Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

The Effect of Gender on the Intestinal Flora of Colorectal Cancer Under Different Stages

This study aims to determine whether gender is a factor in the interplay between the human intestinal flora and colorectal cancer (CRC), ultimately providing new evidence for the clinical prediction and management of CRC in different genders. In this study, we included 186 untreated CRC patients, and classified them into two groups based on pathological staging: Groups Ⅰ-Ⅱ and Groups Ⅲ-Ⅳ, with male and female groups within each group. We collected preoperative fecal samples from these patients and performed 16S rRNA gene sequencing to analyze their intestinal flora. In the CRC Stages I-II cohort, the gut microbiota of the female group exhibited greater diversity and abundance compared to the male group, with a total of 13 gut microbiota demonstrating significant disparities. Notably, s__Parabacteroides gordonii, s__Bacteroides faecis, and s__Bacteroides nordii were found to be more prevalent in the female group relative to the male group. Within the CRC Stages III-IV cohort, 51 gut microbiota exhibited significant differences between the genders. In the immunocyte composition of fecal samples from patients with CRC, a higher proportion of naive B cells is observed in the male group as compared to the female group. In female CRC patients within the CRC Stages III-IV cohort, Actinomyces exhibited a significant negative correlation with activated dendritic cells, CD4+ memory T cells, and eosinophils. In male CRC patients within the CRC Stages III-IV cohort, Actinomyces demonstrated a significant positive correlation with naive B cells and a significant positive correlation with immune activation genes TNFRSF25 and TMIGD2. In female CRC patients within the CRC Stages III-IV cohort, Actinomyces showed a significant negative correlation with activated dendritic cells, CD4+ memory T cells, and eosinophils, and a significant positive correlation with immune activation genes TNFSF13B, LTA, KLRK1, and CXCL12. In the CRC Stages I-II group, the female group's intestinal flora is more diverse and richer than the male group. In the CRC Stages III-IV group, there are a total of 51 different intestinal flora in both the male and female groups. We also found that Actinomyces affects the occurrence and development of CRC in the male and female groups through different pathways. The results show that the intestinal flora differs between male and female CRC patients and is closely associated with cancer development.

Prediction of Crohn's disease based on deep feature recognition

BACKGROUND

Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction.

METHODS

In this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method.

RESULTS

Data augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement.

CONCLUSIONS

In summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Interaction of microbiota, mucosal malignancies, and immunotherapy-Mechanistic insights

The microbiome has emerged as a crucial modulator of host-immune interactions and clearly impacts tumor development and therapy efficacy. The microbiome is a double-edged sword in cancer development and therapy as both pro-tumorigenic and anti-tumorigenic bacterial taxa have been identified. The staggering number of association-based studies in various tumor types has led to an enormous amount of data that makes it difficult to identify bacteria that promote tumor development or modulate therapy efficacy from bystander bacteria. Here we aim to comprehensively summarize the current knowledge of microbiome-host immunity interactions and cancer therapy in various mucosal tissues to find commonalities and thus identify potential functionally relevant bacterial taxa. Moreover, we also review recent studies identifying specific bacteria and mechanisms through which the microbiome modulates cancer development and therapy efficacy.

The Role of Lung Microbiome in Fibrotic Interstitial Lung Disease-A Systematic Review

Interstitial Lung Disease (ILD) involves lung disorders marked by chronic inflammation and fibrosis. ILDs include pathologies like idiopathic pulmonary fibrosis (IPF), connective tissue disease-associated ILD (CTD-ILD), hypersensitivity pneumonitis (HP) or sarcoidosis. Existing data covers pathogenesis, diagnosis (especially using high-resolution computed tomography), and treatments like antifibrotic agents. Despite progress, ILD diagnosis and management remains challenging with significant morbidity and mortality. Recent focus is on Progressive Fibrosing ILD (PF-ILD), characterized by worsening symptoms and fibrosis on HRCT. Prevalence is around 30%, excluding IPF, with a poor prognosis. Early diagnosis is crucial for optimizing outcomes in PF-ILD individuals. The lung microbiome comprises all the microorganisms that are in the respiratory tract. Relatively recent research try to evaluate its role in respiratory disease. Healthy lungs have a diverse microbial community. An imbalance in bacterial composition, changes in bacterial metabolic activities, or changes in bacterial distribution within the lung termed dysbiosis is linked to conditions like COPD, asthma and ILDs. We conducted a systematic review of three important scientific data base using a focused search strategy to see how the lung microbiome is involved in the progression of ILDs. Results showed that some differences in the composition and quality of the lung microbiome exist in ILDs that show progressive fibrosing phenotype. The results seem to suggest that the lung microbiota could be involved in ILD progression, but more studies showing its exact pathophysiological mechanisms are needed.

