Colorectal Cancer and the Human Gut Microbiome: Reproducibility with Whole-Genome Shotgun Sequencing

Metagenomic analysis of the fecal microbiome in colorectal cancer patients compared to healthy controls as a function of age

BACKGROUND AND AIMS

Colorectal cancer (CRC) incidence is increasing in young patients without a clear etiology. Emerging data have implicated the fecal microbiome in CRC carcinogenesis. However, its impact on young onset CRC is poorly defined.

METHODS

We performed a meta-analysis of fecal metagenomics sequencing data from n = 692 patients with CRC and n = 602 healthy controls from eleven studies to evaluate features of the fecal metagenome associated with CRC. We hypothesized that known carcinogenic virulence factors (colibactin, fadA) and species abundance may be differentially enriched in young CRC patients relative to older CRC patients and controls.

RESULTS

Summary odds ratios (OR) for CRC were increased with the presence of colibactin (OR 1.92 95% CI 1.08-3.38), fadA (OR 4.57 95% CI 1.63-12.85), and F. nucleatum (OR 6.93 95% CI 3.01-15.96) in meta-analysis models adjusted for age, gender, and body mass index. The OR for CRC for the presence of E.coli was 2.02 (0.92-4.45). An increase in the prevalence of Fusobacterium nucleatum (OR = 1.40 [1.18; 1.65]) and Escherichia coli (OR = 1.14 [1.02; 1.28]) per 10-year increase in age was observed in models including samples from both CRC and healthy controls. Species relative abundance was differentially enriched in young CRC patients for five species-Intestinimonas butyriciproducens, Holdemania filiformis, Firimicutues bacterium CAG 83, Bilophilia wadsworthia, and Alistipes putredinis.

CONCLUSION

In this study, we observed strong associations with CRC status for colibactin, fadA, and Fusobacterium nucleatum with CRC relative to controls. In addition, we identified several microbial species differentially enriched in young colorectal cancer patients. Studies targeting the young CRC patients are warranted to elucidate underlying preclinical mechanisms.

Uncovering 1058 Novel Human Enteric DNA Viruses Through Deep Long-Read Third-Generation Sequencing and Their Clinical Impact

BACKGROUND & AIMS

Lack of viral reference genomes poses a challenge to virome study. We investigated human gut virome and its clinical implication by ultra-deep metagenomic sequencing.

METHODS

We extracted sufficient viral DNA from human feces for ultra-deep PacBio sequencing (>10 μg) and Illumina sequencing (>1 μg). Upon de novo assembly and 6 stages of strict filtering, viral genomes were generated and validated in 3 cohorts of 2819 published fecal metagenomes. Diagnostic performance of assembled viruses for colorectal cancer were tested in a training cohort and 2 independent validation cohorts. Virus mapping ratio, evolutionary history, and virus status (lytic or temperate) were also examined.

RESULTS

The mean amount of extracted viral DNA increased by 14-fold compared with previous protocols. We obtained PacBio long reads and Illumina short reads with 290-fold higher depth than previous studies. We assembled and validated 1178 contigs as complete viral genomes, of which 1058 were newly identified. Thirteen viral genomes (398-839 kb) that are longer than the largest bacteriophage found in humans (393 kb) were discovered. Phylogenetic tree was constructed based on Hidden Markov Models alignment scores of 4 conserved viral proteins. Incorporating our assembled genomes into the National Center for Biotechnology Information database improved the mapping ratio of published metagenomes ≤18 times. Lytic viruses (75.9% ± 12.2% of total) were predominantly present in our sample. A biomarker panel of 14 novel viruses could discriminate patients with colorectal cancer from controls with an area under the receiver operating characteristics curve of 0.87 in the training cohort, which was validated with areas under the receiver operating characteristics curve of 0.85 and 0.73 in 2 independent cohorts.

CONCLUSIONS

We uncovered 1058 novel human gut viruses. These findings can contribute to clinical diagnosis, current viral reference genome, and future virome investigation.

Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes

The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78−0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66−0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41−0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.

Identification of clinical and ecological determinants of strain engraftment after fecal microbiota transplantation using metagenomics

Fecal microbiota transplantation (FMT) is a promising therapeutic approach for microbiota-associated pathologies, but our understanding of the post-FMT microbiome assembly process and its ecological and clinical determinants is incomplete. Here we perform a comprehensive fecal metagenome analysis of 14 FMT trials, involving five pathologies and >250 individuals, and determine the origins of strains in patients after FMT. Independently of the underlying clinical condition, conspecific coexistence of donor and recipient strains after FMT is uncommon and donor strain engraftment is strongly positively correlated with pre-FMT recipient microbiota dysbiosis. Donor strain engraftment was enhanced through antibiotic pretreatment and bowel lavage and dependent on donor and recipient ɑ-diversity; strains from relatively abundant species were more likely and from predicted oral, oxygen-tolerant, and gram-positive species less likely to engraft. We introduce a general mechanistic framework for post-FMT microbiome assembly in alignment with ecological theory, which can guide development of optimized, more targeted, and personalized FMT therapies.

A faecal microbiota signature with high specificity for pancreatic cancer

BACKGROUND

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

OBJECTIVE

To explore the faecal and salivary microbiota as potential diagnostic biomarkers.

METHODS

We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase.

RESULTS

Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.

CONCLUSION

Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Comparison Between Full-Length 16S rRNA Metabarcoding and Whole Metagenome Sequencing Suggests the Use of Either Is Suitable for Large-Scale Microbiome Studies

Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including Acinetobacter, Bacillus, and Escherichia genera, highlights the possible application of both methods for different purposes. This study allowed the first comparison of FL-16S rRNA sequencing and WMS to investigate the microbial composition of a food-related matrix, pointing out the advantageous use of FL-16S rRNA to identify dominant microorganisms and the superior power of WMS for the taxonomic detection of low abundant microorganisms and to perform functional analysis of the microbial communities.

