The gut microbial diversity of colon cancer patients and the clinical significance

Distinct gut microbiomes in Thai patients with colorectal polyps

BACKGROUND

Colorectal polyps that develop via the conventional adenoma-carcinoma sequence [e.g., tubular adenoma (TA)] often progress to malignancy and are closely associated with changes in the composition of the gut microbiome. There is limited research concerning the microbial functions and gut microbiomes associated with colorectal polyps that arise through the serrated polyp pathway, such as hyperplastic polyps (HP). Exploration of microbiome alterations associated with HP and TA would improve the understanding of mechanisms by which specific microbes and their metabolic pathways contribute to colorectal carcinogenesis.

AIM

To investigate gut microbiome signatures, microbial associations, and microbial functions in HP and TA patients.

METHODS

Full-length 16S rRNA sequencing was used to characterize the gut microbiome in stool samples from control participants without polyps [control group (CT), n = 40], patients with HP (n = 52), and patients with TA (n = 60). Significant differences in gut microbiome composition and functional mechanisms were identified between the CT group and patients with HP or TA. Analytical techniques in this study included differential abundance analysis, co-occurrence network analysis, and differential pathway analysis.

RESULTS

Colorectal cancer (CRC)-associated bacteria, including Streptococcus gallolyticus (S. gallolyticus), Bacteroides fragilis, and Clostridium symbiosum, were identified as characteristic microbial species in TA patients. Mediterraneibacter gnavus, associated with dysbiosis and gastrointestinal diseases, was significantly differentially abundant in the HP and TA groups. Functional pathway analysis revealed that HP patients exhibited enrichment in the sulfur oxidation pathway exclusively, whereas TA patients showed dominance in pathways related to secondary metabolite biosynthesis (e.g., mevalonate); S. gallolyticus was a major contributor. Co-occurrence network and dynamic network analyses revealed co-occurrence of dysbiosis-associated bacteria in HP patients, whereas TA patients exhibited co-occurrence of CRC-associated bacteria. Furthermore, the co-occurrence of SCFA-producing bacteria was lower in TA patients than HP patients.

CONCLUSION

This study revealed distinct gut microbiome signatures associated with pathways of colorectal polyp development, providing insights concerning the roles of microbial species, functional pathways, and microbial interactions in colorectal carcinogenesis.

The effects of Mitragyna speciosa extracts on intestinal microbiota and their metabolites in vitro fecal fermentation

BACKGROUND

Kratom (Mitragyna speciosa) has a long history of traditional use. It contains various alkaloids and polyphenols. The properties of kratom's alkaloids have been well-documented. However, the property of kratom's polyphenols in water-soluble phase have been less frequently reported. This study assessed the effects of water-soluble Mitragyna speciosa (kratom) extract (MSE) on gut microbiota and their metabolite production in fecal batch culture.

RESULTS

The water-soluble kratom extract (MSE0) and the water-soluble kratom extract after partial sugar removal (MSE50) both contained polyphenols, with total phenolic levels of 2037.91 ± 51.13 and 3997.95 ± 27.90 mg GAE/g extract, respectively and total flavonoids of 81.10 ± 1.00 and 84.60 ± 1.43 mg CEQ/g extract. The gut microbiota in fecal batch culture was identified by 16S rRNA gene sequencing at 0 and 24 h of fermentation. After fermentation, MSE50 stimulated the growth of Bifidobacterium more than MSE0. MSE0 gave the highest total fatty acids level among the treatments. The phenolic metabolites produced by some intestinal microbiota during fecal fermentation at 24 h were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The major metabolite of biotransformation of both water-soluble MSEs by intestinal microbiota was pyrocatechol (9.85-11.53%).

CONCLUSION

The water-soluble MSEs and their produced metabolites could potentially be used as ingredients for functional and medicinal food production that supports specific gut microbiota. © 2024 Society of Chemical Industry.

