Quantifying technical confounders in microbiome studies

Gut Microbiome Composition in Polycystic Ovary Syndrome Adult Women: A Systematic Review and Meta-analysis of Observational Studies

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder that is frequently linked to anovulation in women who are experiencing infertility. Intestinal flora, also known as the "second genome" of the host, is closely associated with chronic metabolic diseases. Recently, there has been increasing attention on the connection between PCOS and the gut microbiome, and experiments have been conducted. However, the results were unsatisfactory and inconsistent. This review aims to provide a comprehensive overview of the literature investigating the associations between the gut microbiome and PCOS in adults. The goal is to identify whether there are changes in the composition of the gut microbiome in individuals with PCOS. This is the first systematic review to focus on functional alterations in the gut microbiome, which could provide insights into potential mechanisms of microbial involvement in the development of PCOS. We found that there was no significant change in gut microbiome biodiversity in PCOS. Meta-analyses of three studies revealed a significantly higher abundance of Proteobacteria (1.12, 95% CI, 0.21, 2.02, I2 = 0%) in adults with PCOS. At the genus level, Bacteroides, Enterococcus, and Escherichia-Shigella were found to be enriched in patients with PCOS. Species such as Ruminococcus gnavus group, Parabacteroides distasonis, and Bacteroides fragilis showed an increase in PCOS. Metabolic pathways associated with glucose, lipid metabolism, bile acid metabolism, and protein absorption were found to be enriched in individuals with PCOS. The gut microbiome in PCOS is not characterized by lower diversity, but the composition is altered at the phylum, family, genus, or species level. Consequently, the metabolic pathway differs according to the phenotype of PCOS.

The microbial landscape of colorectal cancer

Colorectal cancer (CRC) is a substantial source of global morbidity and mortality in dire need of improved prevention and treatment strategies. As our understanding of CRC grows, it is becoming increasingly evident that the gut microbiota, consisting of trillions of microorganisms in direct interface with the colon, plays a substantial role in CRC development and progression. Understanding the roles that individual microorganisms and complex microbial communities play in CRC pathogenesis, along with their attendant mechanisms, will help yield novel preventive and therapeutic interventions for CRC. In this Review, we discuss recent evidence concerning global perturbations of the gut microbiota in CRC, associations of specific microorganisms with CRC, the underlying mechanisms by which microorganisms potentially drive CRC development and the roles of complex microbial communities in CRC pathogenesis. While our understanding of the relationship between the microbiota and CRC has improved in recent years, our findings highlight substantial gaps in current research that need to be filled before this knowledge can be used to the benefit of patients.

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

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

Choice of DNA extraction method affects stool microbiome recovery and subsequent phenotypic association analyses

The lack of standardization in the methods of DNA extraction from fecal samples represents the major source of experimental variation in the microbiome research field. In this study, we aimed to compare the metagenomic profiles and microbiome-phenotype associations obtained by applying two commercially available DNA extraction kits: the AllPrep DNA/RNA Mini Kit (APK) and the QIAamp Fast DNA Stool Mini Kit (FSK). Using metagenomic sequencing data available from 745 paired fecal samples from two independent population cohorts, Lifelines-DEEP (LLD, n = 292) and the 500 Functional Genomics project (500FG, n = 453), we confirmed significant differences in DNA yield and the recovered microbial communities between protocols, with the APK method resulting in a higher DNA concentration and microbial diversity. Further, we observed a massive difference in bacterial relative abundances at species-level between the APK and the FSK protocols, with > 75% of species differentially abundant between protocols in both cohorts. Specifically, comparison with a standard mock community revealed that the APK method provided higher accuracy in the recovery of microbial relative abundances, with the absence of a bead-beating step in the FSK protocol causing an underrepresentation of gram-positive bacteria. This heterogeneity in the recovered microbial composition led to remarkable differences in the association with anthropometric and lifestyle phenotypes. The results of this study further reinforce that the choice of DNA extraction method impacts the metagenomic profile of human gut microbiota and highlight the importance of harmonizing protocols in microbiome studies.