Meta-Analysis of Altered Gut Microbiota Reveals Microbial and Metabolic Biomarkers for Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. The dysbiotic gut microbiota and its metabolite secretions play a significant role in CRC development and progression. In this study, we identified microbial and metabolic biomarkers applicable to CRC using a meta-analysis of metagenomic datasets from diverse geographical regions. We used LEfSe, random forest (RF), and co-occurrence network methods to identify microbial biomarkers. Geographic dataset-specific markers were identified and evaluated using area under the ROC curve (AUC) scores and random effect size. Co-occurrence networks analysis showed a reduction in the overall microbial associations and the presence of oral pathogenic microbial clusters in CRC networks. Analysis of predicted metabolites from CRC datasets showed the enrichment of amino acids, cadaverine, and creatine in CRC, which were positively correlated with CRC-associated microbes (Peptostreptococcus stomatis, Gemella morbillorum, Bacteroides fragilis, Parvimonas spp., Fusobacterium nucleatum, Solobacterium moorei, and Clostridium symbiosum), and negatively correlated with control-associated microbes. Conversely, butyrate, nicotinamide, choline, tryptophan, and 2-hydroxybutanoic acid showed positive correlations with control-associated microbes (P < 0.05). Overall, our study identified a set of global CRC biomarkers that are reproducible across geographic regions. We also reported significant differential metabolites and microbe-metabolite interactions associated with CRC. This study provided significant insights for further investigations leading to the development of noninvasive CRC diagnostic tools and therapeutic interventions.

IMPORTANCE Several studies showed associations between gut dysbiosis and CRC. Yet, the results are not conclusive due to cohort-specific associations that are influenced by genomic, dietary, and environmental stimuli and associated reproducibility issues with various analysis approaches. Emerging evidence suggests the role of microbial metabolites in modulating host inflammation and DNA damage in CRC. However, the experimental validations have been hindered by cost, resources, and cumbersome technical expertise required for metabolomic investigations. In this study, we performed a meta-analysis of CRC microbiota data from diverse geographical regions using multiple methods to achieve reproducible results. We used a computational approach to predict the metabolomic profiles using existing CRC metagenomic datasets. We identified a reliable set of CRC-specific biomarkers from this analysis, including microbial and metabolite markers. In addition, we revealed significant microbe-metabolite associations through correlation analysis and microbial gene families associated with dysregulated metabolic pathways in CRC, which are essential in understanding the vastly sporadic nature of CRC development and progression.

Just Add Data: automated predictive modeling for knowledge discovery and feature selection

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.

Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer

The association of colorectal cancer (CRC) and the human gut microbiome dysbiosis has been the focus of several studies in the past. Many bacterial taxa have been shown to have differential abundance among CRC patients compared to healthy controls. However, the relationship between CRC and non-bacterial gut microbiome such as the gut virome is under-studied and not well understood. In this study we conducted a comprehensive analysis of the association of viral abundances with CRC using metagenomic shotgun sequencing data of 462 CRC subjects and 449 healthy controls from 7 studies performed in 8 different countries. Despite the high heterogeneity, our results showed that the virome alpha diversity was consistently higher in CRC patients than in healthy controls (p-value &lt;0.001). This finding is in sharp contrast to previous reports of low alpha diversity of prokaryotes in CRC compared to healthy controls. In addition to the previously known association of Podoviridae, Siphoviridae and Myoviridae with CRC, we further demonstrate that Herelleviridae, a newly constructed viral family, is significantly depleted in CRC subjects. Our interkingdom association analysis reveals a less intertwined correlation between the gut virome and bacteriome in CRC compared to healthy controls. Furthermore, we show that the viral abundance profiles can be used to accurately predict CRC disease status (AUROC &gt;0.8) in both within-study and cross-study settings. The combination of training sets resulted in rather generalized and accurate prediction models. Our study clearly shows that subjects with colorectal cancer harbor a distinct human gut virome profile which may have an important role in this disease.

The Role of Microbiome in Brain Development and Neurodegenerative Diseases

Hundreds of billions of commensal microorganisms live in and on our bodies, most of which colonize the gut shortly after birth and stay there for the rest of our lives. In animal models, bidirectional communications between the central nervous system and gut microbiota (Gut-Brain Axis) have been extensively studied, and it is clear that changes in microbiota composition play a vital role in the pathogenesis of various neurodevelopmental and neurodegenerative disorders, such as Autism Spectrum Disorder, Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, anxiety, stress, and so on. The makeup of the microbiome is impacted by a variety of factors, such as genetics, health status, method of delivery, environment, nutrition, and exercise, and the present understanding of the role of gut microbiota and its metabolites in the preservation of brain functioning and the development of the aforementioned neurological illnesses is summarized in this review article. Furthermore, we discuss current breakthroughs in the use of probiotics, prebiotics, and synbiotics to address neurological illnesses. Moreover, we also discussed the role of boron-based diet in memory, boron and microbiome relation, boron as anti-inflammatory agents, and boron in neurodegenerative diseases. In addition, in the coming years, boron reagents will play a significant role to improve dysbiosis and will open new areas for researchers.

Whole-Genome Shotgun Metagenomic Sequencing Reveals Distinct Gut Microbiome Signatures of Obese Cats

Overweight and obesity are growing health problems in domestic cats, increasing the risks of insulin resistance, lipid dyscrasias, neoplasia, cardiovascular disease, and decreasing longevity. The signature of obesity in the feline gut microbiota has not been studied at the whole-genome metagenomic level. We performed whole-genome shotgun metagenomic sequencing in the fecal samples of eight overweight/obese and eight normal cats housed in the same research environment. We obtained 271 Gbp of sequences and generated a 961-Mbp de novo reference contig assembly, with 1.14 million annotated microbial genes. In the obese cat microbiome, we discovered a significant reduction in microbial diversity (P < 0.01) and Firmicutes abundance (P = 0.005), as well as decreased Firmicutes/Bacteroidetes ratios (P = 0.02), which is the inverse of obese human/mouse microbiota. Linear discriminant analysis and quantitative PCR (qPCR) validation revealed significant increases of Bifidobacterium sp., Olsenella provencensis, Dialister sp.CAG:486, and Campylobacter upsaliensis as the hallmark of obese microbiota among 400 enriched species, whereas 1,525 bacterial species have decreased abundance in the obese microbiome. Phascolarctobacterium succinatutens and an uncharacterized Erysipelotrichaceae bacterium are highly abundant (>0.05%) in the normal gut with over 400-fold depletion in the obese microbiome. Fatty acid synthesis-related pathways are significantly overrepresented in the obese compared with the normal cat microbiome. In conclusion, we discovered dramatically decreased microbial diversity in obese cat gut microbiota, suggesting potential dysbiosis. A panel of seven significantly altered, highly abundant species can serve as a microbiome indicator of obesity. Our findings in the obese cat microbiome composition, abundance, and functional capacities provide new insights into feline obesity.

