT enables a systematic characterization of HGT events within the human gut microbiome across 2098 samples, revealing that multiple recipient genome sites can become targets of a transferred sequence, microhomology is enriched in HGT breakpoint junctions (P-value = 3.3e-58), and HGTs can function as host-specific fingerprints indicated by the significantly higher HGT similarity of intra-personal temporal samples than inter-personal samples (P-value = 4.3e-303). Crucially, HGTs showed potential contributions to colorectal cancer (CRC) and acute diarrhoea, as evidenced by the enrichment of the butyrate metabolism pathway (P-value = 3.8e-17) and the shigellosis pathway (P-value = 5.9e-13) in the respective associated HGTs. Furthermore, differential HGTs demonstrated promise as biomarkers for predicting various diseases. Integrating HGTs into a CRC prediction model achieved an AUC of 0.87.

A genomic compendium of cultivated human gut fungi characterizes the gut mycobiome and its relevance to common diseases

The gut fungal community represents an essential element of human health, yet its functional and metabolic potential remains insufficiently elucidated, largely due to the limited availability of reference genomes. To address this gap, we presented the cultivated gut fungi (CGF) catalog, encompassing 760 fungal genomes derived from the feces of healthy individuals. This catalog comprises 206 species spanning 48 families, including 69 species previously unidentified. We explored the functional and metabolic attributes of the CGF species and utilized this catalog to construct a phylogenetic representation of the gut mycobiome by analyzing over 11,000 fecal metagenomes from Chinese and non-Chinese populations. Moreover, we identified significant common disease-related variations in gut mycobiome composition and corroborated the associations between fungal signatures and inflammatory bowel disease (IBD) through animal experimentation. These resources and findings substantially enrich our understanding of the biological diversity and disease relevance of the human gut mycobiome.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

Colorectal cancer microbiome programs DNA methylation of host cells by affecting methyl donor metabolism

BACKGROUND

Colorectal cancer (CRC) arises from complex interactions between host and environment, which include the gut and tissue microbiome. It is hypothesized that epigenetic regulation by gut microbiota is a fundamental interface by which commensal microbes dynamically influence intestinal biology. The aim of this study is to explore the interplay between gut and tissue microbiota and host DNA methylation in CRC.

METHODS

Metagenomic sequencing of fecal samples was performed on matched CRC patients (n = 18) and healthy controls (n = 18). Additionally, tissue microbiome was profiled with 16S rRNA gene sequencing on tumor (n = 24) and tumor-adjacent normal (n = 24) tissues of CRC patients, while host DNA methylation was assessed through whole-genome bisulfite sequencing (WGBS) in a subset of 13 individuals.

RESULTS

Our analysis revealed substantial alterations in the DNA methylome of CRC tissues compared to adjacent normal tissues. An extensive meta-analysis, incorporating publicly available and in-house data, identified significant shifts in microbial-derived methyl donor-related pathways between tumor and adjacent normal tissues. Of note, we observed a pronounced enrichment of microbial-associated CpGs within the promoter regions of genes in adjacent normal tissues, a phenomenon notably absent in tumor tissues. Furthermore, we established consistent and recurring associations between methylation patterns of tumor-related genes and specific bacterial taxa.

CONCLUSIONS

This study emphasizes the pivotal role of the gut microbiota and pathogenic bacteria in dynamically shaping DNA methylation patterns, impacting physiological homeostasis, and contributing to CRC tumorigenesis. These findings provide valuable insights into the intricate host-environment interactions in CRC development and offer potential avenues for therapeutic interventions in this disease.

Oropharyngeal microbial ecosystem perturbations influence the risk for acute respiratory infections in common variable immunodeficiency

Background

The respiratory tract microbiome is essential for human health and well-being and is determined by genetic, lifestyle, and environmental factors. Patients with Common Variable Immunodeficiency (CVID) suffer from respiratory and intestinal tract infections, leading to chronic diseases and increased mortality rates. While CVID patients' gut microbiota have been analyzed, data on the respiratory microbiome ecosystem are limited.

