16S rRNA Gene Sequencing for Deciphering the Colorectal Cancer Gut Microbiome: Current Protocols and Workflows

Determinants of Metastatic Colorectal Cancer With Permanent Liver- Limited Disease

Colorectal cancer (CRC) is a complex and genetically heterogeneous disease presenting a specific metastatic pattern, with the liver being the most common site of metastasis. Around 20%-25% of patients with CRC will develop exclusively hepatic metastatic disease throughout their disease history. With its specific characteristics and therapeutic options, liver-limited disease (LLD) should be considered as a specific entity. The identification of these patients is particularly relevant in view of the growing interest in liver transplantation in selected patients with advanced CRC. Identifying why some patients will develop only LLD remains a challenge, mainly because of a lack of a systemic understanding of this complex and interlinked phenomenon given that cancer has traditionally been investigated according to distinct physiological compartments. Recently, multidisciplinary efforts and new diagnostic tools have made it possible to study some of these complex issues in greater depth and may help identify targets and specific treatment strategies to benefit these patients. In this review we analyze the underlying biology and available tools to help clinicians better understand this increasingly common and specific disease.

Role of Gut Microbial Metabolites in the Pathogenesis of Primary Liver Cancers

Hepatobiliary malignancies, which include hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are the sixth most common cancers and the third leading cause of cancer-related death worldwide. Hepatic carcinogenesis is highly stimulated by chronic inflammation, defined as fibrosis deposition, and an aberrant imbalance between liver necrosis and nodular regeneration. In this context, the gut-liver axis and gut microbiota have demonstrated a critical role in the pathogenesis of HCC, as dysbiosis and altered intestinal permeability promote bacterial translocation, leading to chronic liver inflammation and tumorigenesis through several pathways. A few data exist on the role of the gut microbiota or bacteria resident in the biliary tract in the pathogenesis of CCA, and some microbial metabolites, such as choline and bile acids, seem to show an association. In this review, we analyze the impact of the gut microbiota and its metabolites on HCC and CCA development and the role of gut dysbiosis as a biomarker of hepatobiliary cancer risk and of response during anti-tumor therapy. We also discuss the future application of gut microbiota in hepatobiliary cancer management.

NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

Novel Wild-Type Pediococcus and Lactiplantibacillus Strains as Probiotic Candidates to Manage Obesity-Associated Insulin Resistance

As the food and pharmaceutical industry is continuously seeking new probiotic strains with unique health properties, the aim of the present study was to determine the impact of short-term dietary intervention with novel wild-type strains, isolated from various sources, on high-fat diet (HFD)-induced insulin resistance. Initially, the strains were evaluated in vitro for their ability to survive in simulated gastrointestinal (GI) conditions, for adhesion to Caco-2 cells, for bile salt hydrolase secretion, for cholesterol-lowering and cellular cholesterol-binding ability, and for growth inhibition of food-borne pathogens. In addition, safety criteria were assessed, including hemolytic activity and susceptibility to antibiotics. The in vivo test on insulin resistance showed that mice receiving the HFD supplemented with Pediococcus acidilactici SK (isolated from human feces) or P. acidilactici OLS3-1 strain (isolated from olive fruit) exhibited significantly improved insulin resistance compared to HFD-fed mice or to the normal diet (ND)-fed group.

Establishment of a consensus protocol to explore the brain pathobiome in patients with mild cognitive impairment and Alzheimer's disease: Research outline and call for collaboration

Microbial infections of the brain can lead to dementia, and for many decades microbial infections have been implicated in Alzheimer's disease (AD) pathology. However, a causal role for infection in AD remains contentious, and the lack of standardized detection methodologies has led to inconsistent detection/identification of microbes in AD brains. There is a need for a consensus methodology; the Alzheimer's Pathobiome Initiative aims to perform comparative molecular analyses of microbes in post mortem brains versus cerebrospinal fluid, blood, olfactory neuroepithelium, oral/nasopharyngeal tissue, bronchoalveolar, urinary, and gut/stool samples. Diverse extraction methodologies, polymerase chain reaction and sequencing techniques, and bioinformatic tools will be evaluated, in addition to direct microbial culture and metabolomic techniques. The goal is to provide a roadmap for detecting infectious agents in patients with mild cognitive impairment or AD. Positive findings would then prompt tailoring of antimicrobial treatments that might attenuate or remit mounting clinical deficits in a subset of patients.