IMPORTANCE Obesity affects around 45% of domestic cats, and licensed drugs for treating feline obesity are lacking. Physical exercise and calorie restrictions are commonly used for weight loss but with limited efficacy. Through comprehensive analyses of normal and obese cat gut bacteria flora, we identified dramatic shifts in the obese gut microbiome, including four bacterial species significantly enriched and two species depleted in the obese cats. The key bacterial community and functional capacity alterations discovered from this study will inform new weight management strategies for obese cats, such as evaluations of specific diet formulas that alter the microbiome composition, and the development of prebiotics and probiotics that promote the increase of beneficial species and the depletion of obesity-associated species. Interestingly, these bacteria identified in our study were also reported to affect the weight loss success in human patients, suggesting translational potential in human obesity.

Metformin-induced reductions in tumor growth involves modulation of the gut microbiome

Background/Purpose

Type 2 diabetes and obesity increase the risk of developing colorectal cancer. Metformin may reduce colorectal cancer but the mechanisms mediating this effect remain unclear. In mice and humans, a high-fat diet (HFD), obesity and metformin are known to alter the gut microbiome but whether this is important for influencing tumor growth is not known.

Methods

Mice with syngeneic MC38 colon adenocarcinomas were treated with metformin or feces obtained from control or metformin treated mice.

Results

We find that compared to chow-fed controls, tumor growth is increased when mice are fed a HFD and that this acceleration of tumor growth can be partially recapitulated through transfer of the fecal microbiome or in vitro treatment of cells with fecal filtrates from HFD-fed animals. Treatment of HFD-fed mice with orally ingested, but not intraperitoneally injected, metformin suppresses tumor growth and increases the expression of short-chain fatty acid (SCFA)-producing microbes Alistipes, Lachnospiraceae and Ruminococcaceae. The transfer of the gut microbiome from mice treated orally with metformin to drug naïve, conventionalized HFD-fed mice increases circulating propionate and butyrate, reduces tumor proliferation, and suppresses the expression of sterol response element binding protein (SREBP) gene targets in the tumor.

Conclusion

These data indicate that in obese mice fed a HFD, metformin reduces tumor burden through changes in the gut microbiome.

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Oral but not intraperitoneal injection of metformin is associated with changes in the gut microbiome and reductions in MC38 tumor cell growth in mice fed a high-fat diet.

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Transferring feces from mice treated with oral metformin into metformin naïve mice inhibits tumor growth independently of changes in body mass, blood glucose or serum insulin.

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Metformin fecal transfers to metformin naïve mice leads to increased abundance of short chain fatty acid producing microbes.

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Metformin fecal transfers reprogram tumor metabolism reducing the expression of SREBP and cholesterol synthesis genes.

Diversity and distribution of sulfur metabolic genes in the human gut microbiome and their association with colorectal cancer

BACKGROUND

Recent evidence implicates microbial sulfidogenesis as a potential trigger of colorectal cancer (CRC), highlighting the need for comprehensive knowledge of sulfur metabolism within the human gut. Microbial sulfidogenesis produces genotoxic hydrogen sulfide (H₂S) in the human colon using inorganic (sulfate) and organic (taurine/cysteine/methionine) substrates; however, the majority of studies have focused on sulfate reduction using dissimilatory sulfite reductases (Dsr).

RESULTS

Here, we show that genes for microbial sulfur metabolism are more abundant and diverse than previously observed and are statistically associated with CRC. Using ~ 17,000 bacterial genomes from publicly available stool metagenomes, we studied the diversity of sulfur metabolic genes in 667 participants across different health statuses: healthy, adenoma, and carcinoma. Sulfidogenic genes were harbored by 142 bacterial genera and both organic and inorganic sulfidogenic genes were associated with carcinoma. Significantly, the anaerobic sulfite reductase (asr) genes were twice as abundant as dsr, demonstrating that Asr is likely a more important contributor to sulfate reduction in the human gut than Dsr. We identified twelve potential pathways for reductive taurine metabolism and discovered novel genera harboring these pathways. Finally, the prevalence of metabolic genes for organic sulfur indicates that these understudied substrates may be the most abundant source of microbially derived H₂S.

CONCLUSIONS

Our findings significantly expand knowledge of microbial sulfur metabolism in the human gut. We show that genes for microbial sulfur metabolism in the human gut are more prevalent than previously known, irrespective of health status (i.e., in both healthy and diseased states). Our results significantly increase the diversity of pathways and bacteria that are associated with microbial sulfur metabolism in the human gut. Overall, our results have implications for understanding the role of the human gut microbiome and its potential contributions to the pathogenesis of CRC. Video abstract.

mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis

The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical. Furthermore, the measurements suffer from unequal sequencing depth, overdispersion, and data redundancy. These nuisance factors introduce substantial noise. We propose an accurate and robust method, mbDenoise, for denoising microbiome data. Assuming a zero-inflated probabilistic PCA (ZIPPCA) model, mbDenoise uses variational approximation to learn the latent structure and recovers the true abundance levels using the posterior, borrowing information across samples and taxa. mbDenoise outperforms state-of-the-art methods to extract the signal for downstream analyses.

Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa

Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies.

The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.

An integrative understanding of the large metabolic shifts induced by antibiotics in critical illness

Antibiotics are commonly used in the Intensive Care Unit (ICU); however, several studies showed that the impact of antibiotics to prevent infection, multi-organ failure, and death in the ICU is less clear than their benefit on course of infection in the absence of organ dysfunction. We characterized here the compositional and metabolic changes of the gut microbiome induced by critical illness and antibiotics in a cohort of 75 individuals in conjunction with 2,180 gut microbiome samples representing 16 different diseases. We revealed an "infection-vulnerable" gut microbiome environment present only in critically ill treated with antibiotics (ICU⁺). Feeding of Caenorhabditis elegans with Bifidobacterium animalis and Lactobacillus crispatus, species that expanded in ICU⁺ patients, revealed a significant negative impact of these microbes on host viability and developmental homeostasis. These results suggest that antibiotic administration can dramatically impact essential functional activities in the gut related to immune responses more than critical illness itself, which might explain in part untoward effects of antibiotics in the critically ill.

