Temporal and technical variability of human gut metagenomes

Optimised human stool sample collection for multi-omic microbiota analysis

To accurately define the role of the gut microbiota in health and disease pathogenesis, the preservation of stool sample integrity, in terms of microbial community composition and metabolic function, is critical. This presents a challenge for any studies which rely on participants self-collecting and returning stool samples as this introduces variability and uncertainty of sample storage/handling. Here, we tested the performance of three stool sample collection/preservation buffers when storing human stool samples at different temperatures (room temperature [20 °C], 4 °C and - 80 °C) for up to three days. We compared and quantified differences in 16S rRNA sequencing composition and short-chain fatty acid profiles compared against immediately snap-frozen stool. We found that the choice of preservation buffer had the largest effect on the resulting microbial community and metabolomic profiles. Collectively analysis confirmed that PSP and RNAlater buffered samples most closely recapitulated the microbial diversity profile of the original (immediately - 80 °C frozen) sample and should be prioritised for human stool microbiome studies.

Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries

Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.

Preservation of conjugated primary bile acids by oxygenation of the small intestinal microbiota in vitro

Bile acids play a critical role in the emulsification of dietary lipids, a critical step in the primary function of the small intestine, which is the digestion and absorption of food. Primary bile acids delivered into the small intestine are conjugated to enhance functionality, in part, by increasing aqueous solubility and preventing passive diffusion of bile acids out of the gut lumen. Bile acid function can be disrupted by the gut microbiota via the deconjugation of primary bile acids by bile salt hydrolases (BSHs), leading to their conversion into secondary bile acids through the expression of bacterial bile acid-inducible genes, a process often observed in malabsorption due to small intestinal bacterial overgrowth. By modeling the small intestinal microbiota in vitro using human small intestinal ileostomy effluent as the inocula, we show here that the infusion of physiologically relevant levels of oxygen, normally found in the proximal small intestine, reduced deconjugation of primary bile acids, in part, through the expansion of bacterial taxa known to have a low abundance of BSHs. Further recapitulating the small intestinal bile acid composition of the small intestine, limited conversion of primary into secondary bile acids was observed. Remarkably, these effects were preserved among four separate communities, each inoculated with a different small intestinal microbiota, despite a high degree of taxonomic variability under both anoxic and aerobic conditions. In total, these results provide evidence for a previously unrecognized role that the oxygenated environment of the small intestine plays in the maintenance of normal digestive physiology.

IMPORTANCE

Conjugated primary bile acids are produced by the liver and exist at high concentrations in the proximal small intestine, where they are critical for proper digestion. Deconjugation of these bile acids with subsequent transformation via dehydroxylation into secondary bile acids is regulated by the colonic gut microbiota and reduces their digestive function. Using an in vitro platform modeling the small intestinal microbiota, we analyzed the ability of this community to transform primary bile acids and studied the effect of physiological levels of oxygen normally found in the proximal small intestine (5%) on this metabolic process. We found that oxygenation of the small intestinal microbiota inhibited the deconjugation of primary bile acids in vitro. These findings suggest that luminal oxygen levels normally found in the small intestine may maintain the optimal role of bile acids in the digestive process by regulating bile acid conversion by the gut microbiota.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Impact of evolution on lifestyle in microbiome

This chapter analyses the interaction between microbiota and humans from an evolutionary point of view. Long-term interactions between gut microbiota and host have been generated as a result of dietary choices through coevolutionary processes, where mutuality of advantage is essential. Likewise, the characteristics of the intestinal environment have made it possible to describe different intrahost evolutionary mechanisms affecting microbiota. For its part, the intestinal microbiota has been of great importance in the evolution of mammals, allowing the diversification of dietary niches, phenotypic plasticity and the selection of host phenotypes. Although the origin of the human intestinal microbial community is still not known with certainty, mother-offspring transmission plays a key role, and it seems that transmissibility between individuals in adulthood also has important implications. Finally, it should be noted that certain aspects inherent to modern lifestyle, including refined diets, antibiotic intake, exposure to air pollutants, microplastics, and stress, could negatively affect the diversity and composition of our gut microbiota. This chapter aims to combine current knowledge to provide a comprehensive view of the interaction between microbiota and humans throughout evolution.

Different gut microbial types were found in captive striped hamsters

Background

Typing analysis has become a popular approach to categorize individual differences in studies of animal gut microbial communities. However, previous definitions of gut microbial types were more understood as a passive reaction process to different external interferences, as most studies involve diverse environmental variables. We wondered whether distinct gut microbial types can also occur in animals under the same external environment. Moreover, the role of host sex in shaping gut microbiota has been widely reported; thus, the current study preliminarily explores the effects of sex on potential different microbial types.

Methods

Here, adult striped hamsters Cricetulus barabensis of different sexes were housed under the same controlled laboratory conditions, and their fecal samples were collected after two months to assess the gut microbiota by 16S rRNA sequencing.

Results

The gut microbiota of captive striped hamsters naturally separated into two types at the amplicon sequence variant (ASV) level. There was a significant difference in the Shannon index among these two types. A receiver operating characteristic (ROC) curve showed that the top 30 ASVs could effectively distinguish each type. Linear discriminant analysis of effect size (LEfSe) showed enrichment of the genera Lactobacillus, Treponema and Pygmaiobacter in one gut microbial type and enrichment of the genera Turicibacter and Ruminiclostridium in the other. The former type had higher carbohydrate metabolism ability, while the latter harbored a more complex co-occurrence network and higher amino acid metabolism ability. The gut microbial types were not associated with sex; however, we did find sex differences in the relative abundances of certain bacterial taxa, including some type-specific sex variations.

Conclusions

Although captive animals live in a unified environment, their gut bacteria can still differentiate into distinct types, but the sex of the hosts may not play an important role in the typing process of small-scale captive animal communities. The relevant driving factors as well as other potential types need to be further investigated to better understand host-microbe interactions.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Effects of Stool Sample Preservation Methods on Gut Microbiota Biodiversity: New Original Data and Systematic Review with Meta-Analysis

Here, we aimed to compare the effects of different preservation methods on outcomes of fecal microbiota. We evaluated the effects of different preservation methods using stool sample preservation experiments for up to 1 year. The stool samples from feces of healthy volunteers were grouped based on whether absolute ethanol was added and whether they were hypothermically preserved. Besides, we performed a systematic review to combine current fecal microbiota preservation evidence. We found that Proteobacteria changed significantly and Veillonellaceae decreased significantly in the 12th month in the room temperature + absolute ethanol group. The four cryopreservation groups have more similarities with fresh sample in the 12 months; however, different cryopreservation methods have different effects on several phyla, families, and genera. A systematic review showed that the Shannon diversity and Simpson index of samples stored in RNAlater for 1 month were not statistically significant compared with those stored immediately at -80°C (P = 0.220 and P = 0.123, respectively). The -80°C refrigerator and liquid nitrogen cryopreservation with 10% glycerine can both maintain stable microbiota of stool samples for long-term preservation. The addition of absolute ethanol to cryopreserved samples had no significant difference in the effect of preserving fecal microbial characteristics. Our study provides empirical insights into preservation details for future studies of the long-term preservation of fecal microbiota. Systematic review and meta-analysis found that the gut microbiota structure, composition, and diversity of samples preserved by storage methods, such as preservation solution, are relatively stable, which were suitable for short-term storage at room temperature. IMPORTANCE The study of gut bacteria has become increasingly popular, and fecal sample preservation methods and times need to be standardized. Here, we detail a 12-month study of fecal sample preservation, and our study provides an empirical reference about experimental details for long-term high-quality storage of fecal samples in the field of gut microbiology research. The results showed that the combination of -80°C/liquid nitrogen deep cryopreservation and 10% glycerol was the most effective method for the preservation of stool samples, which is suitable for long-term storage for at least 12 months. The addition of anhydrous ethanol to the deep cryopreserved samples did not make a significant difference in the preservation of fecal microbiological characteristics. Combined with the results of systematic reviews and meta-analyses, we believe that, when researchers preserve fecal specimens, it is essential to select the proper preservation method and time period in accordance with the goal of the study.

