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

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.

Performance of a Shotgun Prediction Model for Colorectal Cancer When Using 16S rRNA Sequencing Data

Colorectal cancer (CRC), the third most common cancer globally, has shown links to disturbed gut microbiota. While significant efforts have been made to establish a microbial signature indicative of CRC using shotgun metagenomic sequencing, the challenge lies in validating this signature with 16S ribosomal RNA (16S) gene sequencing. The primary obstacle is reconciling the differing outputs of these two methodologies, which often lead to divergent statistical models and conclusions. In this study, we introduce an algorithm designed to bridge this gap by mapping shotgun-derived taxa to their 16S counterparts. This mapping enables us to assess the predictive performance of a shotgun-based microbiome signature using 16S data. Our results demonstrate a reduction in performance when applying the 16S-mapped taxa in the shotgun prediction model, though it retains statistical significance. This suggests that while an exact match between shotgun and 16S data may not yet be feasible, our approach provides a viable method for comparative analysis and validation in the context of CRC-associated microbiome research.

Tumor-associated microbiome features of metastatic colorectal cancer and clinical implications

Background

Colon microbiome composition contributes to the pathogenesis of colorectal cancer (CRC) and prognosis. We analyzed 16S rRNA sequencing data from tumor samples of patients with metastatic CRC and determined the clinical implications.

Materials and methods

We enrolled 133 patients with metastatic CRC at St. Vincent Hospital in Korea. The V3-V4 regions of the 16S rRNA gene from the tumor DNA were amplified, sequenced on an Illumina MiSeq, and analyzed using the DADA2 package.

Results

After excluding samples that retained <5% of the total reads after merging, 120 samples were analyzed. The median age of patients was 63 years (range, 34-82 years), and 76 patients (63.3%) were male. The primary cancer sites were the right colon (27.5%), left colon (30.8%), and rectum (41.7%). All subjects received 5-fluouracil-based systemic chemotherapy. After removing genera with <1% of the total reads in each patient, 523 genera were identified. Rectal origin, high CEA level (≥10 ng/mL), and presence of lung metastasis showed higher richness. Survival analysis revealed that the presence of Prevotella (p = 0.052), Fusobacterium (p = 0.002), Selenomonas (p<0.001), Fretibacterium (p = 0.001), Porphyromonas (p = 0.007), Peptostreptococcus (p = 0.002), and Leptotrichia (p = 0.003) were associated with short overall survival (OS, <24 months), while the presence of Sphingomonas was associated with long OS (p = 0.070). From the multivariate analysis, the presence of Selenomonas (hazard ratio [HR], 6.35; 95% confidence interval [CI], 2.38-16.97; p<0.001) was associated with poor prognosis along with high CEA level.

Conclusion

Tumor microbiome features may be useful prognostic biomarkers for metastatic CRC.

Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants

The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.

Multi-kingdom microbial signatures in excess body weight colorectal cancer based on global metagenomic analysis

Excess body weight (EBW) increases the risk of colorectal cancer (CRC) and is linked to lower colonoscopy compliance. Here, we extensively analyzed 981 metagenome samples from multiple cohorts to pinpoint the specific microbial signatures and their potential capability distinguishing EBW patients with CRC. The gut microbiome displayed considerable variations between EBW and lean CRC. We identify 44 and 37 distinct multi-kingdom microbial species differentiating CRC and controls in EBW and lean populations, respectively. Unique bacterial-fungal associations are also observed between EBW-CRC and lean-CRC. Our analysis revealed specific microbial functions in EBW-CRC, including D-Arginine and D-ornithine metabolism, and lipopolysaccharide biosynthesis. The best-performing classifier for EBW-CRC, comprising 12 bacterial and three fungal species, achieved an AUROC of 0.90, which was robustly validated across three independent cohorts (AUROC = 0.96, 0.94, and 0.80). Pathogenic microbial species, Anaerobutyricum hallii, Clostridioides difficile and Fusobacterium nucleatum, are EBW-CRC specific signatures. This work unearths the specific multi-kingdom microbial signatures for EBW-CRC and lean CRC, which may contribute to precision diagnosis and treatment of CRC.