The Regional Specificity of Mucosa-Associated Microbiota in Patients with Distal Colorectal Cancer

BACKGROUND/AIMS

Recent studies have demonstrated that the populations of several microbes are significantly increased in fecal samples from patients with colorectal cancer (CRC), suggesting their involvement in the development of CRC. The aim of this study was to identify microbes which are increased in distal CRCs and to identify the specific location of microbes increased in mucosal tissue around the tumor.

METHODS

Tissue specimens were collected from surgical resections of 28 distal CRCs. Five samples were collected from each specimen (location A: tumor, B: adjacent normal mucosa, C: normal mucosa 1 cm proximal to the tumor, D: normal mucosa 3 cm proximally, and E: normal mucosa 6 cm proximally). The microbiota in the sample were analyzed using 16S rRNA gene amplicon sequencing and the relative abundance (RA) of microbiota compared among the 5 locations.

RESULTS

At the genus level, the RA of Fusobacterium and Streptococcus at location A was the highest among the 5 locations, significantly different from that in location E. The dominant species of each genus was Fusobacterium nucleatum and Streptococcus anginosus. The RAs of these species gradually decreased from locations B to E with a statistically significant difference in F. nucleatum. The genus Peptostreptococcus also showed a similar trend, and the RA of Peptostreptococcus stomatis in location A was significantly associated with depth of tumor invasion and tumor size.

CONCLUSION

Although the clinical relevance is not clear yet, these results suggest that F. nucleatum, S. anginosus, and P. stomatis can spread to the adjacent normal tissues and may change the surrounding microenvironment to support the progression of CRC.

Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles. Based on within data cross-validation and cross-dataset prediction, we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken. We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial organisms to further increase the prediction performance for colorectal cancer from metagenomes.

A multiple-dimension model for microbiota of patients with colorectal cancer from normal participants and other intestinal disorders

Gut microbiota is a primary driver of inflammation in the colon and is linked to early colorectal cancer (CRC) development. Thus, a novel and noninvasive microbiome-based model could promote screening in patients at average risk for CRC. Nevertheless, the relevance and effectiveness of microbial biomarkers for noninvasive CRC screening remains unclear, and researchers lack the data to distinguish CRC-related gut microbiome biomarkers from those of other common gastrointestinal (GI) diseases. Microbiome-based classification distinguishes patients with CRC from normal participants and excludes other CRC-relevant diseases (e.g., GI bleed, adenoma, bowel diseases, and postoperative). The area under the receiver operator characteristic curve (AUC) was 92.2%. Known associations with oral pathogenic features, benefits-generated features, and functional features of CRC were confirmed using the model. Our optimised prediction model was established using large-scale experimental population-based data and other sequence-based faecal microbial community data. This model can be used to identify the high-risk groups and has the potential to become a novel screening method for CRC biomarkers because of its low false-positive rate (FPR) and good stability. KEY POINTS: • A total of 5744 CRC and non-CRC large-scale faecal samples were sequenced, and a model was constructed for CRC discrimination on the basis of the relative abundance of taxonomic and functional features. • This model could identify high-risk groups and become a novel screening method for CRC biomarkers because of its low FPR and good stability. • The association relationship of oral pathogenic features, benefits-generated features, and functional features in CRC was confirmed by the study.

The gut microbiome and antibiotic resistome of chronic diarrhea rhesus macaques (Macaca mulatta) and its similarity to the human gut microbiome

BACKGROUND

Chronic diarrhea is a common disease causing morbidity and mortality of captive rhesus macaques (RMs, Macaca mulatta). Chronic diarrhea in RMs is typically characterized by long-term diarrhea and a weak response to antibiotic treatment. Diarrhea is also a common disease in humans and can cause death. However, the etiology of about half of diarrheal cases of humans is still unclear. Therefore, we performed shotgun metagenomic sequencing to characterize the differences in the gut microbiome and resistome of chronic diarrhea RMs and asymptomatic individuals.

RESULTS

Our results showed Lactobacillus spp. (mainly L. johnsonii, L. reuteri and L. amylovorus) were significantly depleted in chronic diarrhea RM guts compared to asymptomatic individuals (5.2 vs 42.4%). Functional annotation of genes suggested these Lactobacillus spp. carried genes involved in the adhesion of intestinal epithelial cells and production of bacteriocin. Chronic diarrhea RM guts also had a significantly greater abundance of many other gut bacteria, including mucin-degrading bacteria and opportunistic pathogens. The metabolic pathways of chronic diarrhea RM gut microbiome were enriched in aerobactin biosynthesis, while the metabolic pathways of asymptomatic RM gut microbiome were enriched in the production of short-chain fatty acids (SCFAs). Chronic diarrhea RM guts had a significantly greater abundance of antibiotic resistance genes (ARGs), such as ermF, aph(3')-IIIa, ermB, and floR. The strains isolated from feces and tissue fluid of chronic diarrhea RMs had higher resistance rates to the majority of tested antibiotics, but not cephamycin and carbapenem antibiotics. Gut microbial composition comparisons showed that several captive nonhuman primate (NHP) guts were more similar to the guts of humans with a non-westernized diet than humans with a westernized diet. Chronic diarrhea RM gut microbiome was strikingly similar to rural-living humans with diarrhea and humans with a non-westernized diet than asymptomatic RMs.

CONCLUSIONS

Our results suggested chronic diarrhea significantly altered the composition and metabolic pathways of the RM gut microbiome. The frequent use of antibiotics caused antibiotic resistance in chronic diarrhea RM gut microbiome with serious consequences for individual treatment and survival. The findings of this study will help us to improve the effective prevention and treatment of diarrhea in RMs. Video Abstract.