Objective

This study aims to analyze the bacterial composition of the oropharynx of adults with CVID and its link with clinical and immunological features and risk for respiratory acute infections.

Methods

Oropharyngeal samples from 72 CVID adults and 26 controls were collected in a 12-month prospective study. The samples were analyzed by metagenomic bacterial 16S ribosomal RNA sequencing and processed using the Quantitative Insights Into Microbial Ecology (QIME) pipeline. Differentially abundant species were identified and used to build a dysbiosis index. A machine learning model trained on microbial abundance data was used to test the power of microbiome alterations to distinguish between healthy individuals and CVID patients.

Results

Compared to controls, the oropharyngeal microbiome of CVID patients showed lower alpha- and beta-diversity, with a relatively increased abundance of the order Lactobacillales, including the family Streptococcaceae. Intra-CVID analysis identified age >45 years, COPD, lack of IgA, and low residual IgM as associated with a reduced alpha diversity. Expansion of Haemophilus and Streptococcus genera was observed in patients with undetectable IgA and COPD, independent from recent antibiotic use. Patients receiving azithromycin as antibiotic prophylaxis had a higher dysbiosis score. Expansion of Haemophilus and Anoxybacillus was associated with acute respiratory infections within six months.

Conclusions

CVID patients showed a perturbed oropharynx microbiota enriched with potentially pathogenic bacteria and decreased protective species. Low residual levels of IgA/IgM, chronic lung damage, anti antibiotic prophylaxis contributed to respiratory dysbiosis.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

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.

Multi-class boosting for the analysis of multiple incomplete views on microbiome data

BACKGROUND

Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views.

RESULTS

We propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease.

CONCLUSIONS

The proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water

Antimicrobial resistance (AMR) poses a significant global health threat as the silent pandemic. Because of the use of antimicrobials in aquaculture systems, fish farms may be potential reservoirs for the dissemination of antimicrobial resistance genes (ARGs). Treatments with disinfectants have been promoted to reduce the use of antibiotics; however, the effect of these types of treatments on AMR or ARGs is not well known. This study aimed to evaluate the effects of low dose ozone treatments (0.15 mg/L) on ARG dynamics in pond water using metagenomic shotgun sequencing analysis. The results suggested that ozone disinfection can increase the relative abundance of acquired ARGs and intrinsic efflux mediated ARGs found in the resistance nodulation cell division (RND) family. Notably, a co-occurrence of efflux and non-efflux ARGs within the same bacterial genera was also observed, with most of these genera dominating the bacterial population following ozone treatments. These findings suggest that ozone treatments may selectively favor the survival of bacterial genera harboring efflux ARGs, which may also have non-efflux ARGs. This study underscores the importance of considering the potential impacts of disinfection practices on AMR gene dissemination particularly in aquaculture settings where disinfectants are frequently used at low levels. Future endeavors should prioritize the evaluation of these strategies, as they may be associated with an increased risk of AMR in aquatic environments.

Fusobacteria alterations are associated with colorectal cancer liver metastasis and a poor prognosis