Fusobacterium nucleatum promotes inflammatory and anti-apoptotic responses in colorectal cancer cells via ADP-heptose release and ALPK1/TIFA axis activation

The anaerobic bacterium Fusobacterium nucleatum is significantly associated with human colorectal cancer (CRC) and is considered a significant contributor to the disease. The mechanisms underlying the promotion of intestinal tumor formation by F. nucleatum have only been partially uncovered. Here, we showed that F. nucleatum releases a metabolite into the microenvironment that strongly activates NF-κB in intestinal epithelial cells via the ALPK1/TIFA/TRAF6 pathway. Furthermore, we showed that the released molecule had the biological characteristics of ADP-heptose. We observed that F. nucleatum induction of this pathway increased the expression of the inflammatory cytokine IL-8 and two anti-apoptotic genes known to be implicated in CRC, BIRC3 and TNFAIP3. Finally, it promoted the survival of CRC cells and reduced 5-fluorouracil chemosensitivity in vitro. Taken together, our results emphasize the importance of the ALPK1/TIFA pathway in Fusobacterium induced-CRC pathogenesis, and identify the role of ADP-H in this process.

Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.

Current Research on Molecular Biomarkers for Colorectal Cancer in Stool Samples

Colorectal cancer (CRC) is one of the most diagnosed cancers worldwide, with a high incidence and mortality rate when diagnosed late. Currently, the methods used in healthcare to diagnose CRC are the fecal occult blood test, flexible sigmoidoscopy, and colonoscopy. However, the lack of sensitivity and specificity and low population adherence are driving the need to implement other technologies that can identify biomarkers that not only help with early CRC detection but allow for the selection of more personalized treatment options. In this regard, the implementation of omics technologies, which can screen large pools of biological molecules, coupled with molecular validation, stands out as a promising tool for the discovery of new biomarkers from biopsied tissues or body fluids. This review delves into the current state of the art in the identification of novel CRC biomarkers that can distinguish cancerous tissue, specifically from fecal samples, as this could be the least invasive approach.

Metagenomics-based exploration of key soil microorganisms contributing to continuously planted Casuarina equisetifolia growth inhibition and their interactions with soil nutrient transformation

Casuarina equisetifolia (C. equisetifolia) is an economically important forest tree species, often cultivated in continuous monoculture as a coastal protection forest. Continuous planting has gradually affected growth and severely restricted the sustainable development of the C. equisetifolia industry. In this study, we analyzed the effects of continuous planting on C. equisetifolia growth and explored the rhizosphere soil microecological mechanism from a metagenomic perspective. The results showed that continuous planting resulted in dwarfing, shorter root length, and reduced C. equisetifolia seedling root system. Metagenomics analysis showed that 10 key characteristic microorganisms, mainly Actinoallomurus, Actinomadura, and Mycobacterium, were responsible for continuously planted C. equisetifolia trees. Quantitative analysis showed that the number of microorganisms in these three genera decreased significantly with the increase of continuous planting. Gene function analysis showed that continuous planting led to the weakening of the environmental information processing-signal transduction ability of soil characteristic microorganisms, and the decrease of C. equisetifolia trees against stress. Reduced capacity for metabolism, genetic information processing-replication and repair resulted in reduced microbial propagation and reduced microbial quantity in the rhizosphere soil of C. equisetifolia trees. Secondly, amino acid metabolism, carbohydrate metabolism, glycan biosynthesis and metabolism, lipid metabolism, metabolism of cofactors and vitamins were all significantly reduced, resulting in a decrease in the ability of the soil to synthesize and metabolize carbon and nitrogen. These reduced capacities further led to reduced soil microbial quantity, microbial carbon and nitrogen, microbial respiration intensity, reduced soil enzyme nutrient cycling and resistance-related enzyme activities, a significant reduction in available nutrient content of rhizosphere soils, a reduction in the ion exchange capacity, and an impediment to C. equisetifolia growth. This study provides an important basis for the management of continuously planted C. equisetifolia plantations.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Designing a multi-epitope vaccine against Peptostreptococcus anaerobius based on an immunoinformatics approach