Shallow shotgun sequencing reduces technical variation in microbiome analysis

The microbiome is known to play a role in many human diseases, but identifying key microbes and their functions generally requires large studies due to the vast number of species and genes, and the high levels of intra-individual and inter-individual variation. 16S amplicon sequencing of the rRNA gene is commonly used for large studies due to its comparatively low sequencing cost, but it has poor taxonomic and functional resolution. Deep shotgun sequencing is a more accurate and comprehensive alternative for small studies, but can be cost-prohibitive for biomarker discovery in large populations. Shallow or moderate-depth shotgun metagenomics may serve as a viable alternative to 16S sequencing for large-scale and/or dense longitudinal studies, but only if resolution and reproducibility are comparable. Here we applied both 16S and shallow shotgun stool microbiome sequencing to a cohort of 5 subjects sampled twice daily and weekly, with technical replication at the DNA extraction and the library preparation/sequencing steps, for a total of 80 16S samples and 80 shallow shotgun sequencing samples. We found that shallow shotgun sequencing produced lower technical variation and higher taxonomic resolution than 16S sequencing, at a much lower cost than deep shotgun sequencing. These findings suggest that shallow shotgun sequencing provides a more specific and more reproducible alternative to 16S sequencing for large-scale microbiome studies where costs prohibit deep shotgun sequencing and where bacterial species are expected to have good coverage in whole-genome reference databases.

Colonic Microbial Abundances Predict Adenoma Formers

OBJECTIVE

We aimed to examine associations between the oral, fecal, and mucosal microbiome communities and adenoma formation.

SUMMARY BACKGROUND DATA

Data are limited regarding the relationships between microbiota and preneoplastic colorectal lesions.

METHODS

Individuals undergoing screening colonoscopy were prospectively enrolled and divided into adenoma and nonadenoma formers. Oral, fecal, nonadenoma and adenoma-adjacent mucosa were collected along with clinical and dietary information. 16S rRNA gene libraries were generated using V4 primers. DADA2 processed sequence reads and custom R-scripts quantified microbial diversity. Linear regression identified differential taxonomy and diversity in microbial communities and machine learning identified adenoma former microbial signatures.

RESULTS

One hundred four subjects were included, 46% with adenomas. Mucosal and fecal samples were dominated by Firmicutes and Bacteroidetes whereas Firmicutes and Proteobacteria were most abundant in oral communities. Mucosal communities harbored significant microbial diversity that was not observed in fecal or oral communities. Random forest classifiers predicted adenoma formation using fecal, oral, and mucosal amplicon sequence variant (ASV) abundances. The mucosal classifier reliably diagnosed adenoma formation with an area under the curve (AUC) = 0.993 and an out-of-bag (OOB) error of 3.2%. Mucosal classifier accuracy was strongly influenced by five taxa associated with the family Lachnospiraceae, genera Bacteroides and Marvinbryantia, and Blautia obeum. In contrast, classifiers built using fecal and oral samples manifested high OOB error rates (47.3% and 51.1%, respectively) and poor diagnostic abilities (fecal and oral AUC = 0.53).

CONCLUSION

Normal mucosa microbial abundances of adenoma formers manifest unique patterns of microbial diversity that may be predictive of adenoma formation.

HNRNPA2B1-Mediated MicroRNA-92a Upregulation and Section Acts as a Promising Noninvasive Diagnostic Biomarker in Colorectal Cancer

MicroRNA-92a (miR-92a) may serve as a novel promising biomarker in multiple cancers, including colorectal cancer (CRC); however, the diagnostic accuracy and the underlying molecular mechanism of miR-92a in CRC is poorly understood. We first carried out meta-analysis and found that serum/plasma miR-92a yield better diagnostic efficacy when compared to stool samples and CRC tissues, and this finding was validated by our independent study through stool sample. Multiple bioinformatics assay indicated that miR-92a expression was positively correlated with heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1) expression and closely related with the clinical characteristics of CRC. Experimental evidence showed that knockdown of HNRNPA2B1 could significantly decrease miR-92a expression and secretion in RKO cells. HNRNPA2B1 mediated miR-92a via m6A RNA modification. These findings indicate that HNRNPA2B1-m6A RNA modification-derived MicroRNA-92a upregulation and section from the local CRC acts a candidate noninvasive serum biomarker in colorectal cancer. Our study provides a novel insight into miR-92a mechanisms in relation to both expression and secretion for CRC diagnosis.

The human microbial exposome: expanding the Exposome-Explorer database with gut microbial metabolites

Metabolites produced by the gut microbiota play an important role in the cross-talk with the human host. Many microbial metabolites are biologically active and can pass the gut barrier and make it into the systemic circulation, where they form the gut microbial exposome, i.e. the totality of gut microbial metabolites in body fluids or tissues of the host. A major difficulty faced when studying the microbial exposome and its role in health and diseases is to differentiate metabolites solely or partially derived from microbial metabolism from those produced by the host or coming from the diet. Our objective was to collect data from the scientific literature and build a database on gut microbial metabolites and on evidence of their microbial origin. Three types of evidence on the microbial origin of the gut microbial exposome were defined: (1) metabolites are produced in vitro by human faecal bacteria; (2) metabolites show reduced concentrations in humans or experimental animals upon treatment with antibiotics; (3) metabolites show reduced concentrations in germ-free animals when compared with conventional animals. Data was manually collected from peer-reviewed publications and inserted in the Exposome-Explorer database. Furthermore, to explore the chemical space of the microbial exposome and predict metabolites uniquely formed by the microbiota, genome-scale metabolic models (GSMMs) of gut bacterial strains and humans were compared. A total of 1848 records on one or more types of evidence on the gut microbial origin of 457 metabolites was collected in Exposome-Explorer. Data on their known precursors and concentrations in human blood, urine and faeces was also collected. About 66% of the predicted gut microbial metabolites (n = 1543) were found to be unique microbial metabolites not found in the human GSMM, neither in the list of 457 metabolites curated in Exposome-Explorer, and can be targets for new experimental studies. This new data on the gut microbial exposome, freely available in Exposome-Explorer ( http://exposome-explorer.iarc.fr/ ), will help researchers to identify poorly studied microbial metabolites to be considered in future studies on the gut microbiota, and study their functionalities and role in health and diseases.