Mucosal Microbiota from Colorectal Cancer, Adenoma and Normal Epithelium Reveals the Imprint of Fusobacterium nucleatum in Cancerogenesis

An increasing amount of evidence suggests the emerging role of the gut microbiota in the development of colorectal cancer (CRC). This study aimed to elucidate the architecture of microbial communities within normal and neoplastic colonic mucosa.

METHODS

Microbiota were analyzed by NGS and by an ensemble of metagenomics analysis tools in a total of 69 tissues from 9 patients with synchronous colorectal neoplasia and adenomas (27 specimens: 9 from normal tissues, 9 adenomas, and 9 tumours), 16 patients with only colonic adenomas (32 specimens: 16 from normal tissues and 16 adenomas), and from healthy subjects (10 specimens of normal mucosa).

RESULTS

Weak differences were observed in alpha and beta metrics among the synchronous tissues from CRC and controls. Through pairwise differential abundance analyses of sample groups, an increasing trend of Rikenellaceae, Pseudomonas and Fusobacterium, and decreasing trends of Staphylococcus, Actinobacillus and Gemmiger were observed in CRC, while Staphylococcus and Bifidobacterium were decreased in patients with only adenomas. At RT-qPCR analysis, Fusobacterium nucleatum was significantly enriched in all the tissues of subjects with synchronous colorectal neoplasia.

CONCLUSION

Our findings provide a comprehensive view of the human mucosa-associated gut microbiota, emphasizing global microbial diversity mostly in synchronous lesions and proving the constant presence of Fusobacterium nucleatum, with its ability to drive carcinogenesis.

Why Does Obesity as an Inflammatory Condition Predispose to Colorectal Cancer?

Obesity is a complex and multifactorial problem of global importance. Additionally, obesity causes chronic inflammation, upregulates cell growth, disturbs the immune system, and causes genomic instability, increasing the risk of carcinogenesis. Colorectal cancer is one of the most common cancers, and it has become a global problem. In 2018, there were around 1.8 million new cases and around 881,000 deaths worldwide. Another risk factor of colorectal cancer associated with obesity is poor diet. A Western diet, including a high intake of red and processed meat and a low consumption of whole grains, fruits, vegetables, and fiber, may increase the risk of both colorectal cancer and obesity. Moreover, the Western diet is associated with a proinflammatory profile diet, which may also affect chronic low-grade inflammation. In fact, people with obesity often present gut dysbiosis, increased inflammation, and risk of colorectal cancer. In this article, the association between obesity and colorectal cancer is discussed, including the most important mechanisms, such as low-grade chronic inflammation, gut dysbiosis, and poor diet.

Identifying distinctive tissue and fecal microbial signatures and the tumor-promoting effects of deoxycholic acid on breast cancer

Introduction

A growing body of evidence indicates that the dysbiosis of both mammary and intestinal microbiota is associated with the initiation and progression of breast tumors. However, the microbial characteristics of patients with breast tumors vary widely across studies, and replicable biomarkers for early-stage breast tumor diagnosis remain elusive.

Methods

We demonstrate a machine learning-based method for the analysis of breast tissue and gut microbial differences among patients with benign breast disease, patients with breast cancer (BC), and healthy individuals using 16S rRNA sequence data retrieved from eight studies. QIIME 2.0 and R software (version 3.6.1) were used for consistent processing. A naive Bayes classifier was trained on the RDP v16 reference database to assign taxonomy using the Vsearch software.

Results

After re-analyzing with a total of 768 breast tissue samples and 1,311 fecal samples, we confirmed that Halomonas and Shewanella were the most representative genera of BC tissue. Bacteroides are frequently and significantly enriched in the intestines of patients with breast tumor. The areas under the curve (AUCs) of random forest models were 74.27% and 68.08% for breast carcinoma tissues and stool samples, respectively. The model was validated for effectiveness via cohort-to-cohort transfer (average AUC =0.65) and leave-one-cohort-out (average AUC = 0.66). The same BC-associated biomarker Clostridium_XlVa exists in the tissues and the gut. The results of the in-vitro experiments showed that the Clostridium-specific-related metabolite deoxycholic acid (DCA) promotes the proliferation of HER2-positive BC cells and stimulates G0/G1 phase cells to enter the S phase, which may be related to the activation of peptide-O-fucosyltransferase activity functions and the neuroactive ligand-receptor interaction pathway.