Liver metastasis is a major cause of mortality in patients with advanced stages of colorectal cancer (CRC). The gut microbiota has been demonstrated to influence the progression of liver diseases, potentially providing novel perspectives for diagnosis, treatment and research. However, the gut microbial characteristics in CRC with liver metastasis (LM) and with no liver metastasis (NLM) have not yet been fully established. In the present study, high-throughput 16S RNA sequencing technology was employed, in order to examine the gut microbial richness and composition in patients with CRC with LM or NLM. A discovery cohort (cohort 2; LM=18; NLM=36) and a validation cohort (cohort 3; LM=13; NLM=41) were established using fresh feces. In addition, primary carcinoma tissue samples were also analyzed (LM=8 and NLM=10) as a supplementary discovery cohort (cohort 1). The findings of the present study indicated that the intestinal microbiota richness and diversity were increased in the LM group as compared to the NLM group. A significant difference was observed in species composition between the LM and NLM group. In the two discovery cohorts with two different samples, the dominant phyla were consistent, but varied at lower taxonomic levels. Phylum Fusobacteria presented consistent and significant enrichment in LM group in both discovery cohorts. Furthermore, with the application of a random forest model and receiver operator characteristic curve analysis, Fusobacteria was identified as a potential biomarker for LM. Moreover, Fusobacteria was also a poor prognosis factor for survival. Importantly, the findings were reconfirmed in the validation cohort. On the whole, the findings of the present study demonstrated that CRC with LM and NLM exhibit distinct gut microbiota characteristics. Fusobacteria detection thus has potential for use in predicting LM and a poor prognosis of patients with CRC.

The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis

The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Utilization of the microbiome in personalized medicine

Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

Gut microbiota and their derivatives in the progression of colorectal cancer: Mechanisms of action, genome and epigenome contributions

Gut microbiota interacts with host epithelial cells and regulates many physiological functions such as genetics, epigenetics, metabolism of nutrients, and immune functions. Dietary factors may also be involved in the etiology of colorectal cancer (CRC), especially when an unhealthy diet is consumed with excess calorie intake and bad practices like smoking or consuming a great deal of alcohol. Bacteria including Fusobacterium nucleatum, Enterotoxigenic Bacteroides fragilis (ETBF), and Escherichia coli (E. coli) actively participate in the carcinogenesis of CRC. Gastrointestinal tract with chronic inflammation and immunocompromised patients are at high risk for CRC progression. Further, the gut microbiota is also involved in Geno-toxicity by producing toxins like colibactin and cytolethal distending toxin (CDT) which cause damage to double-stranded DNA. Specific microRNAs can act as either tumor suppressors or oncogenes depending on the cellular environment in which they are expressed. The current review mainly highlights the role of gut microbiota in CRC, the mechanisms of several factors in carcinogenesis, and the role of particular microbes in colorectal neoplasia.

Characterization of tongue coating microbiome from patients with colorectal cancer

Background

Tongue coating microbiota has aroused particular interest in profiling oral and digestive system cancers. However, little is known on the relationship between tongue coating microbiome and colorectal cancer (CRC).

Methods

Metagenomic shotgun sequencing was performed on tongue coating samples collected from 30 patients with CRC, 30 patients with colorectal polyps (CP), and 30 healthy controls (HC). We further validated the potential of the tongue coating microbiota to predict the CRC by a random forest model.

Results

We found a greater species diversity in CRC samples, and the nucleoside and nucleotide biosynthesis pathway was more apparent in the CRC group. Importantly, various species across participants jointly shaped three distinguishable fur types.The tongue coating microbiome profiling data gave an area under the receiver operating characteristic curve (AUC) of 0.915 in discriminating CRC patients from control participants; species such as Atopobium rimae, Streptococcus sanguinis, and Prevotella oris aided differentiation of CRC patients from healthy participants.

Conclusion

These results elucidate the use of tongue coating microbiome in CRC patients firstly, and the fur-types observed contribute to a better understanding of the microbial community in human. Furthermore, the tongue coating microbiota-based biomarkers provide a valuable reference for CRC prediction and diagnosis.

Deciphering the Impact of Defecation Frequency on Gut Microbiome Composition and Diversity