Long-term taxonomic and functional stability of the gut microbiome from human fecal samples

Appropriate storage of fecal samples is a critical step for unbiased analysis in human microbiome studies. The purpose of this study was to evaluate the stability of the fecal microbial community for up to 18 months. Ten healthy volunteers provided fecal samples at the Jeonbuk National University Hospital. Stool samples were stored under the following six conditions: four different storage temperatures (- 70 °C, - 20 °C, 4 °C, and room temperature [20-25 °C]) and two different collection tubes (OMNIgene-Gut and DNA/RNA shield-fecal collection tubes). The gut microbiome was analyzed with 16S rRNA sequencing. We compared the taxonomic composition, alpha diversity, beta diversity and inferred pathway abundance between the baseline and 18 months after storage. Samples collected in the DNA/RNA Shield-fecal collection tubes showed the best performance in preservation of the taxonomic composition at 18 months. Pairwise differences in alpha diversity metrics showed the least deviation from zero. The PERMANOVA test showed non-significant change of beta diversity metrics (Unweighted Unifrac: q-value 0.268; Weighted Unifrac: q-value 0.848). The functional stability was significantly well preserved in the DNA/RNA Shield-fecal collection tubes (adjusted p value < 0.05). Our results demonstrate the use of the DNA/RNA Shield-fecal collection tube as an alternative storage method for fecal samples to preserve the taxonomic and functional stability of the microbiome over a long term.

The gut-microbiota-brain axis in a Spanish population in the aftermath of the COVID-19 pandemic: microbiota composition linked to anxiety, trauma, and depression profiles

The prevalence of anxiety and depression soared following the COVID-19 pandemic. To effectively treat these conditions, a comprehensive understanding of all etiological factors is needed. This study investigated fecal microbial features associated with mental health outcomes (symptoms of anxiety, depression, or posttraumatic stress disorder (PTSD)) in a Spanish cohort in the aftermath of the COVID-19 pandemic. Microbial communities from stool samples were profiled in 198 individuals who completed validated, self-report questionnaires. 16S ribosomal RNA gene V3-4 amplicon sequencing was performed. Microbial diversity and community structure were analyzed, together with relative taxonomic abundance. In our cohort of N=198, 17.17% reported depressive symptoms, 37.37% state anxiety symptoms, 40.90% trait anxiety symptoms, and 8.08% PTSD symptoms, with high levels of comorbidity. Individuals with trait anxiety had lower Simpson's diversity. Fusicatenibacter saccharivorans was reduced in individuals with comorbid PTSD + depression + state and trait anxiety symptoms, whilst an expansion of Proteobacteria and depletion of Synergistetes phyla were noted in individuals with depressive symptoms. The relative abundance of Anaerostipes was positively correlated with childhood trauma, and higher levels of Turicibacter sanguinis and lower levels of Lentisphaerae were found in individuals who experienced life-threatening traumas. COVID-19 infection and vaccination influenced the overall microbial composition and were associated with distinct relative taxonomic abundance profiles. These findings will help lay the foundation for future studies to identify microbial role players in symptoms of anxiety, depression, and PTSD and provide future therapeutic targets to improve mental health outcomes.

Feсal microbiota transplantation in the format of complex therapy in obesive siblings: clinical case

Obesity and associated metabolic diseases are often accompanied by changes in the gut microbiota leading to metagenome gene diversity decrease. Fecal microbiota transplantation (FMT) is one of the most effective methods for correcting the  intestinal microflora. FMT obtained from healthy donors has been proven to be an effective treatment of infections caused by Clostridium difficile. The use of FMT for correction of metabolic disorders is promising, however, data on its application is limited and has contradictory results. In our work, two patients (siblings) presented with obesity grade II and various types of diabetes mellitus (DM): the older brother (44 years old) with diabetes mellitus type 2 (DM 2), a younger brother (39 years old) with diabetes mellitus type 1 (DM 1). Both patients underwent FMT as part of complex antidiabetic therapy. During the course of treatment, a decrease in body weight was noted in both patients (4–5 kg for the first month of observation, then -1–2 kg per month). One year after FMT, a patient with type 2 diabetes showed a decrease in the severity of insulin resistance (IR), measured by the hyperinsulinemic euglycemic clamp test (initial M-index 2.42 mg/kg*min, after 1 year — 3.83 mg/kg* min) as well as the maintenance of satisfactory carbohydrate metabolism compensation against the diminishing the hypoglycemic therapy. In a patient with DM 1, no significant dynamics of carbohydrate exchange indices, including detected glycated hemoglobin (HbA1c), insulin dose and IR were during the observation period. Metagenomic sequencing of stool samples (n = 20) collected from both patients before and within 1 year after FMT showed no significant changes in the taxonomic profile of the microbiota at the level of microbial families. Metabolomic analysis of the composition of feces showed no directed changes in the composition of metabolites after the FMT procedure, the nature of changes within the samples from each patient during the entire study period was random. Thus, FMT had no effect on the course of DM1, but served as a starting point for weight loss and improvement glucose profile in DM2. However, convincing data confirming a causal correlation between FMT and improvement in the course of T2DM have not been obtained.

Early fecal microbiome transfer after donor defecation determines response in patients with moderate to severe ulcerative colitis

BACKGROUND

Fecal microbiome transfer (FMT) targeting gut microbiome dysbiosis is an emerging therapy for ulcerative colitis (UC). There is however no consensus on protocols for performing FMT in UC, especially in relation to time after donor feces defecation.

METHODS

This is a single-center retrospective analysis of patients with moderate-severe UC (total Mayo Clinic score ≥6 and endoscopic Mayo Clinic subscore of ≥2) treated with FMT between September 2017 and December 2019 at Dayanand Medical College and Hospital, Ludhiana, India. Fresh fecal samples from unrelated healthy voluntary donors were administered through colonoscopy at weeks 0, 2, 6, 10, 14, 18, and 22. Time interval between donor feces defecation and FMT procedure was recorded for each FMT session and the mean time of seven sessions was designated aika. Impact of aika on clinical response and safety of FMT was evaluated.

RESULTS

During the study period, 123 adult patients (mean age 33.75±11.97 years, 61.8% [n=76] males) with moderate-severe UC (mean total Mayo Clinic and endoscopic Mayo Clinic scores 7.49±1.60 and 2.50±0.50, respectively) were treated with FMT. The mean aika was 2.29±0.75 h. The aika was smaller in patients who responded to FMT as compared to non-responders (2.13±0.75 h vs. 2.71±0.76 h, p=0.0002) as well as in patients achieving clinical remission (2.15±0.76 h vs. 2.42±0.76 h, p=0.05). There was no significant impact of aika on adverse effects except for the incidence of borborygmi after FMT, which was higher in patients with aika ≤2 h.

CONCLUSION

Early FMT after donor feces defecation favorably impacts the clinical response rates in patients with active UC.