Evaluating supervised and unsupervised background noise correction in human gut microbiome data

The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach that is presently used in other domains but has not been applied to microbiome data to date. We find that the unsupervised principal component correction approach has comparable ability in reducing false discovery of biomarkers as the supervised approaches, with the added benefit of not needing to know the sources of variation apriori. However, in prediction tasks, it appears to only improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

The human gut microbiome is known to play a major role in health and is associated with many diseases including colorectal cancer, obesity, and diabetes. The prediction of host phenotypes and identification of biomarkers of disease is essential for harnessing the therapeutic potential of the microbiome. However, many metagenomic datasets are affected by technical variables that introduce unwanted variation that can confound the ability to predict phenotypes and identify biomarkers. Currently, supervised methods originally designed for gene expression and RNA-seq data are commonly applied to microbiome data for correction of background noise, but they are limited in that they cannot correct for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach and find that all correction approaches reduce false positives for biomarker discovery. In the task of predicting phenotypes, different approaches have varying success where the unsupervised correction can improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

Completion of the gut microbial epi-bile acid pathway

Bile acids are detergent molecules that solubilize dietary lipids and lipid-soluble vitamins. Humans synthesize bile acids with α-orientation hydroxyl groups which can be biotransformed by gut microbiota to toxic, hydrophobic bile acids, such as deoxycholic acid (DCA). Gut microbiota can also convert hydroxyl groups from the α-orientation through an oxo-intermediate to the β-orientation, resulting in more hydrophilic, less toxic bile acids. This interconversion is catalyzed by regio- (C-3 vs. C-7) and stereospecific (α vs. β) hydroxysteroid dehydrogenases (HSDHs). So far, genes encoding the urso- (7α-HSDH & 7β-HSDH) and iso- (3α-HSDH & 3β-HSDH) bile acid pathways have been described. Recently, multiple human gut clostridia were reported to encode 12α-HSDH, which interconverts DCA and 12-oxolithocholic acid (12-oxoLCA). 12β-HSDH completes the epi-bile acid pathway by converting 12-oxoLCA to the 12β-bile acid denoted epiDCA; however, a gene(s) encoding this enzyme has yet to be identified. We confirmed 12β-HSDH activity in cultures of Clostridium paraputrificum ATCC 25780. From six candidate C. paraputrificum ATCC 25780 oxidoreductase genes, we discovered the first gene (DR024_RS09610) encoding bile acid 12β-HSDH. Phylogenetic analysis revealed unforeseen diversity for 12β-HSDH, leading to validation of two additional bile acid 12β-HSDHs through a synthetic biology approach. By comparison to a previous phylogenetic analysis of 12α-HSDH, we identified the first potential C-12 epimerizing strains: Collinsella tanakaei YIT 12063 and Collinsella stercoris DSM 13279. A Hidden Markov Model search against human gut metagenomes located putative 12β-HSDH genes in about 30% of subjects within the cohorts analyzed, indicating this gene is relevant in the human gut microbiome.

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

Bifidobacterium animalis: the missing link for the cancer-preventive effect of Gynostemma pentaphyllum

Colorectal cancer (CRC) ranks the third most common cancer type in both men and women. Besides the known genetic and epigenetic changes in the gut epithelial cells, we now know that disturbed gut microbes could also contribute to the onset and progression of CRC. Hence, keeping a balanced gut microbiota (GM) has become a novel pursue in the medical field, particularly in the area of gastrointestinal disorders. Gynostemma pentaphyllum (Gp) is a dietary herbal medicine. In our previous study, Gp saponins (GpS) displayed prebiotic and cancer-preventive properties through the modulation of GM in Apc^Min/+ mice. However, the specific group(s) of GM links to the health effects of GpS remains unknown. To track down the missing link, we first investigated and found that inoculation with fecal materials from GpS-treated Apc^Min/+ mice effectively reduces polyps in Apc^Min/+ mice. From the same source of the fecal sample, we successfully isolated 16 bacterial species. Out of the 16 bacteria, Bifidobacterium animalis stands out as the responder to the GpS-growth stimulus. Biochemical and RNAseq analysis demonstrated that GpS enhanced expressions of a wide range of genes encoding biogenesis and metabolic pathways in B. animalis culture. Moreover, we found that colonization of B. animalis markedly reduces the polyp burden in Apc^Min/+ mice. These findings reveal a mutualistic interaction between the prebiotic and a probiotic to achieve anticancer and cancer-preventive activities. Our result, for the first time, unveils the anticancer function of B. animalis and extend the probiotic horizon of B. animalis.

Species-Level Analysis of the Human Gut Microbiome Shows Antibiotic Resistance Genes Associated With Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer deaths and continuously increases new cancer cases globally. Accumulating evidence links risks of CRC to antibiotic use. Long-term use and abuse of antibiotics increase the resistance of the gut microbiota; however, whether CRC is associated with antibiotic resistance in gut microbiota is still unclear. In this study, we performed a de novo assembly to metagenomic sequences in 382 CRC patients and 387 healthy controls to obtain representative species-level genome bins (rSGBs) and plasmids and analyzed the abundance variation of species and antibiotic resistance genes (ARGs). Twenty-five species and 65 ARGs were significantly enriched in the CRC patients, and among these ARGs, 12 were multidrug-resistant genes (MRGs), which mainly included acrB, TolC, marA, H-NS, Escherichia coli acrR mutation, and AcrS. These MRGs could confer resistance to fluoroquinolones, tetracyclines, cephalosporins, and rifamycin antibiotics by antibiotic efflux and inactivation. A classification model was built using the abundance of species and ARGs and achieved areas under the curve of 0.831 and 0.715, respectively. Our investigation has identified the antibiotic resistance types of ARGs and suggested that E. coli is the primary antibiotic resistance reservoir of ARGs in CRC patients, providing valuable evidence for selecting appropriate antibiotics in the CRC treatment.

Towards the biogeography of prokaryotic genes

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats^1-3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

Putative Familial Transmissible Bacteria of Various Body Niches Link with Home Environment and Children's Immune Health

Owing to their significant impact on children's long-term health, familial factors in the microbiomes of children have attracted increasing attention. However, the mechanism underlying microbiome transmission across generations remains unclear. A significantly lower alpha diversity was observed in the gut flora of children than in the gut flora of parents and grandparents; the alpha diversity of oral and skin microbiota was relatively higher in children than in their predecessors. Gut, oral, and skin microbiome was more similar between family members than between unrelated individuals. Meanwhile, 55.05%, 61.09%, and 76.73% of amplicon sequence variants (ASVs) in children's gut, oral, and skin microbiomes, respectively, were transmitted from all family members. Among these, the most transmissible ASVs belonged to Methylophilaceae, Solimonadaceae, Neisseriaceae, and Burkholderiaceae, which were defined as "putative familial transmissible bacteria." Furthermore, we found that the time spent with parents/grandparents and children's dietary preferences were important factors that influenced the proportion of the transmissible microbiome. Moreover, the majority of transmissible ASVs (85.06%), especially those of Ruminococcaceae and Lachnospiraceae, were significantly associated with the immune indices, such as CD3⁺, CD4⁺, CD8⁺, IgG, and IgA. IMPORTANCE Our study revealed that the children's microbiota was partially transmitted from their family members and specific putative transmissible ASVs were associated with the immune system of children. These findings suggest that home life plays a key role in the shaping of young children's microbiomes and has long-term health benefits.

