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

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

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

A long journey to treat epilepsy with the gut microbiota

Epilepsy is a common neurological disorder that affects approximately 10.5 million children worldwide. Approximately 33% of affected patients exhibit resistance to all available antiseizure medications, but the underlying mechanisms are unknown and there is no effective treatment. Increasing evidence has shown that an abnormal gut microbiota may be associated with epilepsy. The gut microbiota can influence the function of the brain through multiple pathways, including the neuroendocrine, neuroimmune, and autonomic nervous systems. This review discusses the interactions between the central nervous system and the gastrointestinal tract (the brain-gut axis) and the role of the gut microbiota in the pathogenesis of epilepsy. However, the exact gut microbiota involved in epileptogenesis is unknown, and no consistent results have been obtained based on current research. Moreover, the target that should be further explored to identify a novel antiseizure drug is unclear. The role of the gut microbiota in epilepsy will most likely be uncovered with the development of genomics technology.

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

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

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

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

Organoids as an in vitro model to study human tumors and bacteria

Organoids faithfully replicate the morphological structure, physiological functions, stable phenotype of the source tissue. Recent research indicates that bacteria can significantly influence the initiation, advancement, and treatment of tumors. This article provides a comprehensive review of the applications of organoid technology in tumor research, the relationship between bacteria and the genesis and development of tumors, and the exploration of the impact of bacteria on tumors and their applications in research.

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

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

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

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

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

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

Tracking and characterization of a novel conjugative transposon identified by shotgun transposon mutagenesis

The horizontal transfer of mobile genetic elements (MGEs) is an essential process determining the functional and genomic diversity of bacterial populations. MGEs facilitate the exchange of fitness determinant genes like antibiotic resistance and virulence factors. Various computational methods exist to identify potential MGEs, but confirming their ability to transfer requires additional experimental approaches. Here, we apply a transposon (Tn) mutagenesis technique for confirming mobilization without the need for targeted mutations. Using this method, we identified two MGEs, including a previously known conjugative transposon (CTn) called BoCTn found in Bacteroides ovatus and a novel CTn, PvCTn, identified in Phocaeicola vulgatus. In addition, Tn mutagenesis and subsequent genetic deletion enabled our characterization of a helix-turn-helix motif gene, BVU3433 which negatively regulates the conjugation efficiency of PvCTn in vitro. Furthermore, our transcriptomics data revealed that BVU3433 plays a crucial role in the repression of PvCTn genes, including genes involved in forming complete conjugation machinery [Type IV Secretion System (T4SS)]. Finally, analysis of individual strain genomes and community metagenomes identified the widespread prevalence of PvCTn-like elements with putative BVU3433 homologs among human gut-associated bacteria. In summary, this Tn mutagenesis mobilization method (TMMM) enables observation of transfer events in vitro and can ultimately be applied in vivo to identify a broader diversity of functional MGEs that may underly the transfer of important fitness determinants.

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

Black rice diet alleviates colorectal cancer development through modulating tryptophan metabolism and activating AHR pathway

Consumption of dietary fiber and anthocyanin has been linked to a lower incidence of colorectal cancer (CRC). This study scrutinizes the potential antitumorigenic attributes of a black rice diet (BRD), abundantly rich in dietary fiber and anthocyanin. Our results demonstrate notable antitumorigenic effects in mice on BRD, indicated by a reduction in both the size and number of intestinal tumors and a consequent extension in life span, compared to control diet-fed counterparts. Furthermore, fecal transplants from BRD-fed mice to germ-free mice led to a decrease in colonic cell proliferation, coupled with maintained integrity of the intestinal barrier. The BRD was associated with significant shifts in gut microbiota composition, specifically an augmentation in probiotic strains Bacteroides uniformis and Lactobacillus. Noteworthy changes in gut metabolites were also documented, including the upregulation of indole-3-lactic acid and indole. These metabolites have been identified to stimulate the intestinal aryl hydrocarbon receptor pathway, inhibiting CRC cell proliferation and colorectal tumorigenesis. In summary, these findings propose that a BRD may modulate the progression of intestinal tumors by fostering protective gut microbiota and metabolite profiles. The study accentuates the potential health advantages of whole-grain foods, emphasizing the potential utility of black rice in promoting health.