Peptostreptococcus anaerobius is an anaerobic bacterium, which has been found selectively en-riched in the fecal and mucosal microbiota of colorectal cancer (CRC) patients. Emerging evidence suggest P. anaerobius may contribute to the development of CRC in human. In this study, we designed a multi-epitope chimeric vaccine against P. anaerobius PCWBR2, a recently identified adhesin that interacts directly with colon cell lines by binding α2/β1 integrin frequently overexpressed in human CRC tumors and cell lines. Immunoinformatics tools predicted six cytotoxic T lymphocyte epitopes, five helper T lymphocyte epitopes, and six linear B lymphocyte epitopes. The predicted epitopes were joined with AAY or GPGPG linkers and a previously reported TLR4 agonist was added to the vaccine construct's N terminal as an adjuvant using EAAAK linkers and the order of epitopes was optimized. Further in silico analysis revealed that the vaccine construct possesses satisfactory antigenicity, allergenicity, solubility, physicochemical properties, adjuvant-TLR4 molecular docking, and immune profile characteristics. Our study provided a promising design for vaccines against P. anaerobius.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data

MOTIVATION

With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model.

RESULTS

To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status.

AVAILABILITY AND IMPLEMENTATION

https://github.com/liaoherui/GDmicro.

Parvimonas micra, an oral pathobiont associated with colorectal cancer, epigenetically reprograms human colonocytes

Recently, an intestinal dysbiotic microbiota with enrichment in oral cavity bacteria has been described in colorectal cancer (CRC) patients. Here, we characterize and investigate one of these oral pathobionts, the Gram-positive anaerobic coccus Parvimonas micra. We identified two phylotypes (A and B) exhibiting different phenotypes and adhesion capabilities. We observed a strong association of phylotype A with CRC, with its higher abundance in feces and in tumoral tissue compared with the normal homologous colonic mucosa, which was associated with a distinct methylation status of patients. By developing an in vitro hypoxic co-culture system of human primary colonic cells with anaerobic bacteria, we show that P. micra phylotype A alters the DNA methylation profile promoters of key tumor-suppressor genes, oncogenes, and genes involved in epithelial-mesenchymal transition. In colonic mucosa of CRC patients carrying P. micra phylotype A, we found similar DNA methylation alterations, together with significant enrichment of differentially expressed genes in pathways involved in inflammation, cell adhesion, and regulation of actin cytoskeleton, providing evidence of P. micra's possible role in the carcinogenic process.

VTwins: inferring causative microbial features from metagenomic data of limited samples

It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method-virtual twins (VTwins)-to eliminate the confounder effects by transforming the original cohort into a paired cohort of "Twin" samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.

Role of the intestinal microbiota and diet in the onset and progression of colorectal and breast cancers and the interconnection between both types of tumours