Exploring the gut microbiota: lifestyle choices, disease associations, and personal genomics

The gut microbiota is a rich and dynamic ecosystem that actively interacts with the human body, playing a significant role in the state of health and disease of the host. Diet, exercise, mental health, and other factors have exhibited the ability to influence the gut bacterial composition, leading to changes that can prevent and improve, or favor and worsen, both intestinal and extra-intestinal conditions. Altered gut microbial states, or 'dysbiosis', associated with conditions and diseases are often characterized by shifts in bacterial abundance and diversity, including an impaired Firmicutes to Bacteroidetes ratio. By understanding the effect of lifestyle on the gut microbiota, personalized advice can be generated to suit each individual profile and foster the adoption of lifestyle changes that can both prevent and ameliorate dysbiosis. The delivery of effective and reliable advice, however, depends not only on the available research and current understanding of the topic, but also on the methods used to assess individuals and to discover the associations, which can introduce bias at multiple stages. The aim of this review is to summarize how human gut microbial variability is defined and what lifestyle choices and diseases have shown association with gut bacterial composition. Furthermore, popular methods to investigate the human gut microbiota are outlined, with a focus on the possible bias caused by the lack of use of standardized methods. Finally, an overview of the current state of personalized advice based on gut microbiota testing is presented, underlining its power and limitations.

A Two-Step Single Plex PCR Method for Evaluating Key Colonic Microbiota Markers in Young Mexicans with Autism Spectrum Disorders: Protocol and Pilot Epidemiological Application

Many neurological disorders have a distinctive colonic microbiome (CM) signature. Particularly, children with autism spectrum disorders (ASD) exhibit a very dissimilar CM when compared to neurotypical (NT) ones, mostly at the species level. Thus far, knowledge on this matter comes from high-throughput (yet very expensive and time-consuming) analytical platforms, such as massive high-throughput sequencing of bacterial 16S rRNA. Here, pure (260/280 nm, ~1.85) stool DNA samples (200 ng.µL^-1) from 48 participants [39 ASD, 9 NT; 3-13 y] were used to amplify four candidate differential CM markers [Bacteroides fragilis (BF), Faecalibacterium prausnitzii (FP), Desulfovibrio vulgaris (DV), Akkermansia muciniphila (AM)], using micro-organism-specific oligonucleotide primers [265 bp (BF), 198 bp (FP), 196 bp (DV), 327 bp (AM)] and a standardized two-step [low (step 1: °Tm-5 °C) to high (stage 2: °Tm-0 °C) astringent annealing] PCR protocol (2S-PCR). The method was sensitive enough to differentiate all CM biomarkers in the studied stool donors [↑ abundance: NT (BF, FP, AM), ASD (DV)], and phylogenetic analysis confirmed the primers' specificity.

Metataxonomic Analysis Demonstrates a Shift in Duodenal Microbiota in Patients with Obstructive Jaundice

The human gastrointestinal tract (GIT) is home to an abundance of diverse microorganisms, and the balance of this microbiome plays a vital role in maintaining a healthy GIT. The obstruction of the flow of bile into the duodenum, resulting in obstructive jaundice (OJ), has a major impact on the health of the affected individual. This study sought to identify changes in the duodenal microbiota in South African patients with OJ compared to those without this disorder. Mucosal biopsies were taken from the duodenum of nineteen jaundiced patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) and nineteen control participants (non-jaundiced patients) undergoing gastroscopy. DNA extracted from the samples was subjected to 16S rRNA amplicon sequencing using the Ion S5 TM sequencing platform. Diversity metrics and statistical correlation analyses with the clinical data were performed to compare duodenal microbial communities in both groups. Differences in the mean distribution of the microbial communities in the jaundiced and non-jaundiced samples were observed; however, this difference did not reach statistical significance. Of note, there was a statistically significant difference between the mean distributions of bacteria comparing jaundiced patients with cholangitis to those without (p = 0.0026). On further subset analysis, a significant difference was observed between patients with benign (Cholelithiasis) and malignant disease, namely, head of pancreas (HOP) mass (p = 0.01). Beta diversity analyses further revealed a significant difference between patients with stone and non-stone related disease when factoring in the Campylobacter-Like Organisms (CLO) test status (p = 0.048). This study demonstrated a shift in the microbiota in jaundiced patients, especially considering some underlying conditions of the upper GI tract. Future studies should aim to verify these findings in a larger cohort.