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

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

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

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

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

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Visual analysis of colorectal cancer and gut microbiota: A bibliometric analysis from 2002 to 2022

A growing number of studies have shown that gut microbiota (GM) plays an essential role in the occurrence and development of colorectal cancer (CRC). The current body of research exploring the relationship between CRC and GM is vast. Nevertheless, bibliometric studies in this area have not yet been reported. This study aimed to explore the hotspots and frontiers of research on GM and CRC in the past 20 years, which may provide a reference for researchers in this field. The Web of Science Core Collection database was searched for publications on CRC and GM from 2002 to 2022. The scientometric softwares CiteSpace and VOSviewer were used to visually analyze the countries, institutions, authors, journals, and keywords involved in the literature. Keywords co-occurrence, cluster, and burst analysis were utilized to further explore the current state and development trends of research on GM and CRC. A total of 2158 publications were included in this study, with a noticeably rising annual publication trend. The majority of these papers are from 80 nations, primarily China and the USA. J Yu was the most active author and WS Garrett has the highest citation. Among all institutions, Shanghai Jiao Tong University has the largest number of papers. Most of the publications were published in the International Journal of Molecular Sciences, with Science being the most frequently cited journal. The 4 main clusters mainly involved probiotics, inflammation, molecular mechanisms, and research methods. Current research hotspots included "Fusobacterium nucleatum," "Escherichia coli," etc. Newly emerging research has focused predominantly on immune response, gene expression, and recent strategies for the treatment of CRC with GM. The relationship between GM and CRC will continue to be a hot research area. Changes in the composition of GM in patients with CRC, the potential molecular mechanisms as well as probiotics and natural products used in the treatment of CRC have been the focus of current research and hotspots for future studies.

A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression

Metagenomic data compression is very important as metagenomic projects are facing the challenges of larger data volumes per sample and more samples nowadays. Reference-based compression is a promising method to obtain a high compression ratio. However, existing microbial reference genome databases are not suitable to be directly used as references for compression due to their large size and redundancy, and different metagenomic cohorts often have various microbial compositions. We present a novel pipeline that generated simplified and tailored reference genomes for large metagenomic cohorts, enabling the reference-based compression of metagenomic data. We constructed customized reference genomes, ranging from 2.4 to 3.9 GB, for 29 real metagenomic datasets and evaluated their compression performance. Reference-based compression achieved an impressive compression ratio of over 20 for human whole-genome data and up to 33.8 for all samples, demonstrating a remarkable 4.5 times improvement than the standard Gzip compression. Our method provides new insights into reference-based metagenomic data compression and has a broad application potential for faster and cheaper data transfer, storage, and analysis.

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

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

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

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

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

Male-Biased Gut Microbiome and Metabolites Aggravate Colorectal Cancer Development

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

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

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

Turicibacter fermentation enhances the inhibitory effects of Antrodia camphorata supplementation on tumorigenic serotonin and Wnt pathways and promotes ROS-mediated apoptosis of Caco-2 cells

Introduction: Diet-induced obesity has been shown to decrease the abundance of Turicibacter, a genus known to play a role in the serotonin signaling system, which is associated with colorectal tumorigenesis, making the presence of Turicibacter potentially influential in the protection of intestinal tumorigenesis. Recently, Antrodia camphorata (AC), a medicinal fungus native to Taiwan, has emerged as a promising candidate for complementary and alternative cancer therapy. Small molecules and polysaccharides derived from AC have been reported to possess health-promoting effects, including anti-cancer properties. Methods: Bacterial culture followed with cell culture were used in this study to determine the role of Turicibacter in colorectal tumorigenesis and to explore the anti-cancer mechanism of AC with Turicibacter fermentation. Results: Turicibacter fermentation and the addition of AC polysaccharide led to a significant increase in the production of nutrients and metabolites, including α-ketoglutaric acid and lactic acid (p < 0.05). Treatment of Turicibacter fermented AC polysaccharide was more effective in inhibiting serotonin signaling-related genes, including Tph1, Htr1d, Htr2a, Htr2b, and Htr2c (p < 0.05), and Wnt-signaling related protein and downstream gene expressions, such as phospho-GSK-3β, active β-catenin, c-Myc, Ccnd1, and Axin2 (p < 0.05). Additionally, it triggered the highest generation of reactive oxygen species (ROS), which activated PI3K/Akt and MAPK/Erk signaling and resulted in cleaved caspase-3 expression. In comparison, the treatment of AC polysaccharide without Turicibacter fermentation displayed a lesser effect. Discussion: Our findings suggest that AC polysaccharide effectively suppresses the tumorigenic serotonin and Wnt-signaling pathways, and promotes ROS-mediated apoptosis in Caco-2 cells. These processes are further enhanced by Turicibacter fermentation.

Streptococcus gallolyticus infection: A neglected marker for colorectal cancer?

BACKGROUND AND STUDY AIMS

Colorectal cancer (CRC) is the second most common cancer in Malaysia and mostly detected at advanced stages due to lack of awareness of CRC symptoms and signs. CRC pathogenesis is multifactorial, and there is ambiguous evidence on association of Streptococcus gallolyticus infection with CRC that needs further attention. Thus, a case-control study was conducted to determine whether S. gallolyticus infection is a predictor for CRC occurrence among patients attending Sultan Ahmad Shah Medical Centre@IIUM (SASMEC@IIUM).