Discussion

The results of this study will improve our understanding of the microbial profile of breast tumors. Changes in the microbial population may be present in both the tissues and the gut of patients with BC, and specific markers could aid in the early diagnosis of BC. The findings from in-vitro experiments confirmed that Clostridium-specific metabolite DCA promotes the proliferation of BC cells. We propose the use of stool-based biomarkers in clinical application as a non-invasive and convenient diagnostic method.

The human oral - nasopharynx microbiome as a risk screening tool for nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a common head and neck cancer with a poor prognosis. There is an urgent need to develop a simple and convenient screening tool for early detection and risk screening of NPC. 139 microbial samples were collected from 40 healthy people and 39 patients with nasopharyngeal biopsy. A total of 40 and 39 oral, eight and 27 nasal cavity, nine and 16 nasopharyngeal microbial samples were collected from the two sets of individuals. A risk screening tool for NPC was established by 16S rDNA sequencing and random forest. Patients with nasopharyngeal biopsy had significantly lower nasal cavity and nasopharynx microbial diversities than healthy people. The beta diversity of the oral microbiome was significantly different between the two groups. The NPC screening tools based on nasopharyngeal and oral microbiomes have 88% and 77.2% accuracies, respectively. The nasopharyngeal biopsy patients had significantly higher Granulicatella abundance in their oral cavity and lower Pseudomonas and Acinetobacter in the nasopharynx than healthy people. This study established microbiome-based non-invasive, simple, no radiation, and low-cost NPC screening tools. Individuals at a high risk of NPC should be advised to seek further examination, which might improve the early detection of NPC and save public health costs.

Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure

Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection process facilitated the identification of taxons that are most predictive of nitrification and to compare habitats. We found that Nitrosomonas and Nitrospirae were more frequently identified as important predictors of nitrification in conventional systems, whereas Thaumarchaeota were more important predictors in diversified systems. Our results suggest that model performance was not substantively improved by incorporating additional time-consuming and expensive qPCR data on amoA gene abundance. We also identified several clades of nitrifiers important for nitrification in different cropping systems, though we were unable to detect system- or rhizosphere-specific patterns in OTU-level biomarkers for nitrification. Finally, our results highlight the inherent risk of combining data from disparate habitats with the goal of increasing sample size to avoid overfitting models. This study represents a step toward developing machine learning approaches for microbiome research to identify nitrifier ecotypes that may be important for distinguishing ecotypes with defining roles in different habitats.

Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes

Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Gut microbiota and age shape susceptibility to clostridial enteritis in lorikeets under human care

Background

Enteritis is a common cause of morbidity and mortality in lorikeets that can be challenging to diagnose and treat. In this study, we examine gut microbiota in two lorikeet flocks with enteritis (Columbus Zoo and Aquarium—CZA; Denver Zoo—DZ). Since 2012, the CZA flock has experienced repeated outbreaks of enteritis despite extensive diet, husbandry, and clinical modifications. In 2018, both CZA and DZ observed a spike in enteritis. Recent research has revealed that the gut microbiota can influence susceptibility to enteropathogens. We hypothesized that a dysbiosis, or alteration in the gut microbial community, was making some lorikeets more susceptible to enteritis, and our goal was to characterize this dysbiosis and determine the features that predicted susceptibility.

Results

We employed 16S rRNA sequencing to characterize the cloacal microbiota in lorikeets (CZA n = 67, DZ n = 24) over time. We compared the microbiota of healthy lorikeets, to lorikeets with enteritis, and lorikeets susceptible to enteritis, with “susceptible” being defined as healthy birds that subsequently developed enteritis. Based on sequencing data, culture, and toxin gene detection in intestinal contents, we identified Clostridium perfringens type A (CZA and DZ) and C. colinum (CZA only) at increased relative abundances in birds with enteritis. Histopathology and immunohistochemistry further identified the presence of gram-positive bacilli and C. perfringens, respectively, in the necrotizing intestinal lesions. Finally, using Random Forests and LASSO models, we identified several features (young age and the presence of Rhodococcus fascians and Pseudomonas umsongensis) associated with susceptibility to clostridial enteritis.