Identification of a novel gut microbiota signature associated with colorectal cancer in Thai population

Colorectal cancer (CRC) is the third most common cancer worldwide. Dysbiosis of human gut microbiota has been linked to sporadic CRC. This study aimed to compare the gut microbiota profiles of 80 Thai volunteers over 50 years of age among 25 CRC patients, 33 patients with adenomatous polyp, and 22 healthy controls. The 16S rRNA sequencing was utilized to characterize the gut microbiome in both mucosal tissue and stool samples. The results revealed that the luminal microbiota incompletely represented the intestinal bacteria at the mucus layer. The mucosal microbiota in beta diversity differed significantly among the three groups. The stepwise increase of Bacteroides and Parabacteroides according to the adenomas-carcinomas sequence was found. Moreover, linear discriminant analysis effect size showed a higher level of Erysipelatoclostridium ramosum (ER), an opportunistic pathogen in the immunocompromised host, in both sample types of CRC patients. These findings indicated that the imbalance of intestinal microorganisms might involve in CRC tumorigenesis. Additionally, absolute quantitation of bacterial burden by quantitative real-time PCR (qPCR) confirmed the increasing ER levels in both sample types of cancer cases. Using ER as a stool-based biomarker for CRC detection by qPCR could predict CRC in stool samples with a specificity of 72.7% and a sensitivity of 64.7%. These results suggested ER might be a potential noninvasive marker for CRC screening development. However, a larger sample size is required to validate this candidate biomarker in diagnosing CRC.

The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data

BACKGROUND

Microbiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life.

RESULTS

We report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer sequences designed from small subunit ribosomal RNA (rRNA) sequences across the Tree of Life (the 'electronic tree of life', eToL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by confirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. The method reduces the computation time versus metagenomics by a factor of >1000. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. Contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method we report the use of the eToL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved.

CONCLUSIONS

This generic methodology is likely to find wide application in microbiome analysis including diagnostics.

Combining p-values from various statistical methods for microbiome data

MOTIVATION

In the field of microbiome analysis, there exist various statistical methods that have been developed for identifying differentially expressed features, that account for the overdispersion and the high sparsity of microbiome data. However, due to the differences in statistical models or test formulations, it is quite often to have inconsistent significance results across statistical methods, that makes it difficult to determine the importance of microbiome taxa. Thus, it is practically important to have the integration of the result from all statistical methods to determine the importance of microbiome taxa. A standard meta-analysis is a powerful tool for integrative analysis and it provides a summary measure by combining p-values from various statistical methods. While there are many meta-analyses available, it is not easy to choose the best meta-analysis that is the most suitable for microbiome data.

RESULTS

In this study, we investigated which meta-analysis method most adequately represents the importance of microbiome taxa. We considered Fisher's method, minimum value of p method, Simes method, Stouffer's method, Kost method, and Cauchy combination test. Through simulation studies, we showed that Cauchy combination test provides the best combined value of p in the sense that it performed the best among the examined methods while controlling the type 1 error rates. Furthermore, it produced high rank similarity with the true ranks. Through the real data application of colorectal cancer microbiome data, we demonstrated that the most highly ranked microbiome taxa by Cauchy combination test have been reported to be associated with colorectal cancer.

Fermented Black Tea and Its Relationship with Gut Microbiota and Obesity: A Mini Review

Fermentation is one of the world’s oldest techniques for food preservation, nutrient enhancement, and alcohol manufacturing. During fermentation, carbohydrates such as glucose and starch are converted into other molecules, such as alcohol and acid, anaerobically through enzymatic action while generating energy for the microorganism or cells involved. Black tea is among the most popular fermented beverages; it is made from the dried tea leaves of the evergreen shrub plant known as Camellia sinensis. The adequate consumption of black tea is beneficial to health as it contains high levels of flavanols, also known as catechins, which act as effective antioxidants and are responsible for protecting the body against the development of illnesses, such as inflammation, diabetes, hypertension, cancer, and obesity. The prevalence of obesity is a severe public health concern associated with the incidence of various serious diseases and is now increasing, including in Malaysia. Advances in ‘omic’ research have allowed researchers to identify the pivotal role of the gut microbiota in the development of obesity. This review explores fermented black tea and its correlation with the regulation of the gut microbiota and obesity.