Variability in the Pre-Analytical Stages Influences Microbiome Laboratory Analyses

Introduction: There are numerous confounding variables in the pre-analytical steps in the analysis of gut microbial composition that affect data consistency and reproducibility. This study compared two DNA extraction methods from the same faecal samples to analyse differences in microbial composition. Methods: DNA was extracted from 20 faecal samples using either (A) chemical/enzymatic heat lysis (lysis buffer, proteinase K, 95 °C + 70 °C) or (B) mechanical and chemical/enzymatic heat lysis (bead-beating, lysis buffer, proteinase K, 65 °C). Gut microbiota was mapped through the 16S rRNA gene (V3−V9) using a set of pre-selected DNA probes targeting >300 bacteria on different taxonomic levels. Apart from the pre-analytical DNA extraction technique, all other parameters including microbial analysis remained the same. Bacterial abundance and deviations in the microbiome were compared between the two methods. Results: Significant variation in bacterial abundance was seen between the different DNA extraction techniques, with a higher yield of species noted in the combined mechanical and heat lysis technique (B). The five predominant bacteria seen in both (A) and (B) were Bacteroidota spp. and Prevotella spp. (p = NS), followed by Bacillota (p = 0.005), Lachhnospiraceae (p = 0.0001), Veillonella spp. (p < 0.0001) and Clostridioides (p < 0.0001). Conclusion: As microbial testing becomes more easily and commercially accessible, a unified international consensus for optimal sampling and DNA isolation procedures must be implemented for robustness and reproducibility of the results.

Deciphering mechanisms and implications of bacterial translocation in human health and disease

Significant increases in potential microbial translocation, especially along the oral-gut axis, have been identified in many immune-related and inflammatory diseases, such as inflammatory bowel disease, colorectal cancer, rheumatoid arthritis, and liver cirrhosis, for which we currently have no cure or long-term treatment options. Recent advances in computational and experimental omics approaches now enable strain tracking, functional profiling, and strain isolation in unprecedented detail, which has the potential to elucidate the causes and consequences of microbial translocation. In this review, we discuss current evidence for the detection of bacterial translocation, examine different translocation axes with a primary focus on the oral-gut axis, and outline currently known translocation mechanisms and how they adversely affect the host in disease. Finally, we conclude with an overview of state-of-the-art computational and experimental tools for strain tracking and highlight the required next steps to elucidate the role of bacterial translocation in human health.

Effect of storage temperature and flash-freezing on salivary microbiota profiles based on 16S rRNA-targeted sequencing

The sample storage environment affects gut microbial profiles as assessed using 16S rRNA sequencing. However, the influence of storage condition on human salivary microbial profiles has not been well characterized. Here, we performed an observational study to assess the robustness of microbiota profiles in three different storage environments (-80°C after flash-freezing, -80°C, and -15°C; all for 14 days) compared to immediate DNA extraction using the MiSeq Illumina platform. Notably, the 16S rRNA V1-V2 region amplicon sequencing revealed no difference in microbiota profiles between the immediate extraction and each of three storage conditions. An almost perfect correlation was shown between the immediate extraction and the -15°C storage group for relative abundance at the genus and operational taxonomic unit levels. The intraindividual UniFrac distances among storage methods were significantly shorter than those of interindividual distances. None of the amount of extracted DNA, the α-diversity indices, or the relative abundance at the phylum/genus/operational taxonomic unit level differed among storage methods. These findings indicate that a storage temperature of -15°C without flash-freezing may be optimal in terms of cost advantage and simplicity in 16S rRNA sequencing-based salivary microbial research.

Development and evaluation of a colorectal cancer screening method using machine learning-based gut microbiota analysis

Accumulating evidence indicates that alterations of gut microbiota are associated with colorectal cancer (CRC). Therefore, the use of gut microbiota for the diagnosis of CRC has received attention. Recently, several studies have been conducted to detect the differences in the gut microbiota between healthy individuals and CRC patients using machine learning‐based gut bacterial DNA meta‐sequencing analysis, and to use this information for the development of CRC diagnostic model. However, to date, most studies had small sample sizes and/or only cross‐validated using the training dataset that was used to create the diagnostic model, rather than validated using an independent test dataset. Since machine learning‐based diagnostic models cause overfitting if the sample size is small and/or an independent test dataset is not used for validation, the reliability of these diagnostic models needs to be interpreted with caution. To circumvent these problems, here we have established a new machine learning‐based CRC diagnostic model using the gut microbiota as an indicator. Validation using independent test datasets showed that the true positive rate of our CRC diagnostic model increased substantially as CRC progressed from Stage I to more than 60% for CRC patients more advanced than Stage II when the false positive rate was set around 8%. Moreover, there was no statistically significant difference in the true positive rate between samples collected in different cities or in any part of the colorectum. These results reveal the possibility of the practical application of gut microbiota‐based CRC screening tests.

Surgical Treatment for Colorectal Cancer Partially Restores Gut Microbiome and Metabolome Traits

The gut microbiome and metabolites are associated with CRC progression and carcinogenesis. Postoperative CRC patients are reported to be at an increased CRC risk; however, how gut microbiome and metabolites are related to CRC risk in postoperative patients remains only partially understood.

Potential influence of the microbiome environment in patients with biliary tract cancer and implications for therapy

Biliary tract cancers, including intra- and extra-hepatic cholangiocarcinoma as well as gallbladder cancer, are associated with poor prognosis and the majority of patients present with advanced-stage, non-resectable disease at diagnosis. Biliary tract cancer may develop through an accumulation of genetic and epigenetic alterations and can be influenced by microbial exposure. Furthermore, the liver and biliary tract are exposed to the gastrointestinal microbiome through the gut-liver axis. The availability of next-generation sequencing technology has led to an increase in studies investigating the relationship between microbiota and human disease. In particular, the interplay between the microbiome, the tumour micro-environment and response to systemic therapy is a prospering area of interest. Given the poor outcomes for patients with biliary tract cancer, this emerging field of research, through which new biomarkers may be identified, offers potential as a tool for early diagnosis, prognostication or even as a future therapeutic target. This review summarises the available evidence on the microbiome environment in patients with biliary tract cancer, including a discussion around confounding factors, implications for therapy and proposed future directions.

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

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

Human Stool Preservation Impacts Taxonomic Profiles in 16S Metagenomics Studies

Microbiotas play critical roles in human health, yet in most cases scientists lack standardized and reproducible methods from collection and preservation of samples, as well as the choice of omic analysis, up to the data processing. To date, stool sample preservation remains a source of technological bias in metagenomic sequencing, despite newly developed storage solutions. Here, we conducted a comparative study of 10 storage methods for human stool over a 14-day period of storage at fluctuating temperatures. We first compared the performance of each stabilizer with observed bacterial composition variation within the same specimen. Then, we identified the nature of the observed variations to determine which bacterial populations were more impacted by the stabilizer. We found that DNA stabilizers display various stabilizing efficacies and affect the recovered bacterial profiles thus highlighting that some solutions are more performant in preserving the true gut microbial community. Furthermore, our results showed that the bias associated with the stabilizers can be linked to the phenotypical traits of the bacterial populations present in the studied samples. Although newly developed storage solutions have improved our capacity to stabilize stool microbial content over time, they are nevertheless not devoid of biases hence requiring the implantation of standard operating procedures. Acknowledging the biases and limitations of the implemented method is key to better interpret and support true associated microbiome patterns that will then lead us towards personalized medicine, in which the microbiota profile could constitute a reliable tool for clinical practice.

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

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

Evaluation of early antibiotic use in patients with non-severe COVID-19 without bacterial infection

OBJECTIVES

The use of antibiotics was common in some countries during the early phase of the coronavirus disease 2019 (COVID-19) pandemic, but adequate evaluation remains lacking. This study aimed to evaluate the effect of early antibiotic use in patients with non-severe COVID-19 admitted without bacterial infection.