Gut microbiota characteristics of colorectal cancer patients in Hubei, China, and differences with cohorts from other Chinese regions

The research on the correlation or causality between gut microbiota and the occurrence, development, and treatment of colorectal cancer (CRC) is receiving increasing emphasis. At the same time, the incidence and mortality of colorectal cancer vary among individuals and regions, as does the gut microbiota. In order to gain a better understanding of the characteristics of the gut microbiota in CRC patients and the differences between different regions, we initially compared the gut microbiota of 25 CRC patients and 26 healthy controls in the central region of China (Hubei Province) using 16S rRNA high-throughput sequencing technology. The results showed that Corynebacterium, Enterococcus, Lactobacillus, and Escherichia-Shigella were significantly enriched in CRC patients. In addition, we also compared the potential differences in functional pathways between the CRC group and the healthy control group using PICRUSt's functional prediction analysis. We then analyzed and compared it with five cohort studies from various regions of China, including Central, East, and Northeast China. We found that geographical factors may affect the composition of intestinal microbiota in CRC patients. The composition of intestinal microbiota is crucial information that influences colorectal cancer screening, early detection, and the prediction of CRC treatment outcomes. This emphasizes the importance of conducting research on CRC-related gut microbiota in various regions of China.

Emergence of rare and low abundant anaerobic gut Firmicutes is associated with a significant downfall of Klebsiella in human colon cancer

Gut bacterial dysbiosis has been linked to several gastrointestinal diseases, including deadly colorectal cancer (CRC), a leading cause of mortality in cancer patients. However, perturbation in gut bacteriome during colon cancer (CC, devoid of colorectal malignancy) remains poorly explored. Here, 16S rRNA gene amplicon sequencing was carried out for fecal DNA samples targeted to hypervariable V3-V4 region by employing MiSeq platform to explore the gut bacterial community shift in CC patients. While alpha diversity indices predicted high species richness and diversity, beta diversity showed marked gut bacterial compositional dissimilarity in CC versus healthy controls (HC, n = 10 each). We observed a significant (p < 0.05, Wilcoxon Rank-Sum test) emergence of low-abundant anaerobic taxa, including Parvimonas and Peptostreptococcus, in addition to Subdoligranulum, Coprococcus, Holdemanella, Solobacterium, Bilophila, Blautia, Dorea, Moryella and several unidentified taxa, mainly affiliated to Firmicutes, in CC patients. In addition, we also traced the emergence of putative probiotic taxon Slackia, belonging to Actinomycetota, in CC patients. The emergence of anaerobic Firmicutes in CC is accompanied by a significant (p < 0.05) decline in the Klebsiella, as determined through linear discriminant analysis effect size (LEfSe) and heat tree analyses. Shifts in core microbiome and variation in network correlation were also witnessed. Taken together, this study highlighted a significant and consistent emergence of rare anaerobic Firmicutes suggesting possible anaerobiosis driving gut microbial community shift, which could be exploited in designing diagnostic and therapeutic tools targeted to CC.

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.

Microbiome Sex-Related Diversity in Non-Muscle-Invasive Urothelial Bladder Cancer

Sex-specific discrepancies in bladder cancer (BCa) are reported, and new studies imply that microbiome may partially explain the diversity. We aim to provide characterization of the bladder microbiome in both sexes diagnosed with non-muscle-invasive BCa with specific insight into cancer grade. In our study, 16S rRNA next-generation sequencing was performed on midstream urine, bladder tumor sample, and healthy-appearing bladder mucosa. Bacterial DNA was isolated using QIAamp Viral RNA Mini Kit. Metagenomic analysis was performed using hypervariable fragments of the 16S rRNA gene on Ion Torrent Personal Genome Machine platform. Of 41 sample triplets, 2153 taxa were discovered: 1739 in tumor samples, 1801 in healthy-appearing bladder mucosa and 1370 in midstream urine. Women were found to have smaller taxa richness in Chao1 index than men (p = 0.03). In comparison to low-grade tumors, patients with high-grade lesions had lower bacterial diversity and richness in urine. Significant differences between sexes in relative abundance of communities at family level were only observed in high-grade tumors.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

SIGNIFICANCE

Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Gut microbiota and clinical response to immune checkpoint inhibitor therapy in patients with advanced cancer

BACKGROUND

There is currently no well-accepted consensus on the association between gut microbiota and the response to treatment of immune checkpoint inhibitors (ICIs) in patients with advanced cancer.

METHODS

Fecal samples were collected before ICI treatment. Gut microbiota was analyzed using 16 S ribosomal RNA sequencing. We investigated the relationship between the α-diversity of fecal microbiota and patients' clinical outcomes. Microbiota profiles from patients and healthy controls were determined. Pre-treatment serum was examined by cytokine array.