Strategies for the treatment of colorectal cancer caused by gut microbiota

Colorectal cancer (CRC), also named as colon and rectal or bowel cancer, is one of the leading neoplasia diagnosed in the world. Genetic sequencing studies of microorganisms from the intestinal microbiota of patients with CRC revealed that changes in its composition occur with the development of the disease, which can play a fundamental role in its development, being mediated by the production of metabolites and toxins that damage enterocytes. Some microorganisms are frequently reported in the literature as the main agents of this process, such as the bacteria Fusobacterium nucleatum, Escherichia coli and Bacteroides fragilis. Thus, understanding the mechanisms and function of each microorganism in CRC is essential for the development of treatment tools that focus on the gut microbiota. This review verifies current research aimed at evaluating the microorganisms present in the microbiota that can influence the development of CRC, as well as possible forms of treatment that can prevent the initiation and/or spread of this disease. Due to the incidence of CRC, alternatives have been launched considering factors beyond those already known in the disease development, such as diet, fecal microbiota transplantation, use of probiotics and antibiotics, which have been widely studied for this purpose. However, despite being promising, the studies that focus on the development of new therapeutic approaches targeting the microorganisms that cause CRC still need to be improved and better developed, involving new techniques to elucidate the effectiveness and safety of these new methods.

Unravelling the collateral damage of antibiotics on gut bacteria

Antibiotics are used to fight pathogens but also target commensal bacteria, disturbing the composition of gut microbiota and causing dysbiosis and disease1. Despite this well-known collateral damage, the activity spectrum of different antibiotic classes on gut bacteria remains poorly characterized. Here we characterize further 144 antibiotics from a previous screen of more than 1,000 drugs on 38 representative human gut microbiome species2. Antibiotic classes exhibited distinct inhibition spectra, including generation dependence for quinolones and phylogeny independence for β-lactams. Macrolides and tetracyclines, both prototypic bacteriostatic protein synthesis inhibitors, inhibited nearly all commensals tested but also killed several species. Killed bacteria were more readily eliminated from in vitro communities than those inhibited. This species-specific killing activity challenges the long-standing distinction between bactericidal and bacteriostatic antibiotic classes and provides a possible explanation for the strong effect of macrolides on animal3-5 and human6,7 gut microbiomes. To mitigate this collateral damage of macrolides and tetracyclines, we screened for drugs that specifically antagonized the antibiotic activity against abundant Bacteroides species but not against relevant pathogens. Such antidotes selectively protected Bacteroides species from erythromycin treatment in human-stool-derived communities and gnotobiotic mice. These findings illluminate the activity spectra of antibiotics in commensal bacteria and suggest strategies to circumvent their adverse effects on the gut microbiota.

Commensal Clostridiales strains mediate effective anti-cancer immune response against solid tumors

Despite overall success, T cell checkpoint inhibitors for cancer treatment are still only efficient in a minority of patients. Recently, intestinal microbiota was found to critically modulate anti-cancer immunity and therapy response. Here, we identify Clostridiales members of the gut microbiota associated with a lower tumor burden in mouse models of colorectal cancer (CRC). Interestingly, these commensal species are also significantly reduced in CRC patients compared with healthy controls. Oral application of a mix of four Clostridiales strains (CC4) in mice prevented and even successfully treated CRC as stand-alone therapy. This effect depended on intratumoral infiltration and activation of CD8⁺ T cells. Single application of Roseburia intestinalis or Anaerostipes caccae was even more effective than CC4. In a direct comparison, the CC4 mix supplementation outperformed anti-PD-1 therapy in mouse models of CRC and melanoma. Our findings provide a strong preclinical foundation for exploring gut bacteria as novel stand-alone therapy against solid tumors.

Early development of the skin microbiome: therapeutic opportunities

As human skin hosts a diverse microbiota in health and disease, there is an emerging consensus that dysregulated interactions between host and microbiome may contribute to chronic inflammatory disease of the skin. Neonatal skin is a unique habitat, structurally similar to the adult but with a different profile of metabolic substrates, environmental stressors, and immune activity. The surface is colonized within moments of birth with a bias toward maternal strains. Initial colonists are outcompeted as environmental exposures increase and host skin matures. Nonetheless, early life microbial acquisitions may have long-lasting effects on health through modulation of host immunity and competitive interactions between bacteria. Microbial ecology and its influence on health have been of interest to dermatologists for >50 years, and an explosion of recent interest in the microbiome has prompted ongoing investigations of several microbial therapeutics for dermatological disease. In this review, we consider how recent insight into the host and microbial factors driving development of the skin microbiome in early life offers new opportunities for therapeutic intervention. IMPACT: Advancement in understanding molecular mechanisms of bacterial competition opens new avenues of investigation into dermatological disease. Primary development of the skin microbiome is determined by immunological features of the cutaneous habitat. Understanding coordinated microbial and immunological development in the pediatric patient requires a multidisciplinary synthesis of primary literature.

The taxonomic distribution of histamine-secreting bacteria in the human gut microbiome

BACKGROUND

Biogenic histamine plays an important role in immune response, neurotransmission, and allergic response. Although endogenous histamine production has been extensively studied, the contributions of histamine produced by the human gut microbiota have not been explored due to the absence of a systematic annotation of histamine-secreting bacteria.

RESULTS

To identify the histamine-secreting bacteria from in the human gut microbiome, we conducted a systematic search for putative histamine-secreting bacteria in 36,554 genomes from the Genome Taxonomy Database and Unified Human Gastrointestinal Genome catalog. Using bioinformatic approaches, we identified 117 putative histamine-secreting bacteria species. A new three-component decarboxylation system including two colocalized decarboxylases and one transporter was observed in histamine-secreting bacteria among three different phyla. We found significant enrichment of histamine-secreting bacteria in patients with inflammatory bowel disease but not in patients with colorectal cancer suggesting a possible association between histamine-secreting bacteria and inflammatory bowel disease.