Quantifying bias introduced by sample collection in relative and absolute microbiome measurements

To gain insight into the accuracy of microbial measurements, it is important to evaluate sources of bias related to sample condition, preservative method and bioinformatic analyses. There is increasing evidence that measurement of the total count and concentration of microbes in the gut, or 'absolute abundance', provides a richer source of information than relative abundance and can correct some conclusions drawn from relative abundance data. However, little is known about how preservative choice can affect these measurements. In this study, we investigated how two common preservatives and short-term storage conditions impact relative and absolute microbial measurements. OMNIgene GUT OMR-200 yields lower metagenomic taxonomic variation between different storage temperatures, whereas Zymo DNA/RNA Shield yields lower metatranscriptomic taxonomic variation. Absolute abundance quantification reveals two different causes of variable Bacteroidetes:Firmicutes ratios across preservatives. Based on these results, we recommend OMNIgene GUT OMR-200 preservative for field studies and Zymo DNA/RNA Shield for metatranscriptomics studies, and we strongly encourage absolute quantification for microbial measurements.

Microbiome as a biomarker and therapeutic target in pancreatic cancer

Studying the effects of the microbiome on the development of different types of cancer has recently received increasing research attention. In this context, the microbial content of organs of the gastrointestinal tract has been proposed to play a potential role in the development of pancreatic cancer (PC). Proposed mechanisms for the pathogenesis of PC include persistent inflammation caused by microbiota leading to an impairment of antitumor immune surveillance and altered cellular processes in the tumor microenvironment. The limited available diagnostic markers that can currently be used for screening suggest the importance of microbial composition as a non-invasive biomarker that can be used in clinical settings. Samples including saliva, stool, and blood can be analyzed by 16 s rRNA sequencing to determine the relative abundance of specific bacteria. Studies have shown the potentially beneficial effects of prebiotics, probiotics, antibiotics, fecal microbial transplantation, and bacteriophage therapy in altering microbial diversity, and subsequently improving treatment outcomes. In this review, we summarize the potential impact of the microbiome in the pathogenesis of PC, and the role these microorganisms might play as biomarkers in the diagnosis and determining the prognosis of patients. We also discuss novel treatment methods being used to minimize or prevent the progression of dysbiosis by modulating the microbial composition. Emerging evidence is supportive of applying these findings to improve current therapeutic strategies employed in the treatment of PC.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

The role of diet and host species in shaping the seasonal dynamics of the gut microbiome

The gut microbiome plays an important role in the health and fitness of hosts. While previous studies have characterized the importance of various ecological and evolutionary factors in shaping the composition of the gut microbiome, most studies have been cross-sectional in nature, ignoring temporal variation. Thus, it remains unknown how these same factors might affect the stability and dynamics of the gut microbiome over time, resulting in variation across the tree of life. Here, we used samples collected in each of four seasons for three taxa: the herbivorous southern white rhinoceros (Ceratotherium simum simum, n = 5); the carnivorous Sumatran tiger (Panthera tigris sumatrae, n = 5); and the red panda (Ailurus fulgens, n = 9), a herbivorous carnivore that underwent a diet shift in its evolutionary history from carnivory to a primarily bamboo-based diet. We characterize the variability of the gut microbiome among these three taxa across time to elucidate the influence of diet and host species on these dynamics. Altogether, we found that red pandas exhibit marked seasonal variation in their gut microbial communities, experiencing both high microbial community turnover and high variation in how individual red panda's gut microbiota respond to seasonal changes. Conversely, while the gut microbiota of rhinoceros change throughout the year, all individuals respond in the same way to seasonal changes. Tigers experience relatively low levels of turnover throughout the year, yet the ways in which individuals respond to seasonal transitions are highly varied. We highlight how the differences in microbiome richness and network connectivity between these three species may affect the level of temporal stability in the gut microbiota across the year.

Age-Dependent and Body Composition-Dependent Association of Child Gut Microbial Enterotype With Puberty Timing: A Chinese Cohort

CONTEXT

Puberty timing, which is vital for adult well-being, has recently been suggested to be linked to specific gut taxa. However, the impact of comprehensive gut microbiome structure assessed by enterotype on puberty timing remains unknown.

OBJECTIVE

Investigate the prospective association of gut microbial enterotype with puberty timing and the potential interaction of age and body composition.

METHODS

This study included 1826 children from the Chinese Adolescent Cohort Study, a cohort that has collected information on sociodemographics, dietary intake, physical activity, anthropometry, and pubertal development of children aged 6-8 years since 2013 and follows them up annually until the age of 15 years. Fecal samples have been collected annually since 2019 and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Cox proportional hazard regression models were used to investigate the prospective association of enterotype with puberty timing and the impact of age and body mass index (BMI) sex- and age-independent standard deviation score (SDS).