RESULTS

We analyzed 74 patients, including 42 with melanoma, 8 with kidney cancer, 13 with lung cancer, and 11 with other cancers. Combination therapy of anti-PD1 and anti-CTLA-4 was used in 14 patients, and monotherapy in the rest. Clinical benefit was observed in 35 (47.3 %) cases, including 2 complete responses, 16 partial responses, and 17 stable diseases according to RECIST criteria. No significant difference in α-diversity was found between the benefiter and non-benefiter groups. However, patients with α-diversity within the range of our healthy control had a significantly longer median overall survival (18.9 months), compared to the abnormal group (8.2 months) (p = 0.041, hazard ratio = 0.546) for all patients. The microbiota composition of the benefiters was similar to that of healthy individuals. Furthermore, specific bacteria, such as Prevotella copri and Faecalibacterium prausnitzii, were associated with a favorable outcome. We also observed that serum IL-18 before treatment was significantly lower in the benefiters, compared to non-benefiters.

CONCLUSIONS

The α-diversity of gut microbiota is positively correlated with more prolonged overall survival in cancer patients following ICI therapy.

Chondroitin Sulphate: An emerging therapeutic multidimensional proteoglycan in colon cancer

Chondroitin sulfate (CS) is a sulfated glycosaminoglycan (GAG) that has captured massive attention in the field of drug delivery. As the colon is considered the preferred site for local and systemic delivery of bioactive agents for the treatment of various diseases, colon-targeted drug delivery rose to the surface of research. Amid several tactics to attain colon-targeted drug release, the exploitation of polymers degraded by colonic bacteria holds great promise. Chondroitin sulfate as a biodegradable, biocompatible mucopolysaccharide is known for its anti-inflammatory, anti-osteoarthritis, anti-atherosclerotic, anti-oxidant, and anti-coagulant effects. Besides these therapeutic functions, CS thrived to play a major role in nanocarriers as a matrix material, coat, and targeting ligand. This review focuses on the role of CS in nanocarriers as a matrix material or as a targeting moiety for colon cancer therapy, relating the present applications to future perspectives.

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

BACKGROUND

Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.

METHODS

RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.

RESULTS

RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.

CONCLUSIONS

Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.

Fecal Microbiota and Associated Volatile Organic Compounds Distinguishing No-Adenoma from High-Risk Colon Adenoma Adults

Microbiota and the metabolites they produce within the large intestine interact with the host epithelia under the influence of a range of host-derived metabolic, immune, and homeostatic factors. This complex host-microbe interaction affects intestinal tumorigenesis, but established microbial or metabolite profiles predicting colorectal cancer (CRC) risk are missing. Here, we aimed to identify fecal bacteria, volatile organic compounds (VOC), and their associations that distinguish healthy (non-adenoma, NA) from CRC prone (high-risk adenoma, HRA) individuals. Analyzing fecal samples obtained from 117 participants ≥15 days past routine colonoscopy, we highlight the higher abundance of Proteobacteria and Parabacteroides distasonis, and the lower abundance of Lachnospiraceae species, Roseburia faecis, Blautia luti, Fusicatenibacter saccharivorans, Eubacterium rectale, and Phascolarctobacterium faecium in the samples of HRA individuals. Volatolomic analysis of samples from 28 participants revealed a higher concentration of five compounds in the feces of HRA individuals, isobutyric acid, methyl butyrate, methyl propionate, 2-hexanone, and 2-pentanone. We used binomial logistic regression modeling, revealing 68 and 96 fecal bacteria-VOC associations at the family and genus level, respectively, that distinguish NA from HRA endpoints. For example, isobutyric acid associations with Lachnospiraceae incertae sedis and Bacteroides genera exhibit positive and negative regression lines for NA and HRA endpoints, respectively. However, the same chemical associates with Coprococcus and Colinsella genera exhibit the reverse regression line trends. Thus, fecal microbiota and VOC profiles and their associations in NA versus HRA individuals indicate the significance of multiple levels of analysis towards the identification of testable CRC risk biomarkers.

Microbiome diversity in African American, European American, and Egyptian colorectal cancer patients

Purpose

Although there is an established role for microbiome dysbiosis in the pathobiology of colorectal cancer (CRC), CRC patients of various race/ethnicities demonstrate distinct clinical behaviors. Thus, we investigated microbiome dysbiosis in Egyptian, African American (AA), and European American (EA) CRC patients.