This study explores the impact of defecation frequency on the gut microbiome structure by analyzing fecal samples from individuals categorized by defecation frequency: infrequent (1-3 times/week, n = 4), mid-frequent (4-6 times/week, n = 7), and frequent (daily, n = 9). Utilizing 16S rRNA gene-based sequencing and LC-MS/MS metabolome profiling, significant differences in microbial diversity and community structures among the groups were observed. The infrequent group showed higher microbial diversity, with community structures significantly varying with defecation frequency, a pattern consistent across all sampling time points. The Ruminococcus genus was predominant in the infrequent group, but decreased with more frequent defecation, while the Bacteroides genus was more common in the frequent group, decreasing as defecation frequency lessened. The infrequent group demonstrated enriched biosynthesis genes for aromatic amino acids and branched-chain amino acids (BCAAs), in contrast to the frequent group, which had a higher prevalence of genes for BCAA catabolism. Metabolome analysis revealed higher levels of metabolites derived from aromatic amino acids and BCAA metabolism in the infrequent group, and lower levels of BCAA-derived metabolites in the frequent group, consistent with their predicted metagenomic functions. These findings underscore the importance of considering stool consistency/frequency in understanding the factors influencing the gut microbiome.

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

Gut Bacteria Provide Genetic and Molecular Reporter Systems to Identify Specific Diseases

With genetic information gained from next-generation sequencing (NGS) and genome-wide association studies (GWAS), it is now possible to select for genes that encode reporter molecules that may be used to detect abnormalities such as alcohol-related liver disease (ARLD), cancer, cognitive impairment, multiple sclerosis (MS), diabesity, and ischemic stroke (IS). This, however, requires a thorough understanding of the gut-brain axis (GBA), the effect diets have on the selection of gut microbiota, conditions that influence the expression of microbial genes, and human physiology. Bacterial metabolites such as short-chain fatty acids (SCFAs) play a major role in gut homeostasis, maintain intestinal epithelial cells (IECs), and regulate the immune system, neurological, and endocrine functions. Changes in butyrate levels may serve as an early warning of colon cancer. Other cancer-reporting molecules are colibactin, a genotoxin produced by polyketide synthetase-positive Escherichia coli strains, and spermine oxidase (SMO). Increased butyrate levels are also associated with inflammation and impaired cognition. Dysbiosis may lead to increased production of oxidized low-density lipoproteins (OX-LDLs), known to restrict blood vessels and cause hypertension. Sudden changes in SCFA levels may also serve as a warning of IS. Early signs of ARLD may be detected by an increase in regenerating islet-derived 3 gamma (REG3G), which is associated with changes in the secretion of mucin-2 (Muc2). Pro-inflammatory molecules such as cytokines, interferons, and TNF may serve as early reporters of MS. Other examples of microbial enzymes and metabolites that may be used as reporters in the early detection of life-threatening diseases are reviewed.

Metagenomic data of microbiota in mangrove soil from Lukut River, Malaysia

The mangrove ecosystem contains sediment microorganisms that play a crucial part in the decomposition of organic matter and the cycling of water and nutrients in the mangrove. Here we present the metagenomics whole genome shotgun (mWGS) sequence data analysis from three soil samples that were collected at the freshwater riverine mangrove at Lukut River, Negeri Sembilan, Malaysia. Data analysis shows different distributions of bacteria of the genera Bradyrhizobium, Methyloceanibacter and Desulfobacteaceae were detected in soil samples collected at freshwater riverine mangrove. In the data analysis, we report the existence of a large number of Carbohydrate-Active genes in metagenomes collected from mangrove soil. An in-depth exploration of functional annotation analysis based on the KEGG database also showed that the most abundant genes found in these three soils are those that function in carbon fixation pathways, followed by methane, nitrogen, sulfur metabolisms, atrazine and dioxin degradations.

Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Metagenomic insight into the pathogenic-related characteristics and resistome profiles within microbiome residing on the Angkor sandstone monuments in Cambodia