RESULTS

592 (32.4%) and 1234 (67.6%) children belonged to the Prevotella-rich enterotype and the Bacteroides-rich enterotype, respectively. Children with the Bacteroides-rich enterotype experienced their menarche/voice break later than those with the Prevotella enterotype (hazard ratio 0.53, 95% CI 0.28-0.98), P = .02). Moreover, this association was more pronounced among younger children with higher BMI SDS (P for interaction = .006).

CONCLUSION

Our findings supported a role for gut microbial communities in pubertal development, in which younger children with higher body mass seems more sensitive.

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.

The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing

PCR amplicon sequencing may lead to detection of spurious operational taxonomic units (OTUs), inflating estimates of gut microbial diversity. There is no consensus in the analytical approach as to what filtering methods should be applied to remove low-abundance OTUs; moreover, few studies have investigated the reliability of OTU detection within replicates. Here, we investigated the reliability of OTU detection (% agreement in detecting OTU in triplicates) and accuracy of their quantification (assessed by coefficient of variation (CV)) in human stool specimens. Stool samples were collected from 12 participants 22-55 years old. We applied several methods for filtering low-abundance OTUs and determined their impact on alpha-diversity and beta-diversity metrics. The reliability of OTU detection without any filtering was only 44.1% (SE=0.9) but increased after filtering low-abundance OTUs. After filtering OTUs with <0.1% abundance in the dataset, the reliability increased to 87.7% (SE=0.6) but at the expense of removing 6.97% reads from the dataset. When filtering was based on individual sample, the reliability increased to 73.1% after filtering OTUs with <10 copies while removing only 1.12% of reads. High abundance OTUs (>10 copies in sample) had lower CV, indicating better accuracy of quantification than low-abundance OTUs. Excluding very low-abundance OTUs had a significant impact on alpha-diversity metrics sensitive to the presence of rare species (observed OTUs, Chao1) but had little impact on relative abundance of major phyla and families and alpha-diversity metrics accounting for both richness and evenness (Shannon, Inverse Simpson). To increase the reliability of microbial composition, we advise removing OTUs with <10 copies in individual samples, particularly in studies where only one subsample per specimen is available for analysis.

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

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

Sputum Microbiome and Chronic Obstructive Pulmonary Disease in a Rural Ugandan Cohort of Well-Controlled HIV Infection

Sub-Saharan Africa has increased morbidity and mortality related to chronic obstructive pulmonary disease (COPD). COPD among people living with HIV (PLWH) has not been well studied in this region, where HIV/AIDS is endemic. Increasing evidence suggests that respiratory microbial composition plays a role in COPD severity. Therefore, we aimed to investigate microbiome patterns and associations among PLWH with COPD in Sub-Saharan Africa. We conducted a cross-sectional study of 200 adults stratified by HIV and COPD in rural Uganda. Induced sputum samples were collected as an easy-to-obtain proxy for the lower respiratory tract microbiota. We performed 16S rRNA gene sequencing and used PICRUSt2 (version 2.2.3) to infer the functional profiles of the microbial community. We used a statistical tool to detect changes in specific taxa that searches and adjusts for confounding factors such as antiretroviral therapy (ART), age, sex, and other participant characteristics. We could cluster the microbial community into three community types whose distribution was shown to be significantly impacted by HIV. Some genera, e.g., Veillonella, Actinomyces, Atopobium, and Filifactor, were significantly enriched in HIV-infected individuals, while the COPD status was significantly associated with Gammaproteobacteria and Selenomonas abundance. Furthermore, reduced bacterial richness and significant enrichment in Campylobacter were associated with HIV-COPD comorbidity. Functional prediction using PICRUSt2 revealed a significant depletion in glutamate degradation capacity pathways in HIV-positive patients. A comparison of our findings with an HIV cohort from the United Kingdom revealed significant differences in the sputum microbiome composition, irrespective of viral suppression. IMPORTANCE Even with ART available, HIV-infected individuals are at high risk of suffering comorbidities, as shown by the high prevalence of noninfectious lung diseases in the HIV population. Recent studies have suggested a role for the respiratory microbiota in driving chronic lung inflammation. The respiratory microbiota was significantly altered among PLWH, with disease persisting up to 3 years post-ART initiation and HIV suppression. The community structure and diversity of the sputum microbiota in COPD are associated with disease severity and clinical outcomes, both in stable COPD and during exacerbations. Therefore, a better understanding of the sputum microbiome among PLWH could improve COPD prognostic and risk stratification strategies. In this study, we observed that in a virologically suppressed HIV cohort in rural Uganda, we could show differences in sputum microbiota stratified by HIV and COPD, reduced bacterial richness, and significant enrichment in Campylobacter associated with HIV-COPD comorbidity.