Patients and methods

CRCs and their corresponding normal tissues from Egyptian (n = 17) patients of the Alexandria University Hospital, Egypt, and tissues from AA (n = 18) and EA (n = 19) patients at the University of Alabama at Birmingham were collected. DNA was isolated from frozen tissues, and the microbiome composition was analyzed by 16S rRNA sequencing. Differential microbial abundance, diversity, and metabolic pathways were identified using linear discriminant analysis (LDA) effect size analyses. Additionally, we compared these profiles with our previously published microbiome data derived from Kenyan CRC patients.

Results

Differential microbiome analysis of CRCs across all racial/ethnic groups showed dysbiosis. There were high abundances of Herbaspirillum and Staphylococcus in CRCs of Egyptians, Leptotrichia in CRCs of AAs, Flexspiria and Streptococcus in CRCs of EAs, and Akkermansia muciniphila and Prevotella nigrescens in CRCs of Kenyans (LDA score >4, adj. p-value <0.05). Functional analyses showed distinct microbial metabolic pathways in CRCs compared to normal tissues within the racial/ethnic groups. Egyptian CRCs, compared to normal tissues, showed lower l-methionine biosynthesis and higher galactose degradation pathways.

Conclusions

Our findings showed altered mucosa-associated microbiome profiles of CRCs and their metabolic pathways across racial/ethnic groups. These findings provide a basis for future studies to link racial/ethnic microbiome differences with distinct clinical behaviors in CRC.

Integrating Microbiome Analysis, Metabolomics, Bioinformatics, and Histopathology to Elucidate the Protective Effects of Pomegranate Juice against Benzo-alpha-pyrene-Induced Colon Pathologies

Polycyclic aromatic hydrocarbons, e.g., benzo[a]pyrene (BaP), are common dietary pollutants with potential carcinogenic activity, while polyphenols are potential chemopreventive antioxidants. Although several health benefits are attributed to polyphenol-rich pomegranate, little is known about its interaction with BaP. This study integrates histochemical, microbiomic, and metabolomic approaches to investigate the protective effects of pomegranate juice from BaP-induced pathologies. To this end, 48 Sprague-Dawley rats received, for four weeks, either pomegranate, BaP, both, or neither (n = 12 rats per group). Whereas histochemical examination of the colon indicated tissue damage marked by mucin depletion in BaP-fed animals, which was partially restored by administration of pomegranate juice, the fecal microbiome and metabolome retained their resilience, except for key changes related to pomegranate and BaP biotransformation. Meanwhile, dramatic microbiome restructuring and metabolome shift were observed as a consequence of the elapsed time (age factor). Additionally, the analysis allowed a thorough examination of fecal microbiome-metabolome associations, which delineated six microbiome clusters (marked by a differential abundance of Lactobacillaceae and Prevotellaceae, Rumincococcaceae, and Erysipelotrichaceae) and two major metabolome clusters (a sugar- and amino-acids-dominated metabotype vs. a cluster of fatty acids and hydrocarbons), with sugar alcohols maintaining a unique signature. In conclusion, using paired comparisons to minimize inter-individual animal variations allowed the dissection of temporal vs. treatment-derived variations. Microbiome-metabolome association clusters may be further exploited for metabotype prediction and gut-health biomarker discovery.

Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction

OBJECTIVE

The relationship between intestinal microbiome and colorectal cancer (CRC) progression is unclear. This study aims to identify the intestinal microbiome associated with CRC progression and construct predictive labels to support the accurate assessment and treatment of CRC.

METHOD

The 192 patients included in the study were divided into stage I-II and stage III-IV CRC patients according to the pathological stages, and preoperative stools were collected from both groups for 16S rDNA sequencing of the intestinal microbiota. Pearson correlation and Spearman correlation coefficient analysis were used to analyze the differential intestinal microbiome and the correlation with tumor microenvironment and to predict the functional pathway. XGBoost model (XGB) and Random Forest model (RF) were used to construct the microbiome-based signature. The total RNA extraction from 17 CRC tumor simples was used for transcriptome sequencing.

RESULT

The Simpson index of intestinal microbiome in stage III-IV CRC were significantly lower than those in stage I-II CRC. Proteus, Parabacteroides, Alistipes and Ruminococcus etc. are significantly enriched genus in feces of CRC patients with stage III-IV. ko00514: Other types of O - glycan biosynthesis pathway is relevant with CRC progression. Alistipes indistinctus was positively correlated with mast cells, immune activators IL-6 and IL6R, and GOBP_PROTEIN_FOLDING_IN_ENDOPLASMIC_RETICULUM dominantly. The Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed with 42 CRC progression-associated differential bacteria were effective in distinguishing CRC patients between stage I-II and stage III-IV.