Colorectal cancer (CRC) is among the leading causes of mortality in adults of both sexes worldwide, while breast cancer (BC) is among the leading causes of death in women. In addition to age, gender, and genetic predisposition, environmental and lifestyle factors exert a strong influence. Global diet, including alcohol consumption, is one of the most important modifiable factors affecting the risk of CRC and BC. Western dietary patterns promoting high intakes of xenobiotics from food processing and ethanol have been associated with increased cancer risk, whereas the Mediterranean diet, generally leading to a higher intake of polyphenols and fibre, has been associated with a protective effect. Gut dysbiosis is a common feature in CRC, where the usual microbiota is progressively replaced by opportunistic pathogens and the gut metabolome is altered. The relationship between microbiota and BC has been less studied. The estrobolome is the collection of genes from intestinal bacteria that can metabolize oestrogens. In a dysbiosis condition, microbial deconjugating enzymes can reactivate conjugated-deactivated oestrogens, increasing the risk of BC. In contrast, intestinal microorganisms can increase the biological activity and bioavailability of dietary phytochemicals through diverse microbial metabolic transformations, potentiating their anticancer activity. Members of the intestinal microbiota can increase the toxicity of xenobiotics through metabolic transformations. However, most of the microorganisms involved in diet-microbiota interactions remain poorly characterized. Here, we provide an overview of the associations between microbiota and diet in BC and CRC, considering the diverse types and heterogeneity of these cancers and their relationship between them and with gut microbiota.

Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome

There generally exists a critical state or tipping point from a stable state to another in the development of colorectal cancer (CRC) beyond which a significant qualitative transition occurs. Gut microbiome sequencing data can be collected non-invasively from fecal samples, making it more convenient to obtain. Furthermore, intestinal microbiome sequencing data contain phylogenetic information at various levels, which can be used to reliably identify critical states, thereby providing early warning signals more accurately and effectively. Yet, pinpointing the critical states using gut microbiome data presents a formidable challenge due to the high dimension and strong noise of gut microbiome data. To address this challenge, we introduce a novel approach termed the specific network information gain (SNIG) method to detect CRC's critical states at various taxonomic levels via gut microbiome data. The numerical simulation indicates that the SNIG method is robust under different noise levels and that it is also superior to the existing methods on detecting the critical states. Moreover, utilizing SNIG on two real CRC datasets enabled us to discern the critical states preceding deterioration and to successfully identify their associated dynamic network biomarkers at different taxonomic levels. Notably, we discovered certain 'dark species' and pathways intimately linked to CRC progression. In addition, we accurately detected the tipping points on an individual dataset of type I diabetes.

Genetic characteristics of Blastocystis sp. ST3 at the genome level in the Chinese population

The gut microbiota comprises the collective genomes of microbial symbionts and is composed of bacteria, fungi, viruses, and protists within the gastrointestinal tract of a host. Although the literature associated with gut microbiota is increasing, studies on eukaryotes in the human gut are just beginning to surface. Blastocystis is one of the most common intestinal parasites of humans and animals and is estimated to colonise more than 1 billion people on a global scale. However, the understanding of the genetic characteristics of Blastocystis subtype (ST) at the genome level and its relationship with other members of the gut microbiota is still limited. In this study, by surveying the prevalence and genome characteristics of Blastocystis sp. ST3 in a Chinese population (prevalence % = 6.09%), the association of Blastocystis sp. ST3 with region and time and the structure of the resident gut bacterial population was clarified. We identified novel sequences (50 mitochondrial and 41 genome sequences) and determined their genetic diversity amongst strains within Blastocystis sp. ST3 (4.14 SNPs/kb). Furthermore, we found that colonisation of Blastocystis was strongly associated with increased bacterial richness and higher abundance of several anaerobes. Finally, we performed time series sampling on two Blastocystis-positive individuals and confirmed that Blastocystis could exist continually in the human gut microbiota and persist for a long time, even for 4 years.

Effects of ex situ conservation on diversity and function of the gut microbiota of the Tibetan wild ass (Equus kiang)