Fasting intervention and its clinical effects on the human host and microbiome

Experimental trials in organisms ranging from yeast to humans have shown that various forms of reducing food intake (caloric restriction) appear to increase both overall and healthy lifespan, delaying the onset of disease and slowing the progression of biomarkers of aging. The gut microbiota is considered one of the key environmental factors strongly contributing to the regulation of host health. Perturbations in the composition and activity of the gut microbiome are thought to be involved in the emergence of multiple diseases. Indeed, many studies investigating gut microbiota have been performed and have shown strong associations between specific microorganisms and metabolic diseases including overweight, obesity, and type 2 diabetes mellitus as well as specific gastrointestinal disorders, neurodegenerative diseases, and even cancer. Dietary interventions known to reduce inflammation and improve metabolic health are potentiated by prior fasting. Inversely, birth weight differential host oxidative phosphorylation response to fasting implies epigenetic control of some of its effector pathways. There is substantial evidence for the efficacy of fasting in improving insulin signaling and blood glucose control, and in reducing inflammation, conditions for which, additionally, the gut microbiota has been identified as a site of both risk and protective factors. Accordingly, human gut microbiota, both in symbiont and pathobiont roles, have been proposed to impact and mediate some health benefits of fasting and could potentially affect many of these diseases. While results from small-N studies diverge, fasting consistently enriches widely recognized anti-inflammatory gut commensals such as Faecalibacterium and other short-chain fatty acid producers, which likely mediates some of its health effects through immune system and barrier function impact.

Mechanisms, therapeutic implications, and methodological challenges of gut microbiota and cardiovascular diseases: a position paper by the ESC Working Group on Coronary Pathophysiology and Microcirculation

The human gut microbiota is the microbial ecosystem in the small and large intestines of humans. It has been naturally preserved and evolved to play an important role in the function of the gastrointestinal tract and the physiology of its host, protecting from pathogen colonization, and participating in vitamin synthesis, the functions of the immune system, as well as glucose homeostasis and lipid metabolism, among others. Mounting evidence from animal and human studies indicates that the composition and metabolic profiles of the gut microbiota are linked to the pathogenesis of cardiovascular disease, particularly arterial hypertension, atherosclerosis, and heart failure. In this review article, we provide an overview of the function of the human gut microbiota, summarize, and critically address the evidence linking compositional and functional alterations of the gut microbiota with atherosclerosis and coronary artery disease and discuss the potential of strategies for therapeutically targeting the gut microbiota through various interventions.

Guts within guts: the microbiome of the intestinal helminth parasite Ascaris suum is derived but distinct from its host

BACKGROUND

Intestinal helminths are extremely prevalent among humans and animals. In particular, intestinal roundworms affect more than 1 billion people around the globe and are a major issue in animal husbandry. These pathogens live in intimate contact with the host gut microbiota and harbor bacteria within their own intestines. Knowledge of the bacterial host microbiome at the site of infection is limited, and data on the parasite microbiome is, to the best of our knowledge, non-existent.

RESULTS

The intestinal microbiome of the natural parasite and zoonotic macropathogen, Ascaris suum was analyzed in contrast to the diversity and composition of the infected host gut. 16S sequencing of the parasite intestine and host intestinal compartments showed that the parasite gut has a significantly less diverse microbiome than its host, and the host gut exhibits a reduced microbiome diversity at the site of parasite infection in the jejunum. While the host's microbiome composition at the site of infection significantly determines the microbiome composition of its parasite, microbial signatures differentiate the nematodes from their hosts as the Ascaris intestine supports the growth of microbes that are otherwise under-represented in the host gut.

CONCLUSION

Our data clearly indicate that a nematode infection reduces the microbiome diversity of the host gut, and that the nematode gut represents a selective bacterial niche harboring bacteria that are derived but distinct from the host gut. Video Abstract.

Assessment of Energy and Nutrient Intake and the Intestinal Microbiome (ErNst study): Protocol and Methods of