CONCLUSIONS

The abundance and diversity of intestinal microbiome may increase gradually with the occurrence and progression of CRC. Elevated fetal abundance of Proteus, Parabacteroides, Alistipes and Ruminococcus may contribute to CRC progression. Enhanced synthesis of O - glycans may result in CRC progression. Alistipes indistinctus may play a facilitated role in mast cell maturation by boosting IL-6 production. Alistipes indistinctus may work in the correct folding of endoplasmic reticulum proteins in CRC, reducing ER stress and prompting the survival and deterioration of CRC, which may owe to the enhanced PERK expression and activation of downstream UPR by Alistipes indistinctus. The CRC progression-associated differential intestinal microbiome identified in our study can be served as potential microbial markers for CRC staging prediction.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Probiotics therapy show significant improvement in obesity and neurobehavioral disorders symptoms

Obesity is a complex metabolic disease, with cognitive impairment being an essential complication. Gut microbiota differs markedly between individuals with and without obesity. The microbial-gut-brain axis is an important pathway through which metabolic factors, such as obesity, affect the brain. Probiotics have been shown to alleviate symptoms associated with obesity and neurobehavioral disorders. In this review, we evaluated previously published studies on the effectiveness of probiotic interventions in reducing cognitive impairment, depression, and anxiety associated with obesity or a high-fat diet. Most of the probiotics studied have beneficial health effects on obesity-induced cognitive impairment and anxiety. They positively affect immune regulation, the hypothalamic-pituitary-adrenal axis, hippocampal function, intestinal mucosa protection, and glucolipid metabolism regulation. Probiotics can influence changes in the composition of the gut microbiota and the ratio between various flora. However, probiotics should be used with caution, particularly in healthy individuals. Future research should further explore the mechanisms underlying the gut-brain axis, obesity, and cognitive function while overcoming the significant variation in study design and high risk of bias in the current evidence.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn's disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn's disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

Investigation of the gut microbiome, bile acid composition and host immunoinflammatory response in a model of azoxymethane-induced colon cancer at discrete timepoints

BACKGROUND

Distinct sets of microbes contribute to colorectal cancer (CRC) initiation and progression. Some occur due to the evolving intestinal environment but may not contribute to disease. In contrast, others may play an important role at particular times during the tumorigenic process. Here, we describe changes in the microbiota and host over the course of azoxymethane (AOM)-induced tumorigenesis.

METHODS

Mice were administered AOM or PBS and were euthanised 8, 12, 24 and 48 weeks later. Samples were analysed using 16S rRNA gene sequencing, UPLC-MS and qRT-PCR.

RESULTS

The microbiota and bile acid profile showed distinct changes at each timepoint. The inflammatory response became apparent at weeks 12 and 24. Moreover, significant correlations between individual taxa, cytokines and bile acids were detected. One co-abundance group (CAG) differed significantly between PBS- and AOM-treated mice at week 24. Correlation analysis also revealed significant associations between CAGs, bile acids and the bile acid transporter, ASBT. Aberrant crypt foci and adenomas were first detectable at weeks 24 and 48, respectively.

CONCLUSION

The observed changes precede host hyperplastic transformation and may represent early therapeutic targets for the prevention or management of CRC at specific timepoints in the tumorigenic process.

The impact of bariatric surgery on colorectal cancer risk

Obesity is considered a risk factor for different types of cancer, including colorectal cancer (CRC). Bariatric surgery has been associated with improvements in obesity-related co-morbidities and reductions in overall cancer risk. However, given the contradictory outcomes of several cohort studies, the impact of bariatric surgery on CRC risk appears controversial. Furthermore, measurement of CRC biomarkers following Roux-en-Y gastric bypass (RYGB) has revealed hyperproliferation and increased pro-inflammatory gene expression in the rectal mucosa. The proposed mechanisms leading to increased CRC risk are alterations of the gut microbiota and exposure of the colorectum to high concentrations of bile acids, both of which are caused by RYGB-induced anatomical rearrangements. Studies in animals and humans have highlighted the similarities between RYGB-induced microbial profiles and the gut microbiota documented in CRC. Microbial alterations common to post-RYGB cases and CRC include the enrichment of pro-inflammatory microbes and reduction in butyrate-producing bacteria. Lower concentrations of butyrate following RYGB may also contribute to an increased risk of CRC, given the anti-inflammatory and anticarcinogenic properties of this molecule. Laparoscopic sleeve gastrectomy appears to have a more moderate impact than RYGB; however, relatively few animal and human studies have investigated its effects on CRC risk. Moreover, evidence regarding the impact of anastomosis gastric bypass on one is even more limited. Therefore, further studies are required to establish whether the potential increase in CRC risk is restricted to RYGB or may also be associated with other bariatric procedures.