To reveal the characteristics of indigenous microbiome including the pathogenic-related ones on Angkor monuments in Cambodia and the distribution pattern of resistome at different locations, several sites, namely Angkor Wat, Bayon of Angkor Thom, and Prasat Preah Vihear with different exposure levels to tourists were selected to conduct the metagenomic analysis in this study. The general characteristics of the microbiome on these monuments were revealed, and the association between the environmental geo-ecological feature and the indigenous microbiome was delineated. The most common microbial groups included 6 phyla, namely Acidobacteria, Actinobacteria, Gemmatimonadetes, Nitrospirae, Proteobacteria and Verrucomicrobia on the monuments, but Firmicutes and Chlamydiae were the most dominant phyla found in bats droppings. The taxonomic family of Chitinophagaceae could serve as a signature microbial group for Preah Vihear, the less visited site. More importantly, the pathogenic-related characteristics of the microbiome residing on Angkor monuments were uncovered. A set of specific antibiotic resistance genes (ARGs) with cross-niches dispersal capacity (between the environmental microbiome and the microbiome within warm blood fauna) was identified to be high by the source tracking analysis based on ARGs profile varies in this study. Among the 10 ARG-types detected in this study, 6 of them are confined to resistance mechanism of antibiotic efflux-pump. The findings of this study provide new a new direction on public health management and implication globally at archaeological sites for tourism.

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Molecular mechanism of ATF6 in unfolded protein response and its role in disease

Activating transcription factor 6 (ATF6), an important signaling molecule in unfolded protein response (UPR), plays a role in the pathogenesis of several diseases, including diseases such as congenital retinal disease, liver fibrosis and ankylosing spondylitis. After endoplasmic reticulum stress (ERS), ATF6 is activated after separation from binding immunoglobulin protein (GRP78/BiP) in the endoplasmic reticulum (ER) and transported to the Golgi apparatus to be hydrolyzed by site 1 and site 2 proteases into ATF6 fragments, which localize to the nucleus and regulate the transcription and expression of ERS-related genes. In these diseases, ERS leads to the activation of UPR, which ultimately lead to the occurrence and development of diseases by regulating the physiological state of cells through the ATF6 signaling pathway. Here, we discuss the evidence for the pathogenic importance of ATF6 signaling in different diseases and discuss preclinical results.

Multi-omics reveals the mechanism of rumen microbiome and its metabolome together with host metabolome participating in the regulation of milk production traits in dairy buffaloes

Recently, it has been discovered that certain dairy buffaloes can produce higher milk yield and milk fat yield under the same feeding management conditions, which is a potential new trait. It is unknown to what extent, the rumen microbiome and its metabolites, as well as the host metabolism, contribute to milk yield and milk fat yield. Therefore, we will analyze the rumen microbiome and host-level potential regulatory mechanisms on milk yield and milk fat yield through rumen metagenomics, rumen metabolomics, and serum metabolomics experiments. Microbial metagenomics analysis revealed a significantly higher abundance of several species in the rumen of high-yield dairy buffaloes, which mainly belonged to genera, such as Prevotella, Butyrivibrio, Barnesiella, Lachnospiraceae, Ruminococcus, and Bacteroides. These species contribute to the degradation of diets and improve functions related to fatty acid biosynthesis and lipid metabolism. Furthermore, the rumen of high-yield dairy buffaloes exhibited a lower abundance of methanogenic bacteria and functions, which may produce less methane. Rumen metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, including lipids, carbohydrates, and organic acids, as well as volatile fatty acids (VFAs), such as acetic acid and butyric acid. Meanwhile, several Prevotella, Butyrivibrio, Barnesiella, and Bacteroides species were significantly positively correlated with these metabolites. Serum metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, mainly lipids and organic acids. Meanwhile, several Prevotella, Bacteroides, Barnesiella, Ruminococcus, and Butyrivibrio species were significantly positively correlated with these metabolites. The combined analysis showed that several species were present, including Prevotella.sp.CAG1031, Prevotella.sp.HUN102, Prevotella.sp.KHD1, Prevotella.phocaeensis, Butyrivibrio.sp.AE3009, Barnesiella.sp.An22, Bacteroides.sp.CAG927, and Bacteroidales.bacterium.52-46, which may play a crucial role in rumen and host lipid metabolism, contributing to milk yield and milk fat yield. The "omics-explainability" analysis revealed that the rumen microbial composition, functions, metabolites, and serum metabolites contributed 34.04, 47.13, 39.09, and 50.14%, respectively, to milk yield and milk fat yield. These findings demonstrate how the rumen microbiota and host jointly affect milk production traits in dairy buffaloes. This information is essential for developing targeted feeding management strategies to improve the quality and yield of buffalo milk.