METHODS

This multi-centre retrospective cohort study included 1,373 inpatients with non-severe COVID-19 admitted without bacterial infection. Patients were divided into two groups according to their exposure to antibiotics within 48 h of admission. The outcomes were progression to severe COVID-19, length of stay >15 days and mortality rate. A mixed-effect Cox model and random effect logistic regression were used to explore the association between early antibiotic use and outcomes.

RESULTS

During the 30-day follow-up period, the proportion of patients who progressed to severe COVID-19 in the early antibiotic use group was almost 1.4 times that of the comparison group. In the mixed-effect model, the early use of antibiotics was associated with higher probability of developing severe COVID-19 and staying in hospital for >15 days. However, there was no significant association between early use of antibiotics and mortality. Analysis with propensity-score-matched cohorts displayed similar results. In subgroup analysis, patients receiving any class of antibiotic were at increased risk of adverse health outcomes. Azithromycin did not improve disease progression and length of stay in patients with COVID-19.

CONCLUSIONS

It is suggested that antibiotic use should be avoided unless absolutely necessary in patients with non-severe COVID-19, particularly in the early stages.

KOMB: K-core based de novo characterization of copy number variation in microbiomes

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Comparison of fecal and oral collection methods for studies of the human microbiota in two Iranian cohorts

Background

To initiate fecal and oral collections in prospective cohort studies for microbial analyses, it is essential to understand how field conditions and geographic differences may impact microbial communities. This study aimed to investigate the impact of fecal and oral sample collection methods and room temperature storage on collection samples for studies of the human microbiota.

Results

We collected fecal and oral samples from participants in two Iranian cohorts located in rural Yazd (n = 46) and urban Gonbad (n = 38) and investigated room temperature stability over 4 days of fecal (RNAlater and fecal occult blood test [FOBT] cards) and comparability of fecal and oral (OMNIgene ORAL kits and Scope mouthwash) collection methods. We calculated interclass correlation coefficients (ICCs) based on 3 alpha and 4 beta diversity metrics and the relative abundance of 3 phyla. After 4 days at room temperature, fecal stability ICCs and ICCs for Scope mouthwash were generally high for all microbial metrics. Similarly, the fecal comparability ICCs for RNAlater and FOBT cards were high, ranging from 0.63 (95% CI: 0.46, 0.75) for the relative abundance of Firmicutes to 0.93 (95% CI: 0.89, 0.96) for unweighted Unifrac. Comparability ICCs for OMNIgene ORAL and Scope mouthwash were lower than fecal ICCs, ranging from 0.55 (95% CI: 0.36, 0.70) for the Shannon index to 0.79 (95% CI: 0.69, 0.86) for Bray-Curtis. Overall, RNAlater, FOBT cards and Scope mouthwash were stable up to 4 days at room temperature. Samples collected using FOBT cards were generally comparable to RNAlater while the OMNIgene ORAL were less similar to Scope mouthwash.

Conclusions

As microbiome measures for feces samples collected using RNAlater, FOBT cards and oral samples collected using Scope mouthwash were stable over four days at room temperature, these would be most appropriate for microbial analyses in these populations. However, one collection method should be consistently since each method may induce some differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02387-9.

Association Between the Sulfur Microbial Diet and Risk of Colorectal Cancer

IMPORTANCE

Sulfur-metabolizing bacteria that reduce dietary sulfur to hydrogen sulfide have been associated with colorectal cancer (CRC). However, there are limited studies investigating the association between diet and sulfur-metabolizing bacteria in the development of CRC.

OBJECTIVE

To develop a dietary score that correlates with gut sulfur-metabolizing bacteria and to examine its association with CRC risk.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study included data from the Health Professionals Follow-up Study (1986-2014), Nurses' Health Study (1984-2016), and Nurses' Health Study II (1991-2017). Participants were US male health professionals and female registered nurses who were free of inflammatory bowel disease and cancer at baseline, with a subsample of participants who provided stool samples from 2012 to 2014. Statistical analysis was conducted from September 1, 2020, to June 1, 2021.

EXPOSURE

A dietary pattern, assessed by a food-frequency questionnaire, that most correlated with 43 sulfur-metabolizing bacteria identified through taxonomic and functional profiling of gut metagenome data.

MAIN OUTCOMES AND MEASURES

Incident CRC.

RESULTS

Among 214 797 participants comprising 46 550 men (mean [SD] age at baseline, 54.3 [9.7] years) and 168 247 women (mean [SD] age at baseline, 43.0 [9.2] years), 3217 incident cases of CRC (1.5%) were documented during 5 278 048 person-years of follow-up. The sulfur microbial diet, developed in a subsample of 307 men (mean [SD] age, 70.5 [4.3] years) and 212 women (mean [SD] age, 61.0 [3.8] years), was characterized by high intakes of low-calorie beverages, french fries, red meats, and processed meats and low intakes of fruits, yellow vegetables, whole grains, legumes, leafy vegetables, and cruciferous vegetables. After adjustment for other risk factors, greater adherence to the sulfur microbial diet was associated with an increased risk of CRC, with a hazard ratio (HR) of 1.27 (95% CI, 1.12-1.44) comparing the highest vs the lowest quintile of the diet score (linear trend of diet score quintiles; P < .001 for trend). When assessed by anatomical subsites, greater adherence to the sulfur microbial diet was positively associated with distal CRC (HR, 1.25; 95% CI, 1.05-1.50; P = .02 for trend) but not proximal colon cancer (HR, 1.13; 95% CI, 0.93-1.39; P = .19 for trend).

CONCLUSIONS AND RELEVANCE

Adherence to the sulfur microbial diet was associated with an increased risk of CRC, suggesting a potential mediating role of sulfur-metabolizing bacteria in the associaton between diet and CRC. Further research is needed to confirm these findings and to determine the underlying mechanisms.

Gut bacteria identified in colorectal cancer patients promote tumourigenesis via butyrate secretion

Emerging evidence is revealing that alterations in gut microbiota are associated with colorectal cancer (CRC). However, very little is currently known about whether and how gut microbiota alterations are causally associated with CRC development. Here we show that 12 faecal bacterial taxa are enriched in CRC patients in two independent cohort studies. Among them, 2 Porphyromonas species are capable of inducing cellular senescence, an oncogenic stress response, through the secretion of the bacterial metabolite, butyrate. Notably, the invasion of these bacteria is observed in the CRC tissues, coinciding with the elevation of butyrate levels and signs of senescence-associated inflammatory phenotypes. Moreover, although the administration of these bacteria into Apc^Δ14/+ mice accelerate the onset of colorectal tumours, this is not the case when bacterial butyrate-synthesis genes are disrupted. These results suggest a causal relationship between Porphyromonas species overgrowth and colorectal tumourigenesis which may be due to butyrate-induced senescence.

Several bacteria in the gut microbiota have been associated with colorectal cancer (CRC) but it is not completely clear whether they have a role in tumourigenesis. Here, the authors show enrichment of 12 bacterial taxa in two cohorts of CRC patients and that two Porphyromonas species accelerate CRC onset through butyrate secretion.