Conclusions

We identified C. perfringens type A and C. colinum associated with lorikeet necrohemorrhagic enteritis at CZA and DZ. Susceptibility testing of isolates lead to an updated clinical treatment plan which ultimately resolved the outbreaks at both institutions. This work provides a foundation for understanding gut microbiota features that are permissive to clostridial colonization and host factors (e.g. age, prior infection) that shape responses to infection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00148-7.

The composition of lung microbiome in lung cancer: a systematic review and meta-analysis

Background

Although recent studies have indicated that imbalance in the respiratory microbiome composition is linked to several chronic respiratory diseases, the association between the lung microbiome and lung cancer has not been extensively studied. Conflicting reports of individual studies on respiratory microbiome alterations in lung cancer complicate the matter for specifying how the lung microbiome is linked to lung cancer. Consequently, as the first meta-analysis on this topic, we integrate publicly available 16S rRNA gene sequence data on lung tissue samples of lung cancer patients to identify bacterial taxa which differ consistently between case and control groups.

Results

The findings of the current study suggest that the relative abundance of several bacterial taxa including Actinobacteria phylum, Corynebacteriaceae and Halomonadaceae families, and Corynebacterium, Lachnoanaerobaculum, and Halomonas genera is significantly decreased (p < 0.05) in lung tumor tissues of lung cancer patients in comparison with tumor-adjacent normal tissues.

Conclusions

Despite the underlying need for scrutinizing the findings further, the present study lays the groundwork for future research and adds to our limited understanding of the key role of the lung microbiome and its complex interaction with lung cancer. More data on demographic factors and tumor tissue types would help establish a greater degree of accuracy in characterizing the lung microbial community which accords with subtypes and stages of the disease and fully capturing the changes of the lung microbiome in lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02375-z.

Recent advancements in the exploitation of the gut microbiome in the diagnosis and treatment of colorectal cancer

Over the last few decades it has been established that the complex interaction between the host and the multitude of organisms that compose the intestinal microbiota plays an important role in human metabolic health and disease. Whilst there is no defined consensus on the composition of a healthy microbiome due to confounding factors such as ethnicity, geographical locations, age and sex, there are undoubtably populations of microbes that are consistently dysregulated in gut diseases including colorectal cancer (CRC). In this review, we discuss the most recent advances in the application of the gut microbiota, not just bacteria, and derived microbial compounds in the diagnosis of CRC and the potential to exploit microbes as novel agents in the management and treatment of CRC. We highlight examples of the microbiota, and their derivatives, that have the potential to become standalone diagnostic tools or be used in combination with current screening techniques to improve sensitivity and specificity for earlier CRC diagnoses and provide a perspective on their potential as biotherapeutics with translatability to clinical trials.

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Microbiome distinctions between the CRC carcinogenic pathways

Colorectal cancer (CRC) is the third most commonly diagnosed cancer, the third leading cause of cancer-related deaths, and has been on the rise among young adults in the United States. Research has established that the colonic microbiome is different in patients with CRC compared to healthy controls, but few studies have investigated if and how the microbiome may relate to CRC progression through the serrated pathway versus the adenoma-carcinoma sequence.Our view is that progress in CRC microbiome research requires consideration of how the microbiome may contribute to CRC carcinogenesis through the distinct pathways that lead to CRC, which could enable the creation of novel and tailored prevention, screening, and therapeutic interventions. We first highlight the limitations in existing CRC microbiome research and offer corresponding solutions for investigating the microbiome's role in the adenoma-carcinoma sequence and serrated pathway. We then summarize the findings in the select human studies that included data points related to the two major carcinogenic pathways. These studies investigate the microbiome in CRC carcinogenesis and 1) utilize mucosal samples and 2) compare polyps or tumors by histopathologic type, molecular/genetic type, or location in the colon.Key findings from these studies include: 1) Fusobacterium is associated with right-sided, more advanced, and serrated lesions; 2) the colons of people with CRC have bacteria typically associated with normal oral flora; and 3) colons from people with CRC have more biofilms, and these biofilms are predominantly located in the proximal colon (single study).

Changes in Gut Microbiome Associated With Co-Occurring Symptoms Development During Chemo-Radiation for Rectal Cancer: A Proof of Concept Study

PURPOSE

To examine a) whether there are significant differences in the severity of symptoms of fatigue, sleep disturbance, or depression between patients with rectal cancer who develop co-occurring symptoms and those with no symptoms before and at the end of chemotherapy and radiation therapy (CRT); b) differences in gut microbial diversity between those with co-occurring symptoms and those with no symptoms; and c) whether before-treatment diversity measurements and taxa abundances can predict co-occurrence of symptoms.