PATIENTS AND METHODS

A total of 33 stool samples from patients diagnosed with CRC and 80 from patients without CRC attending surgical clinic of SASMEC@IIUM were collected and analyzed with iFOBT test and PCR assay to detect S. gallolyticus.

RESULTS

In this study, the proportion of S. gallolyticus infection was higher among patients with CRC (48.5%) compared with the control group (20%). Univariate analysis shows that occult blood in stool, S. gallolyticus infection and family history were significantly associated with the development of CRC (P < 0.05). Using the multivariate logistic regression model, positive stool PCR for S. gallolyticus had the lowest relative standard error and almost five times the odds of developing CRC after adjusting other factors (adjusted odds ratio = 4.7, 95% confidence interval = 1.7-12.6, relative standard error = 59.6%).

CONCLUSION

This finding suggests that S. gallolyticus infection was the strongest predictor of CRC's development in our study and potentially serves as a predictive marker for early detection of disease progression.

Resistome expansion in disease-associated human gut microbiomes

BACKGROUND

The resistome, the collection of antibiotic resistance genes (ARGs) in a microbiome, is increasingly recognised as relevant to the development of clinically relevant antibiotic resistance. Many metagenomic studies have reported resistome differences between groups, often in connection with disease and/or antibiotic treatment. However, the consistency of resistome associations with antibiotic- and non-antibiotic-treated diseases has not been established. In this study, we re-analysed human gut microbiome data from 26 case-control studies to assess the link between disease and the resistome.

RESULTS

The human gut resistome is highly variable between individuals both within and between studies, but may also vary significantly between case and control groups even in the absence of large taxonomic differences. We found that for diseases commonly treated with antibiotics, namely cystic fibrosis and diarrhoea, patient microbiomes had significantly elevated ARG abundances compared to controls. Disease-associated resistome expansion was found even when ARG abundance was high in controls, suggesting ongoing and additive ARG acquisition in disease-associated strains. We also found a trend for increased ARG abundance in cases from some studies on diseases that are not treated with antibiotics, such as colorectal cancer.

CONCLUSIONS

Diseases commonly treated with antibiotics are associated with expanded gut resistomes, suggesting that historical exposure to antibiotics has exerted considerable selective pressure for ARG acquisition in disease-associated strains. Video Abstract.

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer

Background

Colorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied.

Methods

We investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC.

Results

The plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUC_composite = 0.804 and mean AUC_bacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUC_composite = 0.839 and mean AUC_bacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC.

Conclusion

We identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy.

Urea cycle activation triggered by host-microbiota maladaptation driving colorectal tumorigenesis

Maladaptation of host-microbiota metabolic interplay plays a critical role in colorectal cancer initiation. Here, through a combination of single-cell transcriptomics, microbiome profiling, metabonomics, and clinical analysis on colorectal adenoma and carcinoma tissues, we demonstrate that host's urea cycle metabolism is significantly activated during colorectal tumorigenesis, accompanied by the absence of beneficial bacteria with ureolytic capacity, such as Bifidobacterium, and the overabundance of pathogenic bacteria lacking ureolytic function. Urea could enter into macrophages, inhibit the binding efficiency of p-STAT1 to SAT1 promotor region, and further skew macrophages toward a pro-tumoral phenotype characterized by the accumulation of polyamines. Treating a murine model using urea cycle inhibitors or Bifidobacterium-based supplements could mitigate urea-mediated tumorigenesis. Collectively, this study highlights the utility of urea cycle inhibitors or therapeutically manipulating microbial composition using probiotics to prevent colorectal cancer.

Tissue vs. Fecal-Derived Bacterial Dysbiosis in Precancerous Colorectal Lesions: A Systematic Review

Alterations in gut microbiota play a pivotal role in the adenoma-carcinoma sequence. However, there is still a notable lack of the correct implementation of tissue and fecal sampling in the setting of human gut microbiota examination. This study aimed to review the literature and to consolidate the current evidence on the use of mucosa and a stool-based matrix investigating human gut microbiota changes in precancerous colorectal lesions. A systematic review of papers from 2012 until November 2022 published on the PubMed and Web of Science databases was conducted. The majority of the included studies have significantly associated gut microbial dysbiosis with premalignant polyps in the colorectum. Although methodological differences hampered the precise fecal and tissue-derived dysbiosis comparison, the analysis revealed several common characteristics in stool-based and fecal-derived gut microbiota structures in patients with colorectal polyps: simple or advanced adenomas, serrated lesions, and carcinomas in situ. The mucosal samples considered were more relevant for the evaluation of microbiota's pathophysiological involvement in CR carcinogenesis, while non-invasive stool sampling could be beneficial for early CRC detection strategies in the future. Further studies are required to identify and validate mucosa-associated and luminal colorectal microbial patterns and their role in CRC carcinogenesis, as well as in the clinical setting of human microbiota studies.