Ex situ conservation is the main method for the protection of endangered wildlife. To explore the effect of ex situ conservation on the gut microbiota of the kiang (Equus kiang), metagenomic sequencing combined with bioinformatics analysis was used to investigate the composition and function of the gut microbiota of the kiang. The results showed that ex situ conservation not only protected wildlife, but also affected the composition and function of gut microbiota, as well as the health of animals. In the zoo, the ratio of the relative abundance of Firmicutes to that of Bacteroidetes (F/B) is higher, clusters of potentially pathogenic bacteria (such as Catonella, Catonella, and Mycoplasma) are more numerous, the abundance of resistance genes is higher, and the abundance of metabolic functions is increased. The dynamic changes of the gut microbiota also played an important role in the nutritional absorption, energy metabolism, and environmental adaptation of the kiang. Improving the rearing environment and increasing food diversity play important roles for increasing the diversity of gut microbiota, reducing the spread of potentially pathogenic bacteria, and reducing diseases. In the wild, especially in winter and in food-deficient areas, food supplementation can enhance the gut microbial homeostasis of wild animals and reduce the impact of crises. In depth studies of the gut microbial function of wildlife have important implications for improving ex situ conservation.

Time-restricted feeding affects the fecal microbiome metabolome and its diurnal oscillations in lung cancer mice

The homeostasis of the gut microbiota and circadian rhythm is critical to host health, and both are inextricably intertwined with lung cancer. Although time-restricted feeding (TRF) can maintain circadian synchronization and improve metabolic disorders, the effects of TRF on the fecal microbiome, metabolome and their diurnal oscillations in lung cancer have not been discussed. We performed 16S rRNA sequencing and untargeted metabonomic sequencing of the feces prepared from models of tumor-bearing BALB/c nude mice and urethane-induced lung cancer. We demonstrated for the first time that TRF significantly delayed the growth of lung tumors. Moreover, TRF altered the abundances of the fecal microbiome, metabolome and circadian clocks, as well as their rhythmicity, in lung cancer models of tumor-bearing BALB/c nude mice and/or urethane-induced lung cancer C57BL/6J mice. The results of fecal microbiota transplantation proved that the antitumor effects of TRF occur by regulating the fecal microbiota. Notably, Lactobacillus and Bacillus were increased upon TRF and were correlated with most differential metabolites. Pathway enrichment analysis of metabolites revealed that TRF mainly affected immune and inflammatory processes, which might further explain how TRF exerted its anticancer benefits. These findings underscore the possibility that the fecal microbiome/metabolome regulates lung cancer following a TRF paradigm.

Roseburia intestinalis generated butyrate boosts anti-PD-1 efficacy in colorectal cancer by activating cytotoxic CD8+ T cells

OBJECTIVE

Roseburia intestinalis is a probiotic species that can suppress intestinal inflammation by producing metabolites. We aimed to study the role of R. intestinalis in colorectal tumourigenesis and immunotherapy.

DESIGN

R. intestinalis abundance was evaluated in stools of patients with colorectal cancer (CRC) (n=444) and healthy controls (n=575). The effects of R. intestinalis were studied in ApcMin/+ or azoxymethane (AOM)-induced CRC mouse models, and in syngeneic mouse xenograft models of CT26 (microsatellite instability (MSI)-low) or MC38 (MSI-high). The change of immune landscape was evaluated by multicolour flow cytometry and immunohistochemistry staining. Metabolites were profiled by metabolomic profiling.

RESULTS

R. intestinalis was significantly depleted in stools of patients with CRC compared with healthy controls. R. intestinalis administration significantly inhibited tumour formation in ApcMin/+ mice, which was confirmed in mice with AOM-induced CRC. R. intestinalis restored gut barrier function as indicated by improved intestinal permeability and enhanced expression of tight junction proteins. Butyrate was identified as the functional metabolite generated by R. intestinalis. R. intestinalis or butyrate suppressed tumour growth by inducing cytotoxic granzyme B+, interferon (IFN)-γ+ and tumour necrosis factor (TNF)-α+ CD8+ T cells in orthotopic mouse models of MC38 or CT26. R. intestinalis or butyrate also significantly improved antiprogrammed cell death protein 1 (anti-PD-1) efficacy in mice bearing MSI-low CT26 tumours. Mechanistically, butyrate directly bound to toll-like receptor 5 (TLR5) receptor on CD8+ T cells to induce its activity through activating nuclear factor kappa B (NF-κB) signalling.