Mining the microbiome of Lake Afdera to gain insights into microbial diversity and biosynthetic potential

Microorganisms inhabiting hypersaline environments have received significant attention due to their ability to thrive under poly-extreme conditions, including high salinity, elevated temperatures and heavy metal stress. They are believed to possess biosynthetic gene clusters (BGCs) that encode secondary metabolites as survival strategy and offer potential biotechnological applications. In this study, we mined BGCs in shotgun metagenomic sequences generated from Lake Afdera, a hypersaline lake in the Afar Depression, Ethiopia. The microbiome of Lake Afdera is predominantly bacterial, with Acinetobacter (18.6%) and Pseudomonas (11.8%) being ubiquitously detected. A total of 94 distinct BGCs were identified in the metagenomic data. These BGCs are found to encode secondary metabolites with two main categories of functions: (i) potential pharmaceutical applications (nonribosomal peptide synthase NRPs, polyketide synthase, others) and (ii) miscellaneous roles conferring adaptation to extreme environment (bacteriocins, ectoine, others). Notably, NRPs (20.6%) and bacteriocins (10.6%) were the most abundant. Furthermore, our metagenomic analysis predicted gene clusters that enable microbes to defend against a wide range of toxic metals, oxidative stress and osmotic stress. These findings suggest that Lake Afdera is a rich biological reservoir, with the predicted BGCs playing critical role in the survival and adaptation of extremophiles.

Proportion-based normalizations outperform compositional data transformations in machine learning applications

BACKGROUND

Normalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm").

RESULTS

In our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin.

CONCLUSIONS

Our results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.

Preliminary data on cytotoxicity and functional group assessment of a herb-mineral combination against colorectal carcinoma cell line

OBJECTIVES

The invasive screening methods and the late stage diagnosis of colorectal carcinoma (CRC) are contributing for the devastative prognosis. The gradual shift of the disease pattern among younger generations requires the implementation of phytochemicals and traditional medicines. Arkeshwara rasa (AR) is a herb-mineral combination of Tamra bhasma/incinerated copper ashes and Dwigun Kajjali/mercury sulphide levigated with Calotropis procera leaf juice, Plumbago zeylanica root decoction and the decoction of three myrobalans (Terminalia chebula, Terminalia bellerica, Emblica Officinalis decoction)/Triphala decoction.

METHODS

The SW-480 cell line was checked for the cytotoxicity and the cell viability criteria with MTT(3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide) assay. The acridine orange/ethidium bromide (AO/EtBr) assay revealed the depth of apoptosis affected cells in the fluorescent images. The FTIR analysis exhibited the graphical spectrum of functional groups within the compound AR.

RESULTS

The IC50 from the 10-7 to 10-3 concentrations against SW-480 cells was 40.4 μg/mL. The staining of AO/EtBr was performed to visualize live and dead cells and it is evident from the result that number of apoptotic cells increases at increasing concentration of AR. The single bond with stretch vibrations of O-H and N-H are more concentrated in the 2,500-3,200 cm-1 and 3,700-4,000 cm-1 of the spectra whereas, the finger print region carries the O-H and S=O type peaks.

CONCLUSIONS

The AR shows strong cyto-toxicity against the SW-480 cells by inducing apoptosis. It also modulates cellular metabolism with the involvement of functional groups which antagonizes the strong acids. Moreover, these effects need to be analyzed further based in the in vivo and various in vitro models.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method

BACKGROUND

Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used.

METHODS

In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance.

RESULTS

A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900.

CONCLUSIONS

Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases.

Identification of enterotype and its predictive value for patients with colorectal cancer

BACKGROUND

Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined.