Role of microbiota and microbiota-derived short-chain fatty acids in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.

Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype

While the association between colorectal cancer (CRC) features and Fusobacterium has been extensively studied, less is known of other intratumoral bacteria. Here, we leverage whole transcriptomes from 807 CRC samples to dually characterize tumor gene expression and 74 intratumoral bacteria. Seventeen of these species, including 4 Fusobacterium spp., are classified as orally derived and are enriched among right-sided, microsatellite instability-high (MSI-H), and BRAF-mutant tumors. Across consensus molecular subtypes (CMSs), integration of Fusobacterium animalis (Fa) presence and tumor expression reveals that Fa has the most significant associations in mesenchymal CMS4 tumors despite a lower prevalence than in immune CMS1. Within CMS4, the prevalence of Fa is uniquely associated with collagen- and immune-related pathways. Additional Fa pangenome analysis reveals that stress response genes and the adhesion FadA are commonly expressed intratumorally. Overall, this study identifies oral-derived bacteria as enriched in inflamed tumors, and the associations of bacteria and tumor expression are context and species specific.

Leveraging explainable AI for gut microbiome-based colorectal cancer classification

Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.

Colon Cancer Microbiome Landscaping: Differences in Right- and Left-Sided Colon Cancer and a Tumor Microbiome-Ileal Microbiome Association

In the current era of precision oncology, it is widely acknowledged that CRC is a heterogeneous disease entity. Tumor location (right- or left-sided colon cancer or rectal cancer) is a crucial factor in determining disease progression as well as prognosis and influences disease management. In the last decade, numerous works have reported that the microbiome is an important element of CRC carcinogenesis, progression and therapy response. Owing to the heterogeneous nature of microbiomes, the findings of these studies were inconsistent. The majority of the studies combined colon cancer (CC) and rectal cancer (RC) samples as CRC for analysis. Furthermore, the small intestine, as the major site for immune surveillance in the gut, is understudied compared to the colon. Thus, the CRC heterogeneity puzzle is far from being solved, and more research is necessary for prospective trials that separately investigate CC and RC. Our prospective study aimed to map the colon cancer landscape using 16S rRNA amplicon sequencing in biopsy samples from the terminal ileum, healthy colon tissue, healthy rectal tissue and tumor tissue as well as in preoperative and postoperative stool samples of 41 patients. While fecal samples provide a good approximation of the average gut microbiome composition, mucosal biopsies allow for detecting subtle variations in local microbial communities. In particular, the small bowel microbiome has remained poorly characterized, mainly because of sampling difficulties. Our analysis revealed the following: (i) right- and left-sided colon cancers harbor distinct and diverse microbiomes, (ii) the tumor microbiome leads to a more consistent cancer-defined microbiome between locations and reveals a tumor microbiome-ileal microbiome association, (iii) the stool only partly reflects the microbiome landscape in patients with CC, and (iv) mechanical bowel preparation and perioperative antibiotics together with surgery result in major changes in the stool microbiome, characterized by a significant increase in the abundance of potentially pathogenic bacteria, such as Enterococcus. Collectively, our results provide new and valuable insights into the complex microbiome landscape in patients with colon cancer.

Gut Microbiota in Colorectal Cancer: Biological Role and Therapeutic Opportunities

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths worldwide. While CRC is thought to be an interplay between genetic and environmental factors, several lines of evidence suggest the involvement of gut microbiota in promoting inflammation and tumor progression. Gut microbiota refer to the ~40 trillion microorganisms that inhabit the human gut. Advances in next-generation sequencing technologies and metagenomics have provided new insights into the gut microbial ecology and have helped in linking gut microbiota to CRC. Many studies carried out in humans and animal models have emphasized the role of certain gut bacteria, such as Fusobacterium nucleatum, enterotoxigenic Bacteroides fragilis, and colibactin-producing Escherichia coli, in the onset and progression of CRC. Metagenomic studies have opened up new avenues for the application of gut microbiota in the diagnosis, prevention, and treatment of CRC. This review article summarizes the role of gut microbiota in CRC development and its use as a biomarker to predict the disease and its potential therapeutic applications.

Understanding the role of the gut microbiome in gastrointestinal cancer: A review

Gastrointestinal cancer represents one of the most diagnosed types of cancer. Cancer is a genetic and multifactorial disease, influenced by the host and environmental factors. It has been stated that 20% of cancer is caused by microorganisms such as Helicobacter pylori, hepatitis B and C virus, and human papillomavirus. In addition to these well-known microorganisms associated with cancer, it has been shown differences in the composition of the microbiota between healthy individuals and cancer patients. Some studies have suggested the existence of the selected microorganisms and their metabolites that can promote or inhibit tumorigenesis via some mechanisms. Recent findings have shown that gut microbiome and their metabolites can act as cancer promotors or inhibitors. It has been shown that gastrointestinal cancer can be caused by a dysregulation of the expression of non-coding RNA (ncRNA) through the gut microbiome. This review will summarize the latest reports regarding the relationship among gut microbiome, ncRNAs, and gastrointestinal cancer. The potential applications of diagnosing and cancer treatments will be discussed.