METHODS

Stool samples and symptom ratings were collected from 31 patients with rectal cancer prior to and at the end of (24-28 treatments) CRT. Descriptive statistics were computed and the Mann-Whitney U test was performed for symptoms. Gut microbiome data were analyzed using R's vegan package software.

RESULTS

Participants with co-occurring symptoms reported greater severity of fatigue at the end of CRT than those with no symptoms. Bacteroides and Blautia2 abundances differed between participants with co-occurring symptoms and those with no symptoms. Our random forest classification (unsupervised learning algorithm) predicted participants who developed co-occurring symptoms with 74% accuracy, using specific phylum, family, and genera abundances as predictors.

CONCLUSION

Our preliminary results point to an association between the gut microbiota and co-occurring symptoms in rectal cancer patients and serves as a first step in potential identification of a microbiota-based classifier.

Prediction of Postoperative Ileus in Patients With Colorectal Cancer by Preoperative Gut Microbiota

Background

Ileus and postoperative ileus (POI) are common complications of colorectal cancer (CRC). However, little is known about the gut microbiota associated with ileus.

Method

Differences in gut microbiota were evaluated by 16S rRNA gene sequencing. We characterized the gut microbiota in 85 CRC patients (cohort 1) and detected differences, and an independent cohort composed of 38 CRC patients (cohort 2) was used to evaluate the results.

Results

The gut microbiota of CRC patients with and without ileus exhibited large differences in alpha- and beta-diversities and bacterial taxa. The Firmicutes-to-Bacteroidetes ratio and microbial dysbiosis index (MDI) showed greater dysbiosis among ileus patients than among those without ileus. According to the location of CRC, the difference in gut microbiota between patients with and without ileus was more obvious in those with distal CRC than in those with proximal CRC. Finally, Faecalibacterium was significantly reduced in the postoperative perioperative period in patients with ileus. Thus, we used Faecalibacterium as a biomarker for predicting perioperative or POI: the AUC value was 0.74 for perioperative ileus and 0.67 for POI that appeared at 6 months after hospital discharge. The predictive power was evaluated in Cohort 2, with an AUC value of 0.79.

Conclusion

These findings regarding difference of gut microbiota in postoperative CRC patients may provide a theoretical basis for the use of microbiota as biomarkers for the prediction of POI.

Human Gut Microbiome-Based Knowledgebase as a Biomarker Screening Tool to Improve the Predicted Probability for Colorectal Cancer

Colorectal cancer (CRC) is a common clinical malignancy globally ranked as the fourth leading cause of cancer mortality. Some microbes are known to contribute to adenoma-carcinoma transition and possess diagnostic potential. Advances in high-throughput sequencing technology and functional studies have provided significant insights into the landscape of the gut microbiome and the fundamental roles of its components in carcinogenesis. Integration of scattered knowledge is highly beneficial for future progress. In this study, literature review and information extraction were performed, with the aim of integrating the available data resources and facilitating comparative research. A knowledgebase of the human CRC microbiome was compiled to facilitate understanding of diagnosis, and the global signatures of CRC microbes, sample types, algorithms, differential microorganisms and various panels of markers plus their diagnostic performance were evaluated based on statistical and phylogenetic analyses. Additionally, prospects about current changelings and solution strategies were outlined for identifying future research directions. This type of data integration strategy presents an effective platform for inquiry and comparison of relevant information, providing a tool for further study about CRC-related microbes and exploration of factors promoting clinical transformation (available at: http://gsbios.com/index/experimental/dts_ mben?id=1).

Association between algal productivity and phycosphere composition in an outdoor Chlorella sorokiniana reactor based on multiple longitudinal analyses

Microalgae as a biofuel source are of great interest. Bacterial phycosphere inhabitants of algal cultures are hypothesized to contribute to productivity. In this study, the bacterial composition of the Chlorella sorokiniana phycosphere was determined over several production cycles in different growing seasons by 16S rRNA gene sequencing and identification. The diversity of the phycosphere increased with time during each individual reactor run, based on Faith's phylogenetic diversity metric versus days post-inoculation (R = 0.66, P < 0.001). During summer months, Vampirovibrio chlorellavorus, an obligate predatory bacterium, was prevalent. Bacterial sequences assigned to the Rhizobiales, Betaproteobacteriales and Chitinophagales were positively associated with algal biomass productivity. Applications of the general biocide, benzalkonium chloride, to a subset of experiments intended to abate V. chlorellavorus appeared to temporarily suppress phycosphere bacterial growth, however, there was no relationship between those bacterial taxa suppressed by benzalkonium chloride and their association with algal productivity, based on multinomial model correlations. Algal health was approximated using a model-based metric, or the 'Health Index' that indicated a robust, positive relationship between C. sorokiniana fitness and presence of members belonging to the Burholderiaceae and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade. Bacterial community composition was linked to the efficiency of microalgal biomass production and algal health.