Luminal and Tumor-Associated Gut Microbiome Features Linked to Precancerous Lesions Malignancy Risk: A Compositional Approach

Colorectal cancer is the third most commonly diagnosed cancer worldwide. Human gut microbiome plays important roles in protecting against it, as well as contributing to its onset and progression. Identification of specific bacterial taxa associated with early stages of colorectal cancer may help develop effective microbiome-based diagnostics. For precancerous lesions, links of their characteristics to luminal and tumor-associated microbiome composition are to be elucidated. Paired stool and tumor brush biopsy samples were collected from 50 patients with precancerous lesions and early forms of colon cancer; their microbial communities were profiled using high-throughput 16S rRNA sequencing. We showed that the microbiome differences between stool and biopsy samples can be to a high extent computationally corrected. Compositionality-aware statistical analysis of microbiome composition revealed its associations with the number of lesions, lesion type, location and malignization pathway. A major determinant of precancerous lesions malignancy risk-the number of lesions-was positively associated with the abundance of H2S-producing taxa. Our results contribute to the basis for developing early non-invasive colorectal cancer diagnostics via identifying microorganisms likely participating in early stages of cancer pathogenesis.

16S rRNA Gene Sequencing Revealed Changes in Gut Microbiota Composition during Pregnancy and Lactation in Mice Model

The gut microbiota play a vital role in regulating endocrine-mediated metabolism, immunity, and energy metabolism. However, little is known about the gut microbiota and metabolite composition and development throughout pregnancy and lactation. Here, we used amplicon sequencing to analyze the gut microbiota composition of 35 female mice in five stages of pregnancy and lactation, namely, non-receptive (NR) stages, sexually-receptive (SR) stages, at day 15 of pregnancy (Pre_D15), at the day of birth (Del), and at day 10 of lactation (Lac_D10). The results revealed that the α diversity of gut microbiota was significantly increased during pregnancy and lactation. In addition, the principal coordinate analysis (PCoA) conducted on the amplicon sequence variants’ (ASVs’) distribution of the 16S rRNA amplicons indicated that the microbiota composition was significantly different among the five groups. Based on a random forest analysis, Oscillospira, Dehalobacterium, and Alistipes were the most important microbiota. The abundance of Allobaculum, Oscillospira, and Ruminococcus were negatively correlated with the serum progesterone concentration, while the abundance of Oscillospira was positively correlated with the propionate and valerate concentration in the caecal contents. Moreover, the concentration of acetate and propionate in the Del and Lac_D10 stages was significantly lower than in the SR and Pre_D15 stages. Our findings indicate that some gut microbes and metabolites vary considerably at the different stages of pregnancy and during lactation stages, which can potentially be used as microbial biomarkers. These results provide information on the potential use of the identified microbes as probiotics to maintain a healthy pregnancy and lactation.

Bacteriophage manipulation of the microbiome associated with tumour microenvironments-can this improve cancer therapeutic response?

Some cancer treatment failures have been attributed to the tumour microbiota, with implications that microbiota manipulation may improve treatment efficacy. While antibiotics have been used to control bacterial growth, their dysbiotic effects on the microbiome, failure to penetrate biofilms and decreased efficacy due to increasing antimicrobial resistance by bacteria, suggest alternatives are needed. Bacteriophages may provide a precise means for targeting oncobacteria whose relative abundance is increased in tumour tissue microbiomes. Fusobacterium, Streptococcus, Peptostreptococcus, Prevotella, Parvimonas, and Treponema species are prevalent in tumour tissue microbiomes of some cancers. They may promote cancer growth by dampening immunity, stimulating release of proinflammatory cytokines, and directly interacting with cancer cells to stimulate proliferation. Lytic bacteriophages against some of these oncobacteria have been isolated and characterised. The search continues for others. The possibility exists for their testing as adjuncts to complement existing therapies. In this review, we highlight the role of oncobacteria, specifically those whose relative abundance in the intra-tumour microbiome is increased, and discuss the potential for bacteriophages against these micro-organisms to augment existing cancer therapies. The capacity for bacteriophages to modulate immunity and kill specific bacteria makes them suitable candidates to manipulate the tumour microbiome and negate the effects of these oncobacteria.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Comparison of targeted metagenomics and IS-Pro methods for analysing the lung microbiome

Background

Targeted metagenomics and IS-Pro method are two of the many methods that have been used to study the microbiome. The two methods target different regions of the 16 S rRNA gene. The aim of this study was to compare targeted metagenomics and IS-Pro methods for the ability to discern the microbial composition of the lung microbiome of COPD patients.

Methods

Spontaneously expectorated sputum specimens were collected from COPD patients. Bacterial DNA was extracted and used for targeted metagenomics and IS-Pro method. The analysis was performed using QIIME2 (targeted metagenomics) and IS-Pro software (IS-Pro method). Additionally, a laboratory cost per isolate and time analysis was performed for each method.