CONCLUSIONS

The findings of this study expand our knowledge of the taxonomic distribution of putative histamine-secreting bacteria in the human gut.

The CRCbiome study: a large prospective cohort study examining the role of lifestyle and the gut microbiome in colorectal cancer screening participants

BACKGROUND

Colorectal cancer (CRC) screening reduces CRC incidence and mortality. However, current screening methods are either hampered by invasiveness or suboptimal performance, limiting their effectiveness as primary screening methods. To aid in the development of a non-invasive screening test with improved sensitivity and specificity, we have initiated a prospective biomarker study (CRCbiome), nested within a large randomized CRC screening trial in Norway. We aim to develop a microbiome-based classification algorithm to identify advanced colorectal lesions in screening participants testing positive for an immunochemical fecal occult blood test (FIT). We will also examine interactions with host factors, diet, lifestyle and prescription drugs. The prospective nature of the study also enables the analysis of changes in the gut microbiome following the removal of precancerous lesions.

METHODS

The CRCbiome study recruits participants enrolled in the Bowel Cancer Screening in Norway (BCSN) study, a randomized trial initiated in 2012 comparing once-only sigmoidoscopy to repeated biennial FIT, where women and men aged 50-74 years at study entry are invited to participate. Since 2017, participants randomized to FIT screening with a positive test result have been invited to join the CRCbiome study. Self-reported diet, lifestyle and demographic data are collected prior to colonoscopy after the positive FIT-test (baseline). Screening data, including colonoscopy findings are obtained from the BCSN database. Fecal samples for gut microbiome analyses are collected both before and 2 and 12 months after colonoscopy. Samples are analyzed using metagenome sequencing, with taxonomy profiles, and gene and pathway content as primary measures. CRCbiome data will also be linked to national registries to obtain information on prescription histories and cancer relevant outcomes occurring during the 10 year follow-up period.

DISCUSSION

The CRCbiome study will increase our understanding of how the gut microbiome, in combination with lifestyle and environmental factors, influences the early stages of colorectal carcinogenesis. This knowledge will be crucial to develop microbiome-based screening tools for CRC. By evaluating biomarker performance in a screening setting, using samples from the target population, the generalizability of the findings to future screening cohorts is likely to be high.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01538550 .

mbImpute: an accurate and robust imputation method for microbiome data

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data-mbImpute-to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Amplicon-sequencing of raw milk microbiota: impact of DNA extraction and library-PCR

Abstract

The highly complex raw milk matrix challenges the sample preparation for amplicon-sequencing due to low bacterial counts and high amounts of eukaryotic DNA originating from the cow. In this study, we optimized the extraction of bacterial DNA from raw milk for microbiome analysis and evaluated the impact of cycle numbers in the library-PCR. The selective lysis of eukaryotic cells by proteinase K and digestion of released DNA before bacterial lysis resulted in a high reduction of mostly eukaryotic DNA and increased the proportion of bacterial DNA. Comparative microbiome analysis showed that a combined enzymatic and mechanical lysis procedure using the DNeasy^® PowerFood^® Microbial Kit with a modified protocol was best suitable to achieve high DNA quantities after library-PCR and broad coverage of detected bacterial biodiversity. Increasing cycle numbers during library-PCR systematically altered results for species and beta-diversity with a tendency to overrepresentation or underrepresentation of particular taxa. To limit PCR bias, high cycle numbers should thus be avoided. An optimized DNA extraction yielding sufficient bacterial DNA and enabling higher PCR efficiency is fundamental for successful library preparation. We suggest that a protocol using ethylenediaminetetraacetic acid (EDTA) to resolve casein micelles, selective lysis of somatic cells, extraction of bacterial DNA with a combination of mechanical and enzymatic lysis, and restriction of PCR cycles for analysis of raw milk microbiomes is optimal even for samples with low bacterial numbers.

Key points

• Sample preparation for high-throughput 16S rRNA gene sequencing of raw milk microbiota.

• Reduction of eukaryotic DNA by enzymatic digestion.

• Shift of detected microbiome caused by high cycle numbers in library-PCR.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11353-4.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models

Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis.

IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases.

Microbiome Analysis of More Than 2,000 NHS Bowel Cancer Screening Programme Samples Shows the Potential to Improve Screening Accuracy

PURPOSE

There is potential for fecal microbiome profiling to improve colorectal cancer screening. This has been demonstrated by research studies, but it has not been quantified at scale using samples collected and processed routinely by a national screening program.

EXPERIMENTAL DESIGN

Between 2016 and 2019, the largest of the NHS Bowel Cancer Screening Programme hubs prospectively collected processed guaiac fecal occult blood test (gFOBT) samples with subsequent colonoscopy outcomes: blood-negative [n = 491 (22%)]; colorectal cancer [n = 430 (19%)]; adenoma [n = 665 (30%)]; colonoscopy-normal [n = 300 (13%)]; nonneoplastic [n = 366 (16%)]. Samples were transported and stored at room temperature. DNA underwent 16S rRNA gene V4 amplicon sequencing. Taxonomic profiling was performed to provide features for classification via random forests (RF).

RESULTS

Samples provided 16S amplicon-based microbial profiles, which confirmed previously described colorectal cancer-microbiome associations. Microbiome-based RF models showed potential as a first-tier screen, distinguishing colorectal cancer or neoplasm (colorectal cancer or adenoma) from blood-negative with AUC 0.86 (0.82-0.89) and AUC 0.78 (0.74-0.82), respectively. Microbiome-based models also showed potential as a second-tier screen, distinguishing from among gFOBT blood-positive samples, colorectal cancer or neoplasm from colonoscopy-normal with AUC 0.79 (0.74-0.83) and AUC 0.73 (0.68-0.77), respectively. Models remained robust when restricted to 15 taxa, and performed similarly during external validation with metagenomic datasets.

CONCLUSIONS

Microbiome features can be assessed using gFOBT samples collected and processed routinely by a national colorectal cancer screening program to improve accuracy as a first- or second-tier screen. The models required as few as 15 taxa, raising the potential of an inexpensive qPCR test. This could reduce the number of colonoscopies in countries that use fecal occult blood test screening.