Alteration of the abundance of Parvimonas micra in the gut along the adenoma-carcinoma sequence

Parvimonas micra (P. micra) is reported to be associated with colorectal cancer (CRC). However, its association with colorectal adenoma (CRA) and its role in the initiation of colorectal tumors remain unknown. The present study aimed to clarify the relationship between P. micra and CRA and CRC by exploring the changes of P. micra abundance in an adenoma-carcinoma sequence in a new cohort and 4 public sequencing datasets. To investigate the alterations of P. micra abundance in the gut along the adenoma-carcinoma sequence, quantitative PCR (qPCR) was conducted to measure the relative abundance of P. micra in fecal samples from 277 subjects (128 patients with CRA, 66 patients with CRC and 83 healthy individuals, as controls) who underwent colonoscopy as outpatients. Then, the relative abundance of P. micra was analyzed in fecal samples from 596 subjects (185 healthy controls, 158 CRC, 253 CRA) in four public 16S rRNA sequencing datasets. The qPCR results demonstrated that the CRA group had an abundance of P. micra (P=0.2) similar to that of the healthy control group, while the CRC group had a significantly increased abundance (P=8.2×10⁻¹¹). The level of P. micra effectively discriminated patients with CRC from healthy controls, while it poorly discriminated patients with CRA from healthy controls; with an area under the receiver operating characteristic curve of 0.867 for patients with CRC and 0.554 for patients with CRA. The same pattern of the alteration of P. micra abundance, which was low in healthy controls and patients with CRA but elevated in patients with CRC, was found in all four public sequencing datasets. These results suggested that P. micra was closely associated with, and may serve as a diagnostic marker for, CRC but not CRA. Moreover, it was indicated that P. micra may be an opportunistic pathogen of CRC, which may promote CRC development but serve a limited role in tumorigenesis.

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability.IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

The promise and challenge of cancer microbiome research

Many microbial agents have been implicated as contributors to cancer genesis and development, and the search to identify and characterize new cancer-related organisms is ongoing. Modern developments in methodologies, especially culture-independent approaches, have accelerated and driven this research. Recent work has shed light on the multifaceted role that the community of organisms in and on the human body plays in cancer onset, development, detection, treatment, and outcome. Much remains to be discovered, however, as methodological variation and functional testing of statistical correlations need to be addressed for the field to advance.

Gut microbiota differences in Island Hispanic Puerto Ricans and mainland non-Hispanic whites during chemoradiation for rectal cancer: A pilot study

PURPOSE

To investigate whether there are differences in diversity, taxonomic composition, and predicted functional pathways of the gut microbiome between Island Hispanic Puerto Ricans (HPR) and mainland non-Hispanic whites (NHW) measured before and at the end of chemo-radiation (CRT) for Rectal Cancer.

METHODS

Fifty-six stool samples of newly diagnosed rectal cancer patients (25 HPR and 31 NHW) were amplicon-sequenced during chemo-radiotherapy. 16S rRNA gene data was analyzed using QIIME2, phyloseq, and LEfSe.

RESULTS

We observed similar within-sample alpha diversity for HPR and NHW participants during CRT. However, at the end of CRT, several taxa were present at significantly different abundances across both groups. Taxa enriched in the gut of HPR compared to NHW included Muribaculaceae, Prevotella 2 and 7, Gemella, Bacillales Family XI, Catenibacterium, Sutterella, Pasteurellales, and Pasteurellaceae genera, whereas over-represented taxa in NHW participants were Turicibacter and Eubacteriaceae. Significant differences in predicted HPR microbiota functions included pathways for synthesis of L-methionine and degradation of phenylethylamine and phenylacetate.

CONCLUSION

In this pilot study, taxonomic analyses and functional predictions of the gut microbiomes suggest greater inflammatory potential in gut microbial functions among HPR rectal cancer patients undergoing CRT compared to that of NHW participants.