Results

Statistically significant differences were observed in alpha diversity when targeted metagenomics and IS-Pro methods’ data were compared using the Shannon diversity measure (p-value = 0.0006) but not with the Simpson diversity measure (p-value = 0.84). Distinct clusters with no overlap between the two technologies were observed for beta diversity. Targeted metagenomics had a lower relative abundance of phyla, such as the Proteobacteria, and higher relative abundance of phyla, such as Firmicutes when compared to the IS-Pro method. Haemophilus, Prevotella and Streptococcus were most prevalent genera across both methods. Targeted metagenomics classified 23 % (144/631) of OTUs to a species level, whereas IS-Pro method classified 86 % (55/64) of OTUs to a species level. However, unclassified OTUs accounted for a higher relative abundance when using the IS-Pro method (35 %) compared to targeted metagenomics (5 %). The two methods performed comparably in terms of cost and time; however, the IS-Pro method was more user-friendly.

Conclusions

It is essential to understand the value of different methods for characterisation of the microbiome. Targeted metagenomics and IS-Pro methods showed differences in ability in identifying and characterising OTUs, diversity and microbial composition of the lung microbiome. The IS-Pro method might miss relevant species and could inflate the abundance of Proteobacteria. However, the IS-Pro kit identified most of the important lung pathogens, such as Burkholderia and Pseudomonas and may work in a more diagnostics-orientated setting. Both methods were comparable in terms of cost and time; however, the IS-Pro method was easier to use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02288-x.

Integrating microbiome, transcriptome and metabolome data to investigate gastric disease pathogenesis: a concise review

Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.

Structural and Functional Impacts of Microbiota on Pyropia yezoensis and Surrounding Seawater in Cultivation Farms along Coastal Areas of the Yellow Sea

Pyropia yezoensis is the most important commercial edible red algae in China, carrying a variety of resident microbes at its surface. To understand microbiome diversity, community structure, interactions and functions with hosts in this regard, thalli and seawater sampleswere collected from Yantai and Rizhao cultivation farms in the Yellow Sea. The thalli and seawater samples (n = 12) were collected and studied using an Illumina NovaSeq 6000 platform and 16S ribosomal RNA (rRNA) gene sequencing, along with the consideration of environmental factors. Bacterial communities in association with P. yezoensis and surrounding seawater were predominated by Cyanobacteria, Proteobacteria, and Bacteroidetes. The variability of bacterial communities related to P. yezoensis and seawater were predominantly shaped by nitrate (NO₃), ammonium (NH₄), and temperature. Cluster analysis revealed a close relationship between thalli (RTH and YTH) and seawater (RSW and YSW) in terms of the residing bacterial communities, respectively. PICRUSt analysis revealed the presence of genes associated with amino acid transportation and metabolism, which explained the bacterial dependence on algal-provided nutrients. This study reveals that the diversity of microbiota for P. yezoensis is greatly influenced by abiotic factors and algal organic exudates which trigger chemical signaling and transportation responses from the bacterial community, which in turn activates genes to metabolize subsequent substrates.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.

Analysis of Gut Microbiome Using Explainable Machine Learning Predicts Risk of Diarrhea Associated With Tyrosine Kinase Inhibitor Neratinib: A Pilot Study

Neratinib has great efficacy in treating HER2+ breast cancer but is associated with significant gastrointestinal toxicity. The objective of this pilot study was to understand the association of gut microbiome and neratinib-induced diarrhea. Twenty-five patients (age ≥ 60) were enrolled in a phase II trial evaluating safety and tolerability of neratinib in older adults with HER2+ breast cancer (NCT02673398). Fifty stool samples were collected from 11 patients at baseline and during treatment. 16S rRNA analysis was performed and relative abundance data were generated. Shannon's diversity was calculated to examine gut microbiome dysbiosis. An explainable tree-based approach was utilized to classify patients who might experience neratinib-related diarrhea (grade ≥ 1) based on pre-treatment baseline microbial relative abundance data. The hold-out Area Under Receiver Operating Characteristic and Area Under Precision-Recall Curves of the model were 0.88 and 0.95, respectively. Model explanations showed that patients with a larger relative abundance of Ruminiclostridium 9 and Bacteroides sp. HPS0048 may have reduced risk of neratinib-related diarrhea and was confirmed by Kruskal-Wallis test (p ≤ 0.05, uncorrected). Our machine learning model identified microbiota associated with reduced risk of neratinib-induced diarrhea and the result from this pilot study will be further verified in a larger study.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, identifier NCT02673398.