Changes in the Gut Microbiome Associated with Intussusception in Patients with Peutz-Jeghers Syndrome

Peutz-Jeghers syndrome (PJS) is a rare hereditary disorder characterized by intestinal polyposis, and intestinal intussusception is one of the most urgent complications. While it is known that imbalance of the gut microbiota is highly associated with intestinal disorders, the role of the gut microbiome in the pathogenesis of PJS has not been reported. In this study, we performed 16S rRNA sequencing on stools from 168 patients and 68 healthy family members who lived together to determine the gut microbiome composition of PJS patients. Metagenomics sequencing was further performed on the representative samples (61 PJS patients and 27 healthy family members) to analyze the functional changes. We found that the fecal microbiome of patients with PJS showed a greater variation in β-diversity. An enhancement of Escherichia coli and a reduction of Faecalibacterium prausnitzii was identified in PJS patients. Further reduction of Faecalibacterium prausnitzii was the characteristic microbial change observed in patients with intussusception. Functional analysis revealed that the abundance of propanoate metabolism was enriched in PJS patients and further enriched in those with intussusception. Escherichia coli was the major contributor to the enrichment of this metabolism pathway, which was associated with the abnormal expression of methylglyoxal synthase (encoded by mgsA) and phosphate acetyltransferase (encoded by pta). Our findings showed a distinct gut microbiome signature in PJS patients and identified the connection between the gut microbiome and intussusception. Alterations in the gut microbiome might be involved in the pathogenesis of PJS and may serve as biomarkers for gastrointestinal surveillance. IMPORTANCE Recent research has established a link between the gut microbiome and polyps and neoplasia, and antibiotic use influences the microbiome and the development of colorectal polyps. Familial adenomatous polyposis (FAP), which is characterized by the early development of benign precursor lesions (polyps), is associated with enterotoxigenic Bacteroides fragilis and Escherichia coli biofilms. However, the relationship between the gut microbiome and the pathophysiology of PJS has not yet been established. In this study, we found that PJS patients had a distinct microbiome composition, with a greater variation in β-diversity, an increase in Escherichia coli, and a decrease in Faecalibacterium prausnitzii. A further reduction of Faecalibacterium prausnitzii was observed in patients with intussusception. Moreover, PJS involved increased propanoate metabolism as well as abnormal mgsA and pta expression. These findings may contribute to a better understanding of the etiology of PJS and improve disease control strategies.

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (T_CRC) and normal paired tissues (T_HC) along with 10 matched fecal samples (F_CRC) and 10 healthy controls (F_HC). The highest microbial phyla abundance from T_HC and T_CRC were Firmicutes, while the dominant phyla from F_HC and F_CRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in T_HC than in T_CRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in F_HC than in F_CRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between T_HC and T_CRC groups and 700 metabolites different between F_HC and F_CRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Excessive consumption of mucin by over-colonized Akkermansia muciniphila promotes intestinal barrier damage during malignant intestinal environment

Gut microbiota disorders damage the intestinal barrier, which causes intestinal disease. Thus, we screened the microbiota with significant changes using an in situ malignant colorectal cancer (CRC) model. Among the colonies with increased abundance, Akkermansia muciniphila (A. muciniphila) is known for its characteristic of breaking down mucin, which is an essential component of the intestinal barrier. The role of A. muciniphila remains controversial. To investigate the effect of excess A. muciniphila on the intestinal barrier, we established an over-colonized A. muciniphila mouse model by administering a live bacterial suspension after disrupting the original gut microbiome with antibiotics. The results showed that over-colonization of A. muciniphila decreased intestinal mucin content. The mRNA and protein expression levels of tight junction proteins also decreased significantly in the over-colonized A. muciniphila mouse model. Our findings reveal that excess colonization by A. muciniphila breaks the dynamic balance between mucin secretion and degradation, reduces the thickness of the intestinal mucus layer, and damages the intestinal barrier, which would eventually aggravate the development of colitis and CRC. These results will raise awareness about the safety of A. muciniphila serving as a probiotic.