Long-term follow-up of colorectal cancer screening attendees identifies differences in Phascolarctobacterium spp. using 16S rRNA and metagenome sequencing

Background

The microbiome has been implicated in the initiation and progression of colorectal cancer (CRC) in cross-sectional studies. However, there is a lack of studies using prospectively collected samples.

Methods

From the Norwegian Colorectal Cancer Prevention (NORCCAP) trial, we analyzed 144 archived fecal samples from participants who were diagnosed with CRC or high-risk adenoma (HRA) at screening and from participants who remained cancer-free during 17 years of follow-up. We performed 16S rRNA sequencing of all the samples and metagenome sequencing on a subset of 47 samples. Differences in taxonomy and gene content between outcome groups were assessed for alpha and beta diversity and differential abundance.

Results

Diversity and composition analyses showed no significant differences between CRC, HRA, and healthy controls. Phascolarctobacterium succinatutens was more abundant in CRC compared with healthy controls in both the 16S and metagenome data. The abundance of Bifidobacterium and Lachnospiraceae spp. was associated with time to CRC diagnosis.

Conclusion

Using a longitudinal study design, we identified three taxa as being potentially associated with CRC. These should be the focus of further studies of microbial changes occurring prior to CRC diagnosis.

Implication of gut microbes and its metabolites in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer with a significant impact on loss of life. In 2020, nearly 1.9 million new cases and over 9,35,000 deaths were reported. Numerous microbes that are abundant in the human gut benefit host physiology in many ways. Although the underlying mechanism is still unknown, their association appears to be crucial in the beginning and progression of CRC. Diet has a significant impact on the microbial composition and may increase the chance of getting CRC. Increasing evidence points to the gut microbiota as the primary initiator of colonic inflammation, which is connected to the development of colonic tumors. However, it is unclear how the microbiota contributes to the development of CRCs. Patients with CRC have been found to have dysbiosis of the gut microbiota, which can be identified by a decline in commensal bacterial species, such as those that produce butyrate, and a concurrent increase in harmful bacterial populations, such as opportunistic pathogens that produce pro-inflammatory cytokines. We believe that using probiotics or altering the gut microbiota will likely be effective tools in the fight against CRC treatment.

PURPOSE

In this review, we revisited the association between gut microbiota and colorectal cancer whether cause or effect. The various factors which influence gut microbiome in patients with CRC and possible mechanism in relation with development of CRC.

CONCLUSION

The clinical significance of the intestinal microbiota may aid in the prevention and management of CRC.

Shotgun Metagenomics of Gastric Biopsies Reveals Compositional and Functional Microbiome Shifts in High- and Low-Gastric-Cancer-Risk Populations from Colombia, South America

Along with Helicobacter pylori infection, the gastric microbiota is hypothesized to modulate stomach cancer risk in susceptible individuals. Whole metagenomic shotgun sequencing (WMS) is a sequencing approach to characterize the microbiome with advantages over traditional culture and 16S rRNA sequencing including identification of bacterial and non-bacterial taxa, species/strain resolution, and functional characterization of the microbiota. In this study, we used WMS to survey the microbiome in extracted DNA from antral gastric biopsy samples from Colombian patients residing in the high-risk gastric cancer town Túquerres (n = 10, H. pylori-positive = 7) and low-risk town of Tumaco (n = 10, H. pylori-positive = 6). Kraken2/Bracken was used for taxonomic classification and abundance. Functional gene profiles were inferred by InterProScan and KEGG analysis of assembled contigs and gene annotation. The most abundant taxa represented bacteria, non-human eukaryota, and viral genera found in skin, oral, food, and plant/soil environments including Staphylococus, Streptococcus, Bacillus, Aspergillus, and Siphoviridae. H. pylori was the predominant taxa present in H. pylori-positive samples. Beta diversity was significantly different based on H. pylori-status, risk group, and sex. WMS detected more bacterial taxa than 16S rRNA sequencing and aerobic, anaerobic, and microaerobic culture performed on the same gastric biopsy samples. WMS identified significant differences in functional profiles found between H. pylori-status, but not risk or sex groups. H. pylori-positive samples were significantly enriched for H. pylori-specific genes including virulence factors such as vacA, cagA, and urease, while carbohydrate and amino acid metabolism genes were enriched in H. pylori-negative samples. This study shows WMS has the potential to characterize the taxonomy and function of the gastric microbiome as risk factors for H. pylori-associated gastric disease. Future studies will be needed to compare and validate WMS versus traditional culture and 16S rRNA sequencing approaches for characterization of the gastric microbiome.

Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis

BACKGROUND

The gut microbiome promotes specific immune responses, and in turn, the immune system has a hand in shaping the microbiome. Cancer and autoimmune diseases are two major disease families that result from the contrasting manifestations of immune dysfunction. We hypothesized that the opposing immunological profiles between cancer and autoimmunity yield analogously inverted gut microbiome signatures. To test this, we conducted a systematic review and meta-analysis on gut microbiome signatures and their directionality in cancers and autoimmune conditions.

METHODOLOGY

We searched PubMed, Web of Science, and Embase to identify relevant articles to be included in this study. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements and PRISMA 2009 checklist. Study estimates were pooled by a generic inverse variance random-effects meta-analysis model. The relative abundance of microbiome features was converted to log fold change, and the standard error was calculated from the p-values, sample size, and fold change.

RESULTS

We screened 3874 potentially relevant publications. A total of 82 eligible studies comprising 37 autoimmune and 45 cancer studies with 4208 healthy human controls and 5957 disease cases from 27 countries were included in this study. We identified a set of microbiome features that show consistent, opposite directionality between cancers and autoimmune diseases in multiple studies. Fusobacterium and Peptostreptococcus were the most consistently increased genera among the cancer cases which were found to be associated in a remarkable 13 (+0.5 log fold change in 5 studies) and 11 studies (+3.6 log fold change in 5 studies), respectively. Conversely, Bacteroides was the most prominent genus, which was found to be increased in 12 autoimmune studies (+0.2 log fold change in 6 studies) and decreased in six cancer studies (-0.3 log fold change in 4 studies). Sulfur-metabolism pathways were found to be the most frequent pathways among the member of cancer-increased genus and species.

CONCLUSIONS

The surprising reproducibility of these associations across studies and geographies suggests a shared underlying mechanism shaping the microbiome across cancers and autoimmune diseases. Video Abstract.

Community-led risk analysis of direct-to-consumer whole-genome sequencing

Direct-to-consumer (DTC) genetic testing is cheaper and more accessible than ever before; however, the intention to combine, reuse, and resell this genetic information as powerful data sets is generally hidden from the consumer. This financial gain is creating a competitive DTC market, reducing the price of whole-genome sequencing (WGS) to under 300 USD. Entering this transition from single-nucleotide polymorphism-based DTC testing to WGS DTC testing, individuals looking for access to their whole-genomic information face new privacy and security risks. Differences between WGS and other methods of consumer genetic tests are left unexplored by regulation, leading to the application of legal data anonymization methods on whole-genome data, and questionable consent methods. Large representative genomic data sets are important for research and improve the standard of medicine and personalized care. However, these data can also be used by market players, law enforcement, and governments for surveillance, population analyses, marketing purposes, and discrimination. Here, we present a summary of the state of WGS DTC genetic testing and its current regulation, through a community-based lens to expose dual-use risks in consumer-facing biotechnologies.

Microbiome analysis reveals universal diagnostic biomarkers for colorectal cancer across populations and technologies

The gut microbial dysbiosis is a risk of colorectal cancer (CRC) and some bacteria have been reported as potential markers for CRC diagnosis. However, heterogeneity among studies with different populations and technologies lead to inconsistent results. Here, we investigated six metagenomic profiles of stool samples from healthy controls (HC), colorectal adenoma (CA) and CRC, and six and four genera were consistently altered between CRC and HC or CA across populations, respectively. In FengQ cohort, which composed with 61 HC, 47 CA, and 46 CRC samples, a random forest (RF) model composed of the six genera, denoted as signature-HC, distinguished CRC from HC with an area under the curve (AUC) of 0.84. Similarly, another RF model composed of the four universal genera, denoted as signature-CA, discriminated CRC from CA with an AUC of 0.73. These signatures were further validated in five metagenomic sequencing cohorts and six independent 16S rRNA gene sequencing cohorts. Interestingly, three genera overlapped in the two models (Porphyromonas, Parvimonas and Peptostreptococcus) were with very low abundance in HC and CA, but sharply increased in CRC. A concise RF model on the three genera distinguished CRC from HC or CA with AUC of 0.87 and 0.67, respectively. Functional gene family analysis revealed that Kyoto Encyclopedia of Genes and Genomes Orthogroups categories which were significantly correlated with markers in signature-HC and signature-CA were mapped into pathways related to lipopolysaccharide and sulfur metabolism, which might be vital risk factors of CRC development. Conclusively, our study identified universal bacterial markers across populations and technologies as potential aids in non-invasive diagnosis of CRC.