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Gastrointestinal (GIT) diseases have risen globally in recent years, and early detection of the host’s gut microbiota, typically through fecal material, has become a crucial component for rapid diagnosis of such diseases. Human fecal material is a complex substance composed of undigested macromolecules and particles, and the processing of such matter is a challenge due to the unstable nature of its products and the complexity of the matrix. The identification of these products can be used as an indication for present and future diseases; however, many researchers focus on one variable or marker looking for specific biomarkers of disease. Therefore, the combination of genomics, transcriptomics, proteomics and metabonomics can give a detailed and complete insight into the gut environment. The proper sample collection, sample preparation and accurate analytical methods play a crucial role in generating precise microbial data and hypotheses in gut microbiome research, as well as multivariate data analysis in determining the gut microbiome functionality in regard to diseases. This review summarizes fecal sample protocols involved in profiling coeliac disease.

Development of a robust protocol for the characterization of the pulmonary microbiota

The lack of methodological standardization diminishes the validity of results obtained and the conclusions drawn when studying the lung microbiota. We report the validation of a complete 16S rRNA gene amplicon sequencing workflow, from patient recruitment to bioinformatics, tailored to the constrains of the pulmonary environment. We minimize the impact of contaminants and establish negative controls to track and account for them at every step. Enzymatic and mechanical homogenization combined to commercially available extraction kits allow for a fast and reliable extraction of bacterial DNA. The DNA extraction kits have a significant impact on the bacterial composition of the controls. The bacterial signatures of extracted cancerous and healthy human tissues from 5 patients are highly distinguishable from methodological controls. Our work expands our understanding of low microbial burdened environments analysis. This article is to be a starting point towards methodological standardization and the implementation of proper sampling procedures in the study of lung microbiota.

Nathan Dumont-Leblond et al. present a protocol for lung microbiota analysis, including all steps from patient recruitment to bioinformatics. The data show how methodological variation, such as use of different DNA extraction kits, can impact the results and represent an important step toward methods standardization in the pulmonary microbiome field.

Parvimonas micra, Peptostreptococcus stomatis, Fusobacterium nucleatum and Akkermansia muciniphila as a four-bacteria biomarker panel of colorectal cancer

Dysbiosis of the gut microbiome has been associated with the pathogenesis of colorectal cancer (CRC). We profiled the microbiome of gut mucosal tissues from 18 CRC patients and 18 non-CRC controls of the UKM Medical Centre (UKMMC), Kuala Lumpur, Malaysia. The results were then validated using a species-specific quantitative PCR in 40 CRC and 20 non-CRC tissues samples from the UMBI-UKMMC Biobank. Parvimonas micra, Fusobacterium nucleatum, Peptostreptococcus stomatis and Akkermansia muciniphila were found to be over-represented in our CRC patients compared to non-CRC controls. These four bacteria markers distinguished CRC from controls (AUROC = 0.925) in our validation cohort. We identified bacteria species significantly associated (cut-off value of > 5 fold abundance) with various CRC demographics such as ethnicity, gender and CRC staging; however, due to small sample size of the discovery cohort, these results could not be further verified in our validation cohort. In summary, Parvimonas micra, Fusobacterium nucleatum, Peptostreptococcus stomatis and Akkermansia muciniphila were enriched in our local CRC patients. Nevertheless, the roles of these bacteria in CRC initiation and progression remains to be investigated.

Inflammatory Bowel Diseases: The Role of Gut Microbiota

Inflammatory bowel diseases (IBD) are chronic multifactorial diseases characterized by partially unclear pathogenic mechanisms including changes in intestinal microbiota. Despite the microbiota, alteration is well established in IBD patients, as reported by 16RNA sequencing analysis, an important goal is to define if it is just a consequence of the disease progression or a trigger factor of the disease itself. To date, gut microbiota composition and gut microbiota-related metabolites seem to affect the host healthy state both by modulating metabolic pathways or acting on the expression of different genes through epigenetic effects. Because of this, it has been suggested that intestinal microbiota might represent a promising therapeutic target for IBD patients. The aim of this review is to summarize both the most recent acquisitions in the field of gut microbiota and its involvement in intestinal inflammation together with the available strategies for the modulation of microbiota, such as prebiotics and/or probiotics administration or fecal microbiota transplantation.