CONCLUSION

R. intestinalis protects against colorectal tumourigenesis by producing butyrate, which could also improve anti-PD-1 efficacy by inducing functional CD8+ T cells. R. intestinalis is a potential adjuvant to augment anti-PD-1 efficacy against CRC.

The Impact of Bamboo Consumption on the Spread of Antibiotic Resistance Genes in Giant Pandas

The spread of antibiotic resistance genes (ARGs) in the environment exacerbates the contamination of these genes; therefore, the role plants play in the transmission of resistance genes in the food chain requires further research. Giant pandas consume different bamboo parts at different times, which provides the possibility of investigating how a single food source can affect the variation in the spread of ARGs. In this study, metagenomic analysis and the Comprehensive Antibiotic Resistance Database (CARD) database were used to annotate ARGs and the differences in gut microbiota ARGs during the consumption of bamboo shoots, leaves, and culms by captive giant pandas. These ARGs were then compared to investigate the impact of bamboo part consumption on the spread of ARGs. The results showed that the number of ARGs in the gut microbiota of the subjects was highest during the consumption of bamboo leaves, while the variety of ARGs was highest during the consumption of shoots. Escherichia coli, which poses a higher risk of ARG dissemination, was significantly higher in the leaf group, while Klebsiella, Enterobacter, and Raoultella were significantly higher in the shoot group. The ARG risk brought by bamboo shoots and leaves may originate from soil and environmental pollution. It is recommended to handle the feces of giant pandas properly and regularly monitor the antimicrobial and virulence genes in their gut microbiota to mitigate the threat of antibiotic resistance.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

KIF15 knockdown inhibits colorectal cancer proliferation and migration through affecting the ubiquitination modification of NRAS

As one of the most common malignancies, colorectal cancer (CRC) requires a thorough understanding of the mechanisms that promote its development and the discovery of new therapeutic targets. In this study, immunohistochemical staining confirmed significantly higher expression levels of KIF15 in CRC. qPCR and western blot results demonstrated the effective suppression of KIF15 mRNA and protein expression by shKIF15. Downregulation of KIF15 inhibited the proliferation and migration of CRC cells while promoting apoptosis. In addition, evidence from the xenograft experiments in nude mice demonstrated that KIF15 knockdown also suppressed tumor growth. Through bioinformatics analysis, the downstream molecular NRAS and Rac signaling pathway associated with KIF15 were identified. KIF15 knockdown was found to inhibit NRAS expression and disrupt Rac signaling pathway. Moreover, WB and Co-IP assays revealed that KIF15 reduced the ubiquitination modification of NRAS protein by interacting with the E3 ligase MDM2, thereby enhancing NRAS protein stability. Functionally, NRAS knockdown was shown to inhibit cell proliferation and migration. In conclusion, KIF15 promoted CRC progression by regulating NRAS expression and Rac signaling pathway.

tRNA queuosine modification is involved in biofilm formation and virulence in bacteria

tRNA modifications are crucial for fine-tuning of protein translation. Queuosine (Q) modification of tRNAs is thought to modulate the translation rate of NAU codons, but its physiological role remains elusive. Therefore, we hypothesize that Q-tRNAs control those physiological processes involving NAU codon-enriched genes (Q-genes). Here, we report a novel bioinformatic strategy to predict Q-genes, revealing a widespread enrichment in functions, especially those related to biofilm formation and virulence in bacteria, and particularly in human pathogens. Indeed, we experimentally verified that these processes were significantly affected by altering the degree of tRNA Q-modification in different model bacteria, representing the first report of a general mechanism controlling biofilm formation and virulence in Gram-positive and Gram-negative bacteria possibly through the coordination of the expression of functionally related genes. Furthermore, we propose that changes in Q availability in a microbiome would affect its functionality. Our findings open the door to the control of bacterial infections and biofilm formation by inhibition of tRNA Q-modification.