Fusobacterium nucleatum - symbiont, opportunist and oncobacterium

Fusobacterium nucleatum has long been found to cause opportunistic infections and has recently been implicated in colorectal cancer; however, it is a common member of the oral microbiota and can have a symbiotic relationship with its hosts. To address this dissonance, we explore the diversity and niches of fusobacteria and reconsider historic fusobacterial taxonomy in the context of current technology. We also undertake a critical reappraisal of fusobacteria with a focus on F. nucleatum as a mutualist, infectious agent and oncogenic microorganism. In this Review, we delve into recent insights and future directions for fusobacterial research, including the current genetic toolkit, our evolving understanding of its mechanistic role in promoting colorectal cancer and the challenges of developing diagnostics and therapeutics for F. nucleatum.

Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer

Researchers have discovered associations between elements of the intestinal microbiome (including specific microbes, signaling pathways, and microbiota-related metabolites) and risk of colorectal cancer (CRC). However, it is unclear whether changes in the intestinal microbiome contribute to the development of sporadic CRC or result from it. Changes in the intestinal microbiome can mediate or modify the effects of environmental factors on risk of CRC. Factors that affect risk of CRC also affect the intestinal microbiome, including overweight and obesity; physical activity; and dietary intake of fiber, whole grains, and red and processed meat. These factors alter microbiome structure and function, along with the metabolic and immune pathways that mediate CRC development. We review epidemiologic and laboratory evidence for the influence of the microbiome, diet, and environmental factors on CRC incidence and outcomes. Based on these data, features of the intestinal microbiome might be used for CRC screening and modified for chemoprevention and treatment. Integrated prospective studies are urgently needed to investigate these strategies.

Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure

Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria that produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with healthy colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power.IMPORTANCE Considering that colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anticarcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentrations of a cohort of individuals with different colonic tumor burdens who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.

Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors

Colorectal cancer (CRC) ranks second in cancer-associated mortality and third in the incidence worldwide. Most of CRC follow adenoma-carcinoma sequence, and have more than 90% chance of survival if diagnosed at early stage. But the recommended screening by colonoscopy is invasive, expensive, and poorly adhered to. Recently, several studies reported that the fecal bacteria might provide non-invasive biomarkers for CRC and precancerous tumors. Therefore, we collected and uniformly re-analyzed these published fecal 16S rDNA sequencing datasets to verify the association and identify biomarkers to classify and predict colorectal tumors by random forest method. A total of 1674 samples (330 CRC, 357 advanced adenoma, 141 adenoma, and 846 control) from 7 studies were analyzed in this study. By random effects model and fixed effects model, we observed significant differences in alpha-diversity and beta-diversity between individuals with CRC and the normal colon, but not between adenoma and the normal. We identified various bacterial genera with significant odds ratios for colorectal tumors at different stages. Through building random forest model with 10-fold cross-validation as well as new test datasets, we classified individuals with CRC, advanced adenoma, adenoma and normal colon. All approaches obtained comparable performance at entire OTU level, entire genus level, and the common genus level as measured using AUC. When combined all samples, the AUC of random forest model based on 12 common genera reached 0.846 for CRC, although the predication performed poorly for advance adenoma and adenoma.

The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data

PCR amplification of 16S rRNA genes is a critical yet underappreciated step in the generation of sequence data to describe the taxonomic composition of microbial communities. Numerous factors in the design of PCR can impact the sequencing error rate, the abundance of chimeric sequences, and the degree to which the fragments in the product represent their abundance in the original sample (i.e., bias). We compared the performance of high fidelity polymerases and various numbers of rounds of amplification when amplifying a mock community and human stool samples. Although it was impossible to derive specific recommendations, we did observe general trends. Namely, using a polymerase with the highest possible fidelity and minimizing the number of rounds of PCR reduced the sequencing error rate, fraction of chimeric sequences, and bias. Evidence of bias at the sequence level was subtle and could not be ascribed to the fragments' fraction of bases that were guanines or cytosines. When analyzing mock community data, the amount that the community deviated from the expected composition increased with the number of rounds of PCR. This bias was inconsistent for human stool samples. Overall, the results underscore the difficulty of comparing sequence data that are generated by different PCR protocols. However, the results indicate that the variation in human stool samples is generally larger than that introduced by the choice of polymerase or number of rounds of PCR.IMPORTANCE A steep decline in sequencing costs drove an explosion in studies characterizing microbial communities from diverse environments. Although a significant amount of effort has gone into understanding the error profiles of DNA sequencers, little has been done to understand the downstream effects of the PCR amplification protocol. We quantified the effects of the choice of polymerase and number of PCR cycles on the quality of downstream data. We found that these choices can have a profound impact on the way that a microbial community is represented in the sequence data. The effects are relatively small compared to the variation in human stool samples; however, care should be taken to use polymerases with the highest possible fidelity and to minimize the number of rounds of PCR. These results also underscore that it is not possible to directly compare sequence data generated under different PCR conditions.