Longitudinal determination of resilience in humans to identify mechanisms of resilience to modern-life stressors: the longitudinal resilience assessment (LORA) study

Resilience is the maintenance and/or quick recovery of mental health during and after periods of adversity. It is conceptualized to result from a dynamic process of successful adaptation to stressors. Up to now, a large number of resilience factors have been proposed, but the mechanisms underlying resilience are not yet understood. To shed light on the complex and time-varying processes of resilience that lead to a positive long-term outcome in the face of adversity, the Longitudinal Resilience Assessment (LORA) study has been established. In this study, 1191 healthy participants are followed up at 3- and 18-month intervals over a course of 4.5 years at two study centers in Germany. Baseline and 18-month visits entail multimodal phenotyping, including the assessment of mental health status, sociodemographic and lifestyle variables, resilience factors, life history, neuropsychological assessments (of proposed resilience mechanisms), and biomaterials (blood for genetic and epigenetic, stool for microbiome, and hair for cortisol analysis). At 3-monthly online assessments, subjects are monitored for subsequent exposure to stressors as well as mental health measures, which allows for a quantitative assessment of stressor-dependent changes in mental health as the main outcome. Descriptive analyses of mental health, number of stressors including major life events, daily hassles, perceived stress, and the ability to recover from stress are here presented for the baseline sample. The LORA study is unique in its design and will pave the way for a better understanding of resilience mechanisms in humans and for further development of interventions to successfully prevent stress-related disorder.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

Simple Analysis of Primary and Secondary Bile Salt Hydrolysis in Mouse and Human Gut Microbiome Samples by Using Fluorogenic Substrates

Animals produce bile to act as an antibacterial agent and to maximize the absorption of lipophilic nutrients in the gut. The physical properties of bile are largely dictated by amphipathic bile salt molecules, which also participate in signaling pathways by modulating physiological processes upon binding host receptors. Upon excretion of bile salts from the gall bladder into the intestine, the gut microbiota can create metabolites with modified signaling capabilities. The category and magnitude of bile salt metabolism can have positive or negative effects on the host. A key modification is bile salt hydrolysis, which is a prerequisite for all additional microbial transformations. We have synthesized five different fluorogenic bile salts for simple and continuous reporting of hydrolysis in both murine and human fecal samples. Our data demonstrate that most gut microbiomes have the highest capacity for hydrolysis of host-produced primary bile salts, but some microbially modified secondary bile salts also display significant turnover.

Differential responses of gut microbiota of male and female fathead minnow (Pimephales promelas) to a short-term environmentally-relevant, aqueous exposure to benzo[a]pyrene

In addition to aiding in digestion of food and uptake of nutrients, microbiota in guts of vertebrates are responsible for regulating several beneficial functions, including development of an organism and maintaining homeostasis. However, little is known about effects of exposures to chemicals on structure and function of gut microbiota of fishes. To assess effects of exposure to polycyclic aromatic hydrocarbons (PAHs) on gut microbiota, male and female fathead minnows (Pimephales promelas) were exposed to environmentally-relevant concentrations of the legacy PAH benzo[a]pyrene (BaP) in water. Measured concentrations of BaP ranged from 2.3 × 10^-3 to 1.3 μg L^-1. The community of microbiota in the gut were assessed by use of 16S rRNA metagenetics. Exposure to environmentally-relevant aqueous concentrations of BaP did not alter expression levels of mRNA for cyp1a1, a "classic" biomarker of exposure to BaP, but resulted in shifts in relative compositions of gut microbiota in females rather than males. Results presented here illustrate that in addition to effects on more well-studied molecular endpoints, relative compositions of the microbiota in guts of fish can also quickly respond to exposure to chemicals, which can provide additional mechanisms for adverse effects on individuals.

MinION Sequencing of colorectal cancer tumour microbiomes-A comparison with amplicon-based and RNA-Sequencing

BACKGROUND

Recent evidence suggests a role for the gut microbiome in the development and progression of many diseases and many studies have been carried out to analyse the microbiome using a variety of methods. In this study, we compare MinION sequencing with meta-transcriptomics and amplicon-based sequencing for microbiome analysis of colorectal tumour tissue samples.

METHODS

DNA and RNA were extracted from 11 colorectal tumour samples. 16S rRNA amplicon sequencing and MinION sequencing was carried out using genomic DNA, and RNA-Sequencing for meta-transcriptomic analysis. Non-human MinION and RNA-Sequencing reads, and 16S rRNA amplicon sequencing reads were taxonomically classified using a database built from available RefSeq bacterial and archaeal genomes and a k-mer based algorithm in Kraken2. Concordance between the three platforms at different taxonomic levels was tested on a per-sample basis using Spearman's rank correlation.