Intracellular bacteria in cancer-prospects and debates

Recent evidence suggests that some human cancers may harbor low-biomass microbial ecosystems, spanning bacteria, viruses, and fungi. Bacteria, the most-studied kingdom in this context, are suggested by these studies to localize within cancer cells, immune cells and other tumor microenvironment cell types, where they are postulated to impact multiple cancer-related functions. Herein, we provide an overview of intratumoral bacteria, while focusing on intracellular bacteria, their suggested molecular activities, communication networks, host invasion and evasion strategies, and long-term colonization capacity. We highlight how the integration of sequencing-based and spatial techniques may enable the recognition of bacterial tumor niches. We discuss pitfalls, debates and challenges in decisively proving the existence and function of intratumoral microbes, while reaching a mechanistic elucidation of their impacts on tumor behavior and treatment responses. Together, a causative understanding of possible roles played by intracellular bacteria in cancer may enable their future utilization in diagnosis, patient stratification, and treatment.

Cross-study analyses of microbial abundance using generalized common factor methods

BACKGROUND

By creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in the environmental and biomedical sciences. However, this is challenging, as genomic data are high-dimensional, sparse, and noisy. Much of this noise reflects the exact conditions under which sequencing took place, and is so significant that it limits consensus-based validation of study results.

RESULTS

We propose an ensemble approach for cross-study exploratory analyses of microbial abundance data in which we first estimate the variance-covariance matrix of the underlying abundances from each dataset on the log scale assuming Poisson sampling, and subsequently model these covariances jointly so as to find a shared low-dimensional subspace of the feature space.

CONCLUSIONS

By viewing the projection of the latent true abundances onto this common structure, the variation is pared down to that which is shared among all datasets, and is likely to reflect more generalizable biological signal than can be inferred from individual datasets. We investigate several ways of achieving this, demonstrate that they work well on simulated and real metagenomic data in terms of signal retention and interpretability, and recommend a particular implementation.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

A Preliminary Study of the Potential Molecular Mechanisms of Individual Growth and Rumen Development in Calves with Different Feeding Patterns

At present, it is common to feed calves with "Concentrate", "Concentrate + hay" and TMR "Total Mixed Rations" feeding patterns in China, which achieved well feeding efficiency, but the three feeding patterns molecular regulation mechanism in actual production is still unclear. The study aimed to explore the most suitable feeding pattern for Chinese Holstein calves to improve the rumen fermentation function and growth performance of calves. In this regard, the interactions between rumen microorganisms and host metabolism were investigated. The rumen volume and weight of calves in the GF group were significantly higher than those in the GFF and TMR groups (p < 0.05), and the rumen pH of calves in the GF group was 6.47~6.79. Metagenomics analysis revealed that the rumen microbiome of GF and GFF calves had higher relative abundances of Methanobrevibacter, Methanosphaera, and Methanolacinia (p < 0.05). Prevotella multisaccharivorax was significantly more abundant in the rumen of GF calves (p < 0.05), indicating that GF group calves had a stronger ability to ferment sugars. Notably, in the pyruvate metabolic pathway, phosphoenolpyruvate carboxylase was significantly up-regulated in GF calves compared with the TMR group, and pyruvate-phosphate dikinase was significantly down-regulated. Metabolomic results showed that Ursodeoxycholic acid was significantly up-regulated in GF calves, and most of the differential metabolites were enriched in Bile secretion pathways. The association analysis study found that the microorganisms of Prevotella and Ruminococcaceae might cooperate with the host, which was helpful for the digestion and absorption of lipids and made the calves have better growth. The three feeding modes had similar effects, but the 'GF' feeding pattern was more beneficial to the individual growth and ruminal development regarding ruminal morphology, contents physiology and microorganisms. Furthermore, the synergistic effect of rumen microorganisms and the host could more effectively hydrolyze lipid substances and promote the absorption of lipids, which was of great significance to the growth of calves.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy

Oncogenesis is associated with intestinal dysbiosis, and stool shotgun metagenomic sequencing in individuals with this condition might constitute a non-invasive approach for the early diagnosis of several cancer types. The prognostic relevance of antibiotic intake and gut microbiota composition urged investigators to develop tools for the detection of intestinal dysbiosis to enable patient stratification and microbiota-centred clinical interventions. Moreover, since the advent of immune-checkpoint inhibitors (ICIs) in oncology, the identification of biomarkers for predicting their efficacy before starting treatment has been an unmet medical need. Many previous studies addressing this question, including a meta-analysis described herein, have led to the description of Gut OncoMicrobiome Signatures (GOMS). In this Review, we discuss how patients with cancer across various subtypes share several GOMS with individuals with seemingly unrelated chronic inflammatory disorders who, in turn, tend to have GOMS different from those of healthy individuals. We discuss findings from the aforementioned meta-analysis of GOMS patterns associated with clinical benefit from or resistance to ICIs across different cancer types (in 808 patients), with a focus on metabolic and immunological surrogate markers of intestinal dysbiosis, and propose practical guidelines to incorporate GOMS in decision-making for prospective clinical trials in immuno-oncology.

Male-Biased Gut Microbiome and Metabolites Aggravate Colorectal Cancer Development

Men demonstrate higher incidence and mortality rates of colorectal cancer (CRC) than women. This study aims to explain the potential causes of such sexual dimorphism in CRC from the perspective of sex-biased gut microbiota and metabolites. The results show that sexual dimorphism in colorectal tumorigenesis is observed in both ApcMin/ + mice and azoxymethane (AOM)/dextran sulfate sodium (DSS)-treated mice with male mice have significantly larger and more tumors, accompanied by more impaired gut barrier function. Moreover, pseudo-germ mice receiving fecal samples from male mice or patients show more severe intestinal barrier damage and higher level of inflammation. A significant change in gut microbiota composition is found with increased pathogenic bacteria Akkermansia muciniphila and deplets probiotic Parabacteroides goldsteinii in both male mice and pseudo-germ mice receiving fecal sample from male mice. Sex-biased gut metabolites in pseudo-germ mice receiving fecal sample from CRC patients or CRC mice contribute to sex dimorphism in CRC tumorigenesis through glycerophospholipids metabolism pathway. Sexual dimorphism in tumorigenesis of CRC mouse models. In conclusion, the sex-biased gut microbiome and metabolites contribute to sexual dimorphism in CRC. Modulating sex-biased gut microbiota and metabolites could be a potential sex-targeting therapeutic strategy of CRC.

Efficient Mining of Anticancer Peptides from Gut Metagenome

The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well-established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi-center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types.

Discovery of potent natural product higenamine derivatives as novel Anti-Fusobacterium nucleatum agents

Fusobacterium nucleatum (F. nucleatum) is closely associated with the occurrence and development of colorectal cancer (CRC). Discovery of specific antibacterial agents against F. nucleatum was urgent for the prevention and treatment of CRC. We screened a natural product library and successfully identified higenamine as an antibacterial hit against F. nucleatum. Further hit optimizations led to the discovery of new higenamine derivatives with improved anti-F. nucleatum activity. Among them, compound 7c showed potent antibacterial activity against F. nucleatum (MIC50 = 0.005 μM) with good selectivity toward intestinal bacteria and normal cells. It significantly inhibited the migration of CRC cells induced by F. nucleatum. Mechanism study revealed that compound 7c impaired the integrity of biofilm and cell wall, which represents a good starting point for the development of novel anti-F. nucleatum agents.

Gene-based microbiome representation enhances host phenotype classification

With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.

MV-CVIB: microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer

Introduction

Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models.

Methods

To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB).

Results

The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets.

Discussion

Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.