OBJECTIVE

To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype.

METHODS

The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated.

RESULTS

Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters.

CONCLUSION

This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

16S rRNA sequencing analysis of the oral and fecal microbiota in colorectal cancer positives versus colorectal cancer negatives in Iranian population

BACKGROUND

Colorectal cancer (CRC) poses a significant healthcare challenge, accounting for nearly 6.1% of global cancer cases. Early detection, facilitated by population screening utilizing innovative biomarkers, is pivotal for mitigating CRC incidence. This study aims to scrutinize the fecal and salivary microbiomes of CRC-positive individuals (CPs) in comparison to CRC-negative counterparts (CNs) to enhance early CRC diagnosis through microbial biomarkers.

MATERIAL AND METHODS

A total of 80 oral and stool samples were collected from Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran, encompassing both CPs and CNs undergoing screening. Microbial profiling was conducted using 16S rRNA sequencing assays, employing the Nextera XT Index Kit on an Illumina NovaSeq platform.

RESULTS

Distinct microbial profiles were observed in saliva and stool samples of CPs, diverging significantly from those of CNs at various taxonomic levels, including phylum, family, and species. Saliva samples from CPs exhibited abundance of Calothrix parietina, Granulicatella adiacens, Rothia dentocariosa, and Rothia mucilaginosa, absent in CNs. Additionally, Lachnospiraceae and Prevotellaceae were markedly higher in CPs' feces, while the Fusobacteria phylum was significantly elevated in CPs' saliva. Conversely, the non-pathogenic bacterium Akkermansia muciniphila exhibited a significant decrease in CPs' fecal samples compared to CNs.

CONCLUSION

Through meticulous selection of saliva and stool microbes based on Mean Decrease GINI values and employing logistic regression for saliva and support vector machine models for stool, we successfully developed a microbiota test with heightened sensitivity and specificity for early CRC detection.

Analysis of Antibiotic Resistance Genes in Water Reservoirs and Related Wastewater from Animal Farms in Central China

This study aimed to explore the phenotype and relationship of drug resistance genes in livestock and poultry farm wastewater and drinking water reservoirs to provide evidence for the transmission mechanisms of drug resistance genes, in order to reveal the spread of drug resistance genes in wastewater from intensive farms in Central China to urban reservoirs that serve as drinking water sources and provide preliminary data for the treatment of wastewater from animal farms to reduce the threat to human beings. DNA extraction and metagenomic sequencing were performed on eight groups of samples collected from four water reservoirs and four related wastewaters from animal farms in Central China. Metagenomic sequencing showed that the top 20 AROs with the highest abundance were vanT_gene, vanY_gene, adeF, qacG, Mtub_rpsL_STR, vanY_gene_, vanW_gene, Mtub_murA_FOF, vanY_gene, vanH_gene, FosG, rsmA, qacJ, RbpA, vanW_gene, aadA6, vanY_gene, sul4, sul1, and InuF. The resistance genes mentioned above belong to the following categories of drug resistance mechanisms: antibiotic target replacement, antibiotic target protection, antibiotic inactivation, and antibiotic efflux. The resistomes that match the top 20 genes are Streptococcus agalactiae and Streptococcus anginosus; Enterococcus faecalis; Enterococcus faecium; Actinomyces viscosus and Bacillus cereus. Enterococcus faecium; Clostridium tetani; Streptococcus agalactiae and Streptococcus anginosus; Streptococcus agalactiae and Streptococcus anginosus; Acinetobacter baumannii, Bifidobacterium bifidum, Bifidobacterium breve, Bifidobacterium longum, Corynebacterium jeikeium, Corynebacterium urealyticum, Mycobacterium kansasii, Mycobacterium tuberculosis, Schaalia odontolytica, and Trueperella pyogenes; Mycobacterium avium and Mycobacterium tuberculosis; Aeromonas caviae, Enterobacter hormaechei, Vibrio cholerae, Vibrio metoecus, Vibrio parahaemolyticus, and Vibrio vulnificus; Pseudomonas aeruginosa and Pseudomonas fluorescens; Staphylococcus aureus and Staphylococcus equorum; M. avium, Achromobacter xylosoxidans, and Acinetobacter baumannii; Sphingobium yanoikuyae, Acinetobacter indicus, Morganella morganii, Proteus mirabilis, Proteus vulgaris, Providencia rettgeri, and Providencia stuartii. Unreported drug resistance genes and drug-resistant bacteria in Central China were identified in 2023. In the transmission path of drug resistance genes, the transmission path from aquaculture wastewater to human drinking water sources cannot be ignored. For the sake of human health and ecological balance, the treatment of aquaculture wastewater needs to be further strengthened, and the effective blocking of drug resistance gene transmission needs to be considered.