Gut microbiome meta-analysis reveals dysbiosis is independent of body mass index in predicting risk of obesity-associated CRC

Objective

Obesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese.

Design

In this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS.

Results

Characterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model.

Conclusion

Overall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

Impact of the gut microbiome on the genome and epigenome of colon epithelial cells: contributions to colorectal cancer development

In recent years, the number of studies investigating the impact of the gut microbiome in colorectal cancer (CRC) has risen sharply. As a result, we now know that various microbes (and microbial communities) are found more frequently in the stool and mucosa of individuals with CRC than healthy controls, including in the primary tumors themselves, and even in distant metastases. We also know that these microbes induce tumors in various mouse models, but we know little about how they impact colon epithelial cells (CECs) directly, or about how these interactions might lead to modifications at the genetic and epigenetic levels that trigger and propagate tumor growth. Rates of CRC are increasing in younger individuals, and CRC remains the second most frequent cause of cancer-related deaths globally. Hence, a more in-depth understanding of the role that gut microbes play in CRC is needed. Here, we review recent advances in understanding the impact of gut microbes on the genome and epigenome of CECs, as it relates to CRC. Overall, numerous studies in the past few years have definitively shown that gut microbes exert distinct impacts on DNA damage, DNA methylation, chromatin structure and non-coding RNA expression in CECs. Some of the genes and pathways that are altered by gut microbes relate to CRC development, particularly those involved in cell proliferation and WNT signaling. We need to implement more standardized analysis strategies, collate data from multiple studies, and utilize CRC mouse models to better assess these effects, understand their functional relevance, and leverage this information to improve patient care.

Integrating tumor genomics into studies of the microbiome in colorectal cancer

Although the gut microbiome has been linked to colorectal cancer (CRC) development, associations of microbial taxa with CRC status are often inconsistent across studies. We have recently shown that tumor genomics, a factor that is rarely incorporated in analyses of the CRC microbiome, has a strong effect on the composition of the microbiota. Here, we discuss these results in the wider context of studies characterizing interaction between host genetics and the microbiome, and describe the implications of our findings for understanding the role of the microbiome in CRC.

Re-purposing 16S rRNA gene sequence data from within case paired tumor biopsy and tumor-adjacent biopsy or fecal samples to identify microbial markers for colorectal cancer

Microbes colonizing colorectal cancer (CRC) tumors have the potential to affect disease, and vice-versa. The manner in which they differ from microbes in physically adjacent tissue or stool within the case in terms of both, taxonomy and biological activity remains unclear. In this study, we systematically analyzed previously published 16S rRNA sequence data from CRC patients with matched tumor:tumor-adjacent biopsies (n = 294 pairs, n = 588 biospecimens) and matched tumor biopsy:fecal pairs (n = 42 pairs, n = 84 biospecimens). Procrustes analyses, random effects regression, random forest (RF) modeling, and inferred functional pathway analyses were conducted to assess community similarity and microbial diversity across heterogeneous patient groups and studies. Our results corroborate previously reported association of increased Fusobacterium with tumor biopsies. Parvimonas and Streptococcus abundances were also elevated while Faecalibacterium and Ruminococcaceae abundances decreased in tumors relative to tumor-adjacent biopsies and stool samples from the same case. With the exception of these limited taxa, the majority of findings from individual studies were not confirmed by other 16S rRNA gene-based datasets. RF models comparing tumor and tumor-adjacent specimens yielded an area under curve (AUC) of 64.3%, and models of tumor biopsies versus fecal specimens exhibited an AUC of 82.5%. Although some taxa were shared between fecal and tumor samples, their relative abundances varied substantially. Inferred functional analysis identified potential differences in branched amino acid and lipid metabolism. Microbial markers that reliably occur in tumor tissue can have implications for microbiome based and microbiome targeting therapeutics for CRC.