Molecular characterization of gut microbial structure and diversity associated with colorectal cancer patients in Egypt

Introduction

a large number of microbes colonizing the gut are highly diverse and complex in their structure, as this complex structure of gut microbiota acts as an indicator of a diseased state. Recently, there is a need for improved biomarkers for colorectal cancer (CRC) and advanced adenoma. Among the CRC associated organisms, bacteria are the most common causes of serious disease and deaths. To understand the dynamic interaction among bacteria colonizing the gut, different approaches have been implicated.

Methods

in this study, faecal microbial markers were evaluated for detecting CRC. As most of these organisms are anaerobic, different molecular tools are of great values for rapid detection of these bacteria. Samples from Tumor Hospital were screened for the presence of different pathogens by both usual polymerase chain reaction (PCR) and a real-time assay.

Results

in a total of 34 samples, by PCR method, bifidobacterium, fusobacterium and Escherichia coli (E. coli) were mainly identified in almost all samples. However, a clear variation in bacterial composition could be observed in Porphyromonas gingivalis, Prevotella intermedia and Peptostreptococcus magnus, where positive results could be detected only in diseased samples. In addition, E. faecium and E. saphenum were mainly identified in diseased samples. In contrast, providencia could be detected mainly in control samples. In realtime assay, the relative abundance was higher for fusobacterium and bifidobacterium markers in CRC patients compared to control samples. However, such increased in abundance has never been observed in both fusobacterium and bifidobacterium in the same sample.

Conclusion

these results demonstrated increased abundance of fusobacterium or bifidobacterium can be considered as a sign for impairment or a diseased condition and the possibility of use of the faecal microbiotain CRC patients as a marker for detecting the disease.

Differences in the gut microbiome by physical activity and BMI among colorectal cancer patients

Associations of energy balance components, including physical activity and obesity, with colorectal cancer risk and mortality are well established. However, the gut microbiome has not been investigated as underlying mechanism. We investigated associations of physical activity, BMI, and combinations of physical activity/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. N=179 patients with colorectal cancer (stages I-IV) were included in the study. Pre-surgery stool samples were used to perform 16S rRNA gene sequencing (Illumina). Physical activity (MET hrs/wk) during the year before diagnosis was assessed by questionnaire and participants were classified as being active vs. inactive based on guidelines. BMI at baseline was abstracted from medical records. Patients were classified into four combinations of physical activity levels/BMI. Lower gut microbial diversity was observed among 'inactive' vs. 'active' patients (Shannon: P=0.01, Simpson: P=0.03), 'obese' vs. 'normal weight' patients (Shannon, Simpson, and Observed species: P=0.02, respectively), and 'overweight/obese/inactive' vs. 'normal weight/active' patients (Shannon: P=0.02, Observed species: P=0.04). Results differed by sex and tumor site. Two phyla and 12 genera (Actinobacteria and Fusobacteria, Adlercreutzia, Anaerococcus, Clostridium, Eubacterium, Mogibacteriaceae, Olsenella, Peptinophilus, Pyramidobacter, RFN20, Ruminococcus, Succinivibrio, Succiniclasticum) were differentially abundant across physical activity and BMI groups. This is the first evidence for associations of physical activity with gut microbiome diversity and abundances, directly among colorectal cancer patients. Our results indicate that physical activity may offset gut microbiome dysbiosis due to obesity. Alterations in gut microbiota may contribute mechanistically to the energy balance-colorectal cancer link and impact clinical outcomes.

Meta-analysis of fecal viromes demonstrates high diagnostic potential of the gut viral signatures for colorectal cancer and adenoma risk assessment

INTRODUCTION

Viruses have been reported as inducers of tumorigenesis. Little studies have explored the impact of the gut virome on the progression of colorectal cancer. However, there is still a problem with the repeatability of viral signatures across multiple cohorts.

OBJECTIVES

The present study aimed to reveal the repeatable gut vial signatures of colorectal cancer and adenoma patients and decipher the potential of viral markers in disease risk assessment for diagnosis.

METHODS

1,282 available fecal metagenomes from 9 published studies for colorectal cancer and adenoma were collected. A gut viral catalog was constructed via a reference-independent approach. Viral signatures were identified by cross-cohort meta-analysis and used to build predictive models based on machine learning algorithms. New fecal samples were collected to validate the generalization of predictive models.

RESULTS

The gut viral composition of colorectal cancer patients was drastically altered compared with healthy, as evidenced by changes in some Siphoviridae and Myoviridae viruses and enrichment of Microviridae, whereas the virome variation in adenoma patients was relatively low. Cross-cohort meta-analysis identified 405 differential viruses for colorectal cancer, including several phages of Porphyromonas, Fusobacterium, and Hungatella that were enriched in patients and some control-enriched Ruminococcaceae phages. In 9 discovery cohorts, the optimal risk assessment model obtained an average cross-cohort area under the curve of 0.830 for discriminating colorectal cancer patients from controls. This model also showed consistently high accuracy in 2 independent validation cohorts (optimal area under the curve, 0.906). Gut virome analysis of adenoma patients identified 88 differential viruses and achieved an optimal area under the curve of 0.772 for discriminating patients from controls.