Microbiome and colorectal cancer: A review of the past, present, and future

The gastrointestinal tract is home to diverse and abundant microorganisms, collectively referred to as the microbiome. This ecosystem typically contains trillions of microbial cells that play an important role in regulation of human health. The microbiome has been implicated in host immunity, nutrient absorption, digestion, and metabolism. In recent years, researchers have shown that alteration of the microbiome is associated with disease development, such as obesity, inflammatory bowel disease, and cancer. This review discusses the five decades of research into the human microbiome and the development of colorectal cancer - the historical context including experiments that sparked interest, the explosion of research that has occurred in the last decade, and finally the future of testing and treatment.

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time-consuming for early-stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high-throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell-free DNA and microbiome data generated by amplicon sequencing or whole-genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

Diversity and composition of gut microbiome of cervical cancer patients: Do results of 16S rRNA sequencing and whole genome sequencing approaches align?

BACKGROUND

Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples.

RESULTS

Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all P-value>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables.

CONCLUSION

Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S.

Dysbiosis Triggers ACF Development in Genetically Predisposed Subjects

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide, characterized by a multifactorial etiology including genetics, lifestyle, and environmental factors including microbiota composition. To address the role of microbial modulation in CRC, we used our recently established mouse model (the Winnie-APC^Min/+) combining inflammation and genetics.

METHODS

Gut microbiota profiling was performed on 8-week-old Winnie-APC^Min/+ mice and their littermates by 16S rDNA gene amplicon sequencing. Moreover, to study the impact of dysbiosis induced by the mother's genetics in ACF development, the large intestines of APC^Min/+ mice born from wild type mice were investigated by histological analysis at 8 weeks.

RESULTS

ACF development in 8-week-old Winnie-APC^Min/+ mice was triggered by dysbiosis. Specifically, the onset of ACF in genetically predisposed mice may result from dysbiotic signatures in the gastrointestinal tract of the breeders. Additionally, fecal transplant from Winnie donors to APC^Min/+ hosts leads to an increased rate of ACF development.

CONCLUSIONS

The characterization of microbiota profiling supporting CRC development in genetically predisposed mice could help to design therapeutic strategies to prevent dysbiosis. The application of these strategies in mothers during pregnancy and lactation could also reduce the CRC risk in the offspring.

Intestinal microbiota and its association with colon cancer and red/processed meat consumption

The human colon harbors a high number of microorganisms that were reported to play a crucial role in colorectal carcinogenesis. In the recent decade, molecular detection and metabolomic techniques have expanded our knowledge on the role of specific microbial species in promoting tumorigenesis. In this study, we reviewed the association between microbial dysbiosis and colorectal carcinoma (CRC). Various microbial species and their association with colorectal tumorigenesis and red/processed meat consumption have been reviewed. The literature demonstrated a significant abundance of Fusobacterium nucleatum, Streptococcus bovis/gallolyticus, Escherichia coli, and Bacteroides fragilis in patients with adenoma or adenocarcinoma compared to healthy individuals. The mechanisms in which each organism was postulated to promote colon carcinogenesis were collated and summarized in this review. These include the microorganisms' ability to adhere to colon cells; modulate the inhibition of tumor suppressor genes, the activations of oncogenes, and genotoxicity; and activate downstream targets responsible for angiogenesis. The role of these microorganisms in conjugation with meat components including N-nitroso compounds, heterocyclic amines, and heme was also evident in multiple studies. The outcome of this review supports the role of red meat consumption in modulating CRC progression and the possibility of gut microbiome influencing the relationship between CRC and diet. The study also demonstrates that microbiota analysis could potentially complement existing screening methods when detecting colonic lesions.

The Sporobiota of the Human Gut

The human gut microbiome is a diverse and complex ecosystem that plays a critical role in health and disease. The composition of the gut microbiome has been well studied across all stages of life. In recent years, studies have investigated the production of endospores by specific members of the gut microbiome. An endospore is a tough, dormant structure formed by members of the Firmicutes phylum, which allows for greater resistance to otherwise inhospitable conditions. This innate resistance has consequences for human health and disease, as well as in biotechnology. In particular, the formation of endospores is strongly linked to antibiotic resistance and the spread of antibiotic resistance genes, also known as the resistome. The term sporobiota has been used to define the spore-forming cohort of a microbial community. In this review, we present an overview of the current knowledge of the sporobiota in the human gut. We discuss the development of the sporobiota in the infant gut and the perinatal factors that may have an effect on vertical transmission from mother to infant. Finally, we examine the sporobiota of critically important food sources for the developing infant, breast milk and powdered infant formula.

MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

The Limits and Avoidance of Biases in Metagenomic Analyses of Human Fecal Microbiota

An increasing body of evidence highlights the role of fecal microbiota in various human diseases. However, more than two-thirds of fecal bacteria cannot be cultivated by routine laboratory techniques. Thus, physicians and scientists use DNA sequencing and statistical tools to identify associations between bacterial subgroup abundances and disease. However, discrepancies between studies weaken these results. In the present study, we focus on biases that might account for these discrepancies. First, three different DNA extraction methods (G’NOME, QIAGEN, and PROMEGA) were compared with regard to their efficiency, i.e., the quality and quantity of DNA recovered from feces of 10 healthy volunteers. Then, the impact of the DNA extraction method on the bacteria identification and quantification was evaluated using our published cohort of sample subjected to both 16S rRNA sequencing and whole metagenome sequencing (WMS). WMS taxonomical assignation employed the universal marker genes profiler mOTU-v2, which is considered the gold standard. The three standard pipelines for 16S RNA analysis (MALT and MEGAN6, QIIME1, and DADA2) were applied for comparison. Taken together, our results indicate that the G’NOME-based method was optimal in terms of quantity and quality of DNA extracts. 16S rRNA sequence-based identification of abundant bacteria genera showed acceptable congruence with WMS sequencing, with the DADA2 pipeline yielding the highest congruent levels. However, for low abundance genera (<0.5% of the total abundance) two pipelines and/or validation by quantitative polymerase chain reaction (qPCR) or WMS are required. Hence, 16S rRNA sequencing for bacteria identification and quantification in clinical and translational studies should be limited to diagnostic purposes in well-characterized and abundant genera. Additional techniques are warranted for low abundant genera, such as WMS, qPCR, or the use of two bio-informatics pipelines.

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).