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Background

Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.

Results

The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.

Discussion

These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Electrospray alginate microgels co-encapsulating degraded Konjac glucomannan and quercetin modulate human gut microbiota in vitro

Alginate microgels co-encapsulating degraded Konjac glucomannan (KGM60) underwent in vitro fecal fermentation and their effects on human microbiota and metabolites were investigated. KGM60 delayed quercetin release and enhanced phenolic metabolites production. Microgels co-encapsulating KGM60 and quercetin increased linear short chain fatty acid but decreased branched chain fatty acid production. Microgels encapsulated with quercetin with or without KGM60 decreased Firmicutes while increased Bacteroidetes over 24 h of fermentation, at genus level promoted Bacteroides growth at 24 h and decreased the abundance of Negativibacillus, Ruminococcus_NK4A214, and Christensenellaceae R_7. Faecalibacterium and Collinsella levels were exclusively promoted by microgels encapsulating KGM60 with or without quercetin, highlighting prebiotic effect of KGM60. Only microgels co-encapsulating both KGM60 and quercetin enhanced Dialister while inhibited Lachnoclostridium, indicating synergism between KGM60 and quercetin. Our study indicates that co-encapsulating KGM60 and quercetin in alginate microgel is effective in modulating human gut microbiota and metabolites production potentially beneficial to gut health.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Loss of symbiotic and increase of virulent bacteria through microbial networks in Lynch syndrome colon carcinogenesis

Purpose

Through a pilot study, we performed whole gut metagenomic analysis in 17 Lynch syndrome (LS) families, including colorectal cancer (CRC) patients and their healthy first-degree relatives. In a second asymptomatic LS cohort (n=150) undergoing colonoscopy-screening program, individuals with early precancerous lesions were compared to those with a normal colonoscopy. Since bacteria are organized into different networks within the microbiota, we compared related network structures in patients and controls.

Experimental design

Fecal prokaryote DNA was extracted prior to colonoscopy for whole metagenome (n=34, pilot study) or 16s rRNA sequencing (validation study). We characterized bacteria taxonomy using Diamond/MEGAN6 and DADA2 pipelines and performed differential abundances using Shaman website. We constructed networks using SparCC inference tools and validated the construction's accuracy by performing qPCR on selected bacteria.

Results

Significant differences in bacterial communities in LS-CRC patients were identified, with an enrichment of virulent bacteria and a depletion of symbionts compared to their first-degree relatives. Bacteria taxa in LS asymptomatic individuals with colonic precancerous lesions (n=79) were significantly different compared to healthy individuals (n=71). The main bacterial network structures, constructed based on bacteria-bacteria correlations in CRC (pilot study) and in asymptomatic precancerous patients (validation-study), showed a different pattern than in controls. It was characterized by virulent/symbiotic co-exclusion in both studies and illustrated (validation study) by a higher Escherichia/Bifidobacterium ratio, as assessed by qPCR.

Conclusion

Enhanced fecal virulent/symbiotic bacteria ratios influence bacterial network structures. As an early event in colon carcinogenesis, these ratios can be used to identify asymptomatic LS individual with a higher risk of CRC.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.