RESULTS

The average number of reads per sample using RNA-Sequencing was greater than 129 times that generated using MinION sequencing. However, the average read length of MinION sequences was more than 13 times that of RNA or 16S rRNA amplicon sequencing. Taxonomic assignment using 16S sequencing was less reliable beyond the genus level, and both RNA-Sequencing and MinION sequencing could detect greater numbers of phyla and genera in the same samples, compared to 16S sequencing. Bacterial species associated with colorectal cancer, Fusobacterium nucleatum, Parvimonas micra, Bacteroides fragilis and Porphyromonas gingivalis, were detectable using MinION, RNA-Sequencing and 16S rRNA amplicon sequencing data.

CONCLUSIONS

Long-read sequences generated using MinION sequencing can compensate for low numbers of reads for bacterial classification. MinION sequencing can discriminate between bacterial strains and plasmids and shows potential as a cost-effective tool for rapid microbiome sequencing in a clinical setting.

Exposure to copper altered the intestinal microbiota in Chinese brown frog (Rana chensinensis)

The intestinal microbiota is a crucial physiological system that offers multiple services to the host and contributes to the health of host. However, substantially less is known concerning the interrelation between amphibian gut microbiota and Cu exposure. R. chensinensis larvae were exposed to different concentrations of Cu (0, 0.1, 0.25, 0.75 μM) until reached Gosner stage 38. Histological and morphological data were measured by four Cu exposure conditions. Then, the diversity, structure, and composition of intestinal microbiota were analyzed via 16S rRNA gene sequencing. These results indicated that total body length, intestinal wet weight, and total body wet weight were reduced in 0.75 μM CuSO₄ exposure group. Besides, obvious histopathologic alterations were observed in CuSO₄ exposure groups. Alpha diversity significantly differentiated in 0.75 μM CuSO₄ exposure group, and beta diversity showed 0.1 μM and 0.2 μM CuSO₄ exposure groups separation with the control group. At the phylum level of intestinal microbial community, the relative abundances of Fusobacteria were significantly decreased, while Bacteroidetes was no significant difference in all CuSO₄ exposure groups. Furthermore, at the genera level, Flavobacterium has a significant higher abundance in 0.75 μM CuSO₄ exposure group, and high abundance of Rahnella was found in 0.1 μM CuSO₄ exposure group. Also, Cu exposure affected the metabolism function of R. chensinensis tadpoles based on functional prediction analysis. This work provides new perspective to explore the effect of heavy metal on the intestinal health of amphibians.

Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks

BACKGROUND

To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0-20 for QIIME1 and 0-30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples.

RESULTS

Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region.

CONCLUSIONS

Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary field that addresses heterogeneous effects of exogenous and endogenous factors (collectively termed 'exposures'), including microorganisms, on disease occurrence and consequences, utilising molecular pathological signatures of the disease. In parallel with the paradigm of precision medicine, findings from MPE research can provide aetiological insights into tailored strategies of disease prevention and treatment. Due to the availability of molecular pathological tests on tumours, the MPE approach has been utilised predominantly in research on cancers including breast, lung, prostate, and colorectal carcinomas. Mounting evidence indicates that the microbiome (inclusive of viruses, bacteria, fungi, and parasites) plays an important role in a variety of human diseases including neoplasms. An alteration of the microbiome may be not only a cause of neoplasia but also an informative biomarker that indicates or mediates the association of an epidemiological exposure with health conditions and outcomes. To adequately educate and train investigators in this emerging area, we herein propose the integration of microbiology into the MPE model (termed 'microbiology-MPE'), which could improve our understanding of the complex interactions of environment, tumour cells, the immune system, and microbes in the tumour microenvironment during the carcinogenic process. Using this approach, we can examine how lifestyle factors, dietary patterns, medications, environmental exposures, and germline genetics influence cancer development and progression through impacting the microbial communities in the human body. Further integration of other disciplines (e.g. pharmacology, immunology, nutrition) into microbiology-MPE would expand this developing research frontier. With the advent of high-throughput next-generation sequencing technologies, researchers now have increasing access to large-scale metagenomics as well as other omics data (e.g. genomics, epigenomics, proteomics, and metabolomics) in population-based research. The integrative field of microbiology-MPE will open new opportunities for personalised medicine and public health. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.