Exploring Semi-Quantitative Metagenomic Studies Using Oxford Nanopore Sequencing: A Computational and Experimental Protocol

The gut microbiome plays a major role in chronic diseases, of which several are characterized by an altered composition and diversity of bacterial communities. Large-scale sequencing projects allowed for characterizing the perturbations of these communities. However, translating these discoveries into clinical applications remains a challenge. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Here, we propose a computational and experimental protocol for whole-genome semi-quantitative metagenomic studies of human gut microbiome with Oxford Nanopore sequencing technology (ONT) that could be applied to other microbial ecosystems. We developed a bioinformatics protocol to analyze ONT sequences taxonomically and functionally and optimized preanalytic protocols, including stool collection and DNA extraction methods to maximize read length. This is a critical parameter for the sequence alignment and classification. Our protocol was evaluated using simulations of metagenomic communities, which reflect naturally occurring compositional variations. Next, we validated both protocols using stool samples from a bariatric surgery cohort, sequenced with ONT, Illumina, and SOLiD technologies. Results revealed similar diversity and microbial composition profiles. This protocol can be implemented in a clinical or research setting, bringing rapid personalized whole-genome profiling of target microbiome species.

Mild and Severe SARS-CoV-2 Infection Induces Respiratory and Intestinal Microbiome Changes in the K18-hACE2 Transgenic Mouse Model

Transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of deaths and declining economies around the world. K18-hACE2 mice develop disease resembling severe SARS-CoV-2 infection in a virus dose-dependent manner. The relationship between SARS-CoV-2 and the intestinal or respiratory microbiome is not fully understood. In this context, we characterized the cecal and lung microbiomes of SARS-CoV-2-challenged K18-hACE2 transgenic mice in the presence or absence of treatment with the Mpro inhibitor GC-376. Cecum microbiome showed decreased Shannon and inverse (Inv) Simpson diversity indexes correlating with SARS-CoV-2 infection dosage and a difference of Bray-Curtis dissimilarity distances among control and infected mice. Bacterial phyla such as Firmicutes, particularly, Lachnospiraceae and Oscillospiraceae, were significantly less abundant, while Verrucomicrobia, particularly, the family Akkermansiaceae, were increasingly more prevalent during peak infection in mice challenged with a high virus dose. In contrast to the cecal microbiome, the lung microbiome showed similar microbial diversity among the control, low-, and high-dose challenge virus groups, independent of antiviral treatment. Bacterial phyla in the lungs such as Bacteroidetes decreased, while Firmicutes and Proteobacteria were significantly enriched in mice challenged with a high dose of SARS-CoV-2. In summary, we identified changes in the cecal and lung microbiomes of K18-hACE2 mice with severe clinical signs of SARS-CoV-2 infection. IMPORTANCE The COVID-19 pandemic has resulted in millions of deaths. The host's respiratory and intestinal microbiome can affect directly or indirectly the immune system during viral infections. We characterized the cecal and lung microbiomes in a relevant mouse model challenged with a low or high dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the presence or absence of an antiviral Mpro inhibitor, GC-376. Decreased microbial diversity and taxonomic abundances of the phyla Firmicutes, particularly, Lachnospiraceae, correlating with infection dosage were observed in the cecum. In addition, microbes within the family Akkermansiaceae were increasingly more prevalent during peak infection, which is observed in other viral infections. The lung microbiome showed similar microbial diversity to that of the control, independent of antiviral treatment. Decreased Bacteroidetes and increased Firmicutes and Proteobacteria were observed in the lungs in a virus dose-dependent manner. These studies add to a better understanding of the complexities associated with the intestinal microbiome during respiratory infections.

Separation of Donor and Recipient Microbial Diversity Allows Determination of Taxonomic and Functional Features of Gut Microbiota Restructuring following Fecal Transplantation

Fecal microbiota transplantation (FMT) is currently used in medicine to treat recurrent clostridial colitis and other intestinal diseases. However, neither the therapeutic mechanism of FMT nor the mechanism that allows the donor bacteria to colonize the intestine of the recipient has yet been clearly described. From a biological point of view, FMT can be considered a useful model for studying the ecology of host-associated microbial communities. FMT experiments can shed light on the relationship features between the host and its gut microbiota. This creates the need for experimentation with approaches to metagenomic data analysis which may be useful for the interpretation of observed biological phenomena. Here, the recipient intestine colonization analysis tool (RECAST) novel computational approach is presented, which is based on the metagenomic read sorting process per their origin in the recipient’s post-FMT stool metagenome. Using the RECAST algorithm, taxonomic/functional annotation, and machine learning approaches, the metagenomes from three FMT studies, including healthy volunteers, patients with clostridial colitis, and patients with metabolic syndrome, were analyzed. Using our computational pipeline, the donor-derived and recipient-derived microbes which formed the recipient post-FMT stool metagenomes (successful microbes) were identified. Their presence is well explained by a higher relative abundance in donor/pre-FMT recipient metagenomes or other metagenomes from the human population. In addition, successful microbes are enriched with gene groups potentially related to antibiotic resistance, including antimicrobial peptides. Interestingly, the observed reorganization features are universal and independent of the disease.

IMPORTANCE We assumed that the enrichment of successful gut microbes by lantibiotic/antibiotic resistance genes can be related to gut microbiota colonization resistance by third-party microbe phenomena and resistance to bacterium-derived or host-derived antimicrobial substances. According to this assumption, competition between the donor-derived and recipient-derived microbes as well as host immunity may play a key role in the FMT-related colonization and redistribution of recipient gut microbiota structure.

Author Video: An author video summary of this article is available.

Fecal sample collection methods and time of day impact microbiome composition and short chain fatty acid concentrations

Associations between the human gut microbiome and health outcomes continues to be of great interest, although fecal sample collection methods which impact microbiome studies are sometimes neglected. Here, we expand on previous work in sample optimization, to promote high quality microbiome data. To compare fecal sample collection methods, amplicons from the bacterial 16S rRNA gene (V4) and fungal (ITS2) region, as well as short chain fatty acid (SCFA) concentrations were determined in fecal material over three timepoints. We demonstrated that spot sampling of stool results in variable detection of some microbial members, and inconsistent levels of SCFA; therefore, sample homogenization prior to subsequent analysis or subsampling is recommended. We also identify a trend in microbial and metabolite composition that shifts over two consecutive stool collections less than 25 h apart. Lastly, we show significant differences in bacterial composition that result from collecting stool samples in OMNIgene·Gut tube (DNA Genotec) or Stool Nucleic Acid Collection and Preservation Tube (NORGEN) compared to immediate freezing. To assist with planning fecal sample collection and storage procedures for microbiome investigations with multiple analyses, we recommend participants to collect the first full bowel movement of the day and freeze the sample immediately after collection.

Gut microbiota variations in patients diagnosed with major depressive disorder-A systematic review

OBJECTIVE

The etiology of major depressive disorder (MDD) is multi-factorial and has been associated with a perturbed gut microbiota. Thus, it is therefore of great importance to determine any variations in gut microbiota in patients with MDD.

METHODS

A systematic literature search was conducted including original research articles based on gut microbiota studies performed in patients with MDD. Demographic and clinical characteristics, applied methodology and observed gut microbiota composition were compared between included studies.