CONCLUSION

Our findings demonstrate the gut virome characteristics in colorectal cancer and adenoma and highlight gut virus-bacterial synergy in the progression of colorectal cancer. The gut viral signatures may be new targets for colorectal cancer treatment. In addition, high repeatability and predictive power of the prediction models suggest the potential of gut viral biomarkers in non-invasive diagnostic tests of colorectal cancer and adenoma.

Altered Mycobiota Signatures and Enriched Pathogenic Aspergillus rambellii Are Associated With Colorectal Cancer Based on Multicohort Fecal Metagenomic Analyses

BACKGROUND & AIMS

The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC.

METHODS

We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo.

RESULTS

Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%).

CONCLUSIONS

This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC.

Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites

BACKGROUND & AIMS

Studies have reported abnormal gut microbiota or circulating metabolome associated with colorectal cancer (CRC), but it remains a challenge to capture the CRC-relevant features consistent across geographic regions. This is particularly the problem for metabolic traits of CRC because the analyses generally use different platforms and laboratory methods, which poses a barrier to cross-dataset examination. In light of this, we sought to elucidate the microbial and metabolic signatures of CRC with broad population relevance.

METHODS

In this integrated metagenomic (healthy controls [HC], n = 91; colorectal adenoma [CRA], n = 63; CRC, n = 71) and metabolomic (HC, n = 34; CRA, n = 31; CRC, n = 35) analysis, CRC-associated features and microbe-metabolite correlations were first identified from a Shanghai cohort. A gut microbial panel was trained in the in-house cohort and cross-validated in 7 published metagenomic datasets of CRC. The in-house metabolic connections to the cross-cohort microbial signatures were used as evidence to infer serum metabolites with potentially external relevance. In addition, a combined microbe-metabolite panel was produced for diagnosing CRC or adenoma.

RESULTS

CRC-associated alterations were identified in the gut microbiome and serum metabolome. A composite microbe-metabolite diagnostic panel was developed and yielded an area under the curve of 0.912 for adenoma and 0.994 for CRC. We showed that many CRC-associated metabolites were linked to cross-cohort gut microbiome signatures of the disease, including CRC-enriched leucylalanine, serotonin, and imidazole propionate; and CRC-depleted perfluorooctane sulfonate, 2-linoleoylglycerol (18:2), and sphingadienine.

CONCLUSIONS

We generated cross-cohort metagenomic signatures of CRC, some of which linked to in-house CRC-associated serum metabolites. The microbial and metabolic shifts may have wide population relevance.

Expansion of Colorectal Cancer Biomarkers Based on Gut Bacteria and Viruses

Simple Summary

The current study identified microbial (including bacterial and viral) diagnostic models that could discriminate colorectal tumor patients from healthy controls, expanding the potential biomarkers for colorectal tumors. A combination of five colorectal cancer-associated gut bacteria was identified in this study for the discrimination of colorectal cancer patients from healthy controls, with verifiable performance in multiple cohorts. The gene pathways regulated by aberrant gut bacteria were also identified, providing possible directions for studying bacterial carcinogenesis mechanisms. Furthermore, this study revealed the potential interactions of gut bacteria with viruses and within bacteria in adenoma-carcinoma sequences, which may extend our understanding of dysbiosis in colorectal carcinogenesis.

Abstract

The alterations in gut bacteria are closely related to colorectal cancer. However, studies on adenoma are still scarce. Besides, the associations of gut viruses with colorectal tumor, and the interactions of bacteria with viruses in colorectal tumors are still under exploration. Therefore, a metagenomic sequencing of stool samples from patients with colorectal adenoma (CRA), colorectal cancer (CRC), and healthy controls was performed to identify changes in gut microbiome in patients with colorectal tumors. Five CRC-enriched bacteria (Peptostreptococcus stomatis, Clostridium symbiosum, Hungatella hathewayi, Parvimonas micra, and Gemella morbillorum) were identified as a diagnostic model to identify CRC patients, and the efficacy of the diagnostic model was verifiable in 1523 metagenomic samples from ten cohorts of eight different countries. We identified the positive association of Bacteroides fragilis with PD-L1 expression and PD-1 checkpoint pathway, providing a possible direction for studying bacterial carcinogenesis mechanisms. Furthermore, the increased interactions within the microbiome in patients may play roles in the development of CRC. In conclusion, this study identified novel microbiota combinations with discrimination for colorectal tumor, and revealed the potential interactions of gut bacteria with viruses in the adenoma-carcinoma sequence, which implies that the microbiome, but not only bacteria, should be paid more attention in further studies.