A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.

Tumor tissue-specific bacterial biomarker panel for colorectal cancer: Bacteroides massiliensis, Alistipes species, Alistipes onderdonkii, Bifidobacterium pseudocatenulatum, Corynebacterium appendicis

Human microbiome studies have shown diversity to exist among different ethnic populations. However, studies pertaining to the microbial composition of CRC among the Indian population have not been well explored. We aimed to decipher the microbial signature in tumor tissues from North Indian CRC patients. Next-generation sequencing of tumor and adjacent tissue-derived bacterial 16S rRNA V3-V4 hypervariable regions was performed to investigate the abundance of specific microbes. The expression profile analysis deciphered a decreased diversity among the tumor-associated microbial communities. At the phyla level, Proteobacteria was differentially expressed in CRC tissues than adjacent normal. Further, DeSeq2 normalization identified 4 out of 79 distinct species (p < 0.005) only in CRC, Bacteroides massiliensis, Alistipes onderdonkii, Bifidobacterium pseudocatenulatum, and Corynebacterium appendicis. Thus, the findings suggest that microbial signatures can be used as putative biomarkers in diagnosis, prognosis and treatment management of CRC.

Cancer-Associated Microbiota: From Mechanisms of Disease Causation to Microbiota-Centric Anti-Cancer Approaches

Helicobacter pylori infection is the only well-established bacterial cause of cancer. However, due to the integral role of tissue-resident commensals in maintaining tissue-specific immunometabolic homeostasis, accumulated evidence suggests that an imbalance of tissue-resident microbiota that are otherwise considered as commensals, can also promote various types of cancers. Therefore, the present review discusses compelling evidence linking tissue-resident microbiota (especially gut bacteria) with cancer initiation and progression. Experimental evidence supporting the cancer-causing role of gut commensal through the modulation of host-specific processes (e.g., bile acid metabolism, hormonal effects) or by direct DNA damage and toxicity has been discussed. The opportunistic role of commensal through pathoadaptive mutation and overcoming colonization resistance is discussed, and how chronic inflammation triggered by microbiota could be an intermediate in cancer-causing infections has been discussed. Finally, we discuss microbiota-centric strategies, including fecal microbiota transplantation, proven to be beneficial in preventing and treating cancers. Collectively, this review provides a comprehensive understanding of the role of tissue-resident microbiota, their cancer-promoting potentials, and how beneficial bacteria can be used against cancers.

The role of gut microbiota in patients with benign and malignant brain tumors: a pilot study

Gut microbiota is associated with the growth of various tumors, including malignant gliomas, through the brain-gut axis. Moreover, the gut microbiota in patients with malignant tumors may considerably differ from those with benign tumors. However, the associations of gut microbiota with benign and malignant brain tumors remain unclear. Hence, in order to explore these underlying relationships, patients with benign meningioma (n = 32), malignant glioma (n = 27), and healthy individuals (n = 41) were selected to participate in this study. The results showed that the diversity of the microbial ecosystem in brain tumor patients were less than the healthy controls, while no significant differences were observed between the meningioma and glioma groups. The microbial composition also differed significantly between individuals with brain tumors and healthy participants. In meningioma group, pathogenic bacteria like Enterobacteriaceae were increased, whereas certain carcinogenic bacteria were overrepresented in the glioma group, including Fusobacterium and Akkermansia. Furthermore, benign and malignant brain tumor patients lacked SCFA-producing probiotics. Thus, a microbial biomarker panel including Fusobacterium, Akkermansia, Escherichia/Shigella, Lachnospira, Agathobacter, and Bifidobacterium was established. Diagnostic models confirmed that this panel could distinguish between brain tumor patients and healthy patients. Additionally, gut microbiota can affect the differentiation and proliferation of brain tumors via several metabolic pathways based on annotations from the Kyoto Encyclopedia of Genes and Genomes (KEGG). This is the first study designed to investigate whether gut microbiota differs between benign and malignant brain tumor patients, and our work concluded that intestinal flora is a valuable tool for the diagnosis and treatment of brain tumors.