RESULTS

Seventeen studies were included with a total of 738 patients with MDD and 782 healthy controls using different DNA purification methods, sequencing platforms and data analysis models. Four studies found a reduced α-diversity in patients with MDD, while gut microbiota compositions clustered separately according to β-diversity between patients and controls in twelve studies. Additionally, there was an increase in relative abundance of Eggerthella, Atopobium, and Bifidobacterium and a decreased relative abundance of Faecalibacterium in patients with MDD compared with healthy controls.

CONCLUSION

Gut microbiota differs significantly when comparing patients with MDD and healthy controls, though inconsistently across studies. The heterogeneity in gut microbiota compositions between the studies may be explained by variations in study design.

Dispersal strategies shape persistence and evolution of human gut bacteria

Human gut bacterial strains can co-exist with their hosts for decades, but little is known about how these microbes persist and disperse, and evolve thereby. Here, we examined these processes in 5,278 adult and infant fecal metagenomes, longitudinally sampled in individuals and families. Our analyses revealed that a subset of gut species is extremely persistent in individuals, families, and geographic regions, represented often by locally successful strains of the phylum Bacteroidota. These “tenacious” bacteria show high levels of genetic adaptation to the human host but a high probability of loss upon antibiotic interventions. By contrast, heredipersistent bacteria, notably Firmicutes, often rely on dispersal strategies with weak phylogeographic patterns but strong family transmissions, likely related to sporulation. These analyses describe how different dispersal strategies can lead to the long-term persistence of human gut microbes with implications for gut flora modulations.

•

Bacterial strains may persist within family members through transfer

•

Bacteria adapt dispersal strategies: heredipersistent, spatiopersistent, and tenacious

•

Dispersal strategies correlate with genetic bottlenecks and effective population size

Critical evaluation of faecal microbiome preservation using metagenomic analysis

The ability to preserve microbial communities in faecal samples is essential as increasing numbers of studies seek to use the gut microbiome to identify biomarkers of disease. Here we use shotgun metagenomics to rigorously evaluate the technical and compositional reproducibility of five room temperature (RT) microbial stabilisation methods compared to the best practice of flash-freezing. These methods included RNALater, OMNIGene-GUT, a dry BBL swab, LifeGuard, and a novel method for preserving faecal samples, a Copan FLOQSwab in an active drying tube (FLOQSwab-ADT). Each method was assessed using six replicate faecal samples from five participants, totalling 180 samples. The FLOQSwab-ADT performed best for both technical and compositional reproducibility, followed by RNAlater and OMNIgene-GUT. LifeGuard and the BBL swab had unpredictable outgrowth of Escherichia species in at least one replicate from each participant. We further evaluated the FLOQSwab-ADT in an additional 239 samples across 10 individuals after storage at -20 °C, RT, and 50 °C for four weeks compared to fresh controls. The FLOQSwab-ADT maintained its performance across all temperatures, indicating this method is an excellent alternative to existing RT stabilisation methods.

Standardization of microbiome studies for urolithiasis: an international consensus agreement

Numerous metagenome-wide association studies (MWAS) for urolithiasis have been published, leading to the discovery of potential interactions between the microbiome and urolithiasis. However, questions remain about the reproducibility, applicability and physiological relevance of these data owing to discrepancies in experimental technique and a lack of standardization in the field. One barrier to interpreting MWAS is that experimental biases can be introduced at every step of the experimental pipeline, including sample collection, preservation, storage, processing, sequencing, data analysis and validation. Thus, the introduction of standardized protocols that maintain the flexibility to achieve study-specific objectives is urgently required. To address this need, the first international consortium for microbiome in urinary stone disease - MICROCOSM - was created and consensus panel members were asked to participate in a consensus meeting to develop standardized protocols for microbiome studies if they had published an MWAS on urolithiasis. Study-specific protocols were revised until a consensus was reached. This consensus group generated standardized protocols, which are publicly available via a secure online server, for each step in the typical clinical microbiome-urolithiasis study pipeline. This standardization creates the benchmark for future studies to facilitate consistent interpretation of results and, collectively, to lead to effective interventions to prevent the onset of urolithiasis, and will also be useful for investigators interested in microbiome research in other urological diseases.

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

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

SuRF: A new method for sparse variable selection, with application in microbiome data analysis

In this article, we present a new variable selection method for regression and classification purposes, particularly for microbiome analysis. Our method, called subsampling ranking forward selection (SuRF), is based on LASSO penalized regression, subsampling and forward-selection methods. SuRF offers major advantages over existing variable selection methods in terms of both sparsity of selected models and model inference. We provide an R package that can implement our method for generalized linear models. We apply our method to classification problems from microbiome data, using a novel agglomeration approach to deal with the special tree-like correlation structure of the variables. Existing methods arbitrarily choose a taxonomic level a priori before performing the analysis, whereas by combining SuRF with these aggregated variables, we are able to identify the key biomarkers at the appropriate taxonomic level, as suggested by the data. We present simulations in multiple sparse settings to demonstrate that our approach performs better than several other popularly used existing approaches in recovering the true variables. We apply SuRF to two microbiome datasets: one about prediction of pouchitis and another for identifying samples from two healthy individuals. We find that SuRF can provide a better or comparable prediction with other methods while controlling the false positive rate of variable selection.

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

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

Enterotypes of the Gut Microbial Community and Their Response to Plant Secondary Compounds in Plateau Pikas

Animal gut microbiomes can be clustered into “enterotypes” characterized by an abundance of signature genera. The characteristic determinants, stability, and resilience of these community clusters remain poorly understood. We used plateau pika (Ochotona curzoniae) as a model and identified three enterotypes by 16S rDNA sequencing. Among the top 15 genera, 13 showed significantly different levels of abundance between the enterotypes combined with different microbial functions and distinct fecal short-chain fatty acids. We monitored changes in the microbial community associated with the transfer of plateau pikas from field to laboratory and observed that feeding them a single diet reduced microbial diversity, resulting in a single enterotype with an altered composition of the dominant bacteria. However, microbial diversity, an abundance of some changed dominant genera, and enterotypes were partially restored after adding swainsonine (a plant secondary compound found in the natural diet of plateau pikas) to the feed. These results provide strong evidence that gut microbial diversity and enterotypes are directly related to specific diet, thereby indicating that the formation of different enterotypes can help animals adapt to complex food conditions. Additionally, natural plant secondary compounds can maintain dominant bacteria and inter-individual differences of gut microbiota and promote the resilience of enterotypes in small herbivorous mammals.

Impact of time and temperature on gut microbiota and SCFA composition in stool samples

Gut dysbiosis has been implicated in the pathophysiology of a growing number of non-communicable diseases. High through-put sequencing technologies and short chain fatty acid (SCFA) profiling enables surveying of the composition and function of the gut microbiota and provide key insights into host-microbiome interactions. However, a methodological problem with analyzing stool samples is that samples are treated and stored differently prior to submission for analysis potentially influencing the composition of the microbiota and its metabolites. In the present study, we simulated the sample acquisition of a large-scale study, in which stool samples were stored for up to two days in the fridge or at room temperature before being handed over to the hospital. To assess the influence of time and temperature on the microbial community and on SCFA composition in a controlled experimental setting, the stool samples of 10 individuals were exposed to room and fridge temperatures for 24 and 48 hours, respectively, and analyzed using 16S rRNA gene amplicon sequencing, qPCR and nuclear magnetic resonance spectroscopy. To best of our knowledge, this is the first study to investigate the influence of storage time and temperature on the absolute abundance of methanogens, and of Lactobacillus reuteri. The results indicate that values obtained for methanogens, L. reuteri and total bacteria are still representative even after storage for up to 48 hours at RT (20°C) or 4°C. The overall microbial composition and structure appeared to be influenced more by laboratory errors introduced during sample processing than by the actual effects of temperature and time. Although microbial activity was demonstrated by elevated SCFA at both 4°C and RT, SCFAs ratios were more stable over the different conditions and may be considered as long as samples are come from similar storage conditions.

Method development for cross-study microbiome data mining: Challenges and opportunities

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".

Increasing Comparability and Utility of Gut Microbiome Studies in Parkinson's Disease: A Systematic Review

Gut microbiota have been studied in relation to the pathophysiology of Parkinson's disease (PD) due to the early gastrointestinal symptomatology and presence of alpha-synuclein pathology in the enteric nervous system, hypothesized to ascend via the vagal nerve to the central nervous system. Accordingly, sixteen human case-control studies have published gut microbiome composition changes in PD and reported over 100 differentially abundant taxa covering all taxonomic levels from phylum to genus or species, depending on methodology. While certain findings were replicated across several studies, various contradictory findings were reported. Here, differences in methodologies and the presence of possible confounders in the study populations are assessed for their potential to confound the results of gut microbiome studies in PD. Gut microbiome studies in PD exhibited considerable variability with respect to the study population, sample transport conditions, laboratory protocols and sequencing, bioinformatics pipelines, and biostatistical methods. To move from the current heterogeneous dataset towards clinically relevant biomarkers and the identification of putative therapeutic targets, recommendations are derived from the limitations of the available studies to increase the future comparability of microbiome studies in PD. In addition, integration of currently available data on the gut microbiome in PD is proposed to identify robust gut microbiome profiles in PD. Furthermore, expansion of the current dataset with atypical parkinsonism cohorts, prodromal and treatment-naïve de novo PD subjects, measurements of fecal microbial concentrations and multi-omics assessments are required to provide clinically relevant biomarkers and reveal therapeutic targets within the gut microbiome of PD.

Mini-Review on the Possible Interconnections between the Gut-Brain Axis and the Infertility-Related Neuropsychiatric Comorbidities

Both the gut-brain axis (GBA) and the hypothalamic–pituitary–adrenal (HPA) axis remain an intriguing yet obscure network with a strong influence over other systems of organs. Recent reports have sought to describe the multitude of harmful stressors that may impact the HPA axis along with the interconnections between these. This has improved our knowledge of how the underlying mechanisms working to establish homeostasis are affected. A disruption to the HPA axis can amplify the chances of gastrointestinal deficiencies, whilst also increasing the risk of a wide spectrum of neuropsychiatric disorders. Thus, the influence of microorganisms found throughout the digestive tract possess the ability to affect both physiology and behaviour by triggering responses, which may be unfavourable. This is sometimes the case in of infertility. Numerous supplements have been formulated with the intention of rebalancing the gut microflora. Accordingly, the gut flora may alter the pharmacokinetics of drugs used as part of fertility treatments, potentially exacerbating the predisposition for various neurological disorders, regardless of the age and gender.

The promise and challenge of cancer microbiome research

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

Variations of Gut Microbiome Profile Under Different Storage Conditions and Preservation Periods: A Multi-Dimensional Evaluation

Gut dysbiosis is heavily involved in the development of various human diseases. There are thousands of publications per year for investigating the role of gut microbiota in diseases. However, emerging evidence has indicated the frequent data inconsistency between different studies, which is largely overlooked. There are many factors that can cause data variation and inconsistency during the process of microbiota study, in particular, sample storage conditions and sequencing process. Here, we systemically evaluated the impacts of six fecal sample storage conditions (three non-commercial storage protocols, −80°C, −80°C with 70% ethanol (ET_−80°C), 4°C with 70% ethanol (ET_4°C), and three commercial storage reagents, OMNIgeneGUT OMR-200 (GT) and MGIEasy (MGIE) at room temperature, and Longsee at 4°C (LS) on gut microbiome profile based on 16S rRNA gene sequencing. In addition, we also investigated the impacts of storage periods (1 and 2 weeks, or 6 months) and sequencing platform on microbiome profile. The efficacy of storage conditions was evaluated by DNA yield and quality, α and β diversity, relative abundance of the dominant and functional bacteria associated with short-chain fatty acid (SCFA) production, and BAs metabolism. Our current study suggested that −80°C was acceptable for fecal sample storage, and the addition of 70% ethanol had some benefits in maintaining the microbial community structure. Meanwhile, we found that samples in ET_4°C and GT reagents were comparable, both of them introduced some biases in α or β diversity, and the relative abundance of functional bacteria. Samples stored in MGIE reagent resulted in the least variation, whereas the most obvious variations were introduced by LS reagents. In addition, our results indicated that variations caused by storage condition were larger than that of storage time and sequencing platform. Collectively, our study provided a multi-dimensional evaluation on the impacts of storage conditions, storage time periods, and sequencing platform on gut microbial profile.

Multi-proxy analyses of a mid-15th century Middle Iron Age Bantu-speaker palaeo-faecal specimen elucidates the configuration of the 'ancestral' sub-Saharan African intestinal microbiome

BACKGROUND

The archaeological incidence of ancient human faecal material provides a rare opportunity to explore the taxonomic composition and metabolic capacity of the ancestral human intestinal microbiome (IM). Here, we report the results of the shotgun metagenomic analyses of an ancient South African palaeo-faecal specimen.

METHODS

Following the recovery of a single desiccated palaeo-faecal specimen from Bushman Rock Shelter in Limpopo Province, South Africa, we applied a multi-proxy analytical protocol to the sample. The extraction of ancient DNA from the specimen and its subsequent shotgun metagenomic sequencing facilitated the taxonomic and metabolic characterisation of this ancient human IM.

RESULTS

Our results indicate that the distal IM of the Neolithic 'Middle Iron Age' (c. AD 1460) Bantu-speaking individual exhibits features indicative of a largely mixed forager-agro-pastoralist diet. Subsequent comparison with the IMs of the Tyrolean Iceman (Ötzi) and contemporary Hadza hunter-gatherers, Malawian agro-pastoralists and Italians reveals that this IM precedes recent adaptation to 'Western' diets, including the consumption of coffee, tea, chocolate, citrus and soy, and the use of antibiotics, analgesics and also exposure to various toxic environmental pollutants.

CONCLUSIONS

Our analyses reveal some of the causes and means by which current human IMs are likely to have responded to recent dietary changes, prescription medications and environmental pollutants, providing rare insight into human IM evolution following the advent of the Neolithic c. 12,000 years ago. Video Abtract.

Understanding the microbiome: a primer on the role of the microbiome in colorectal neoplasia

Colorectal cancer is a leading cause of cancer-related death internationally, with mounting evidence pointing to the role of the microbiome in adenoma and cancer development. This article aims to provide clinicians with a foundation for understanding the field of research into the microbiome. We also illustrate the various ways in which the microbiota have been linked to colorectal cancer, with a specific focus on microbiota with identified virulence factors, and also on the ways that byproducts of microbiota metabolism may result in oncogenesis. We also review strategies for manipulating the microbiome for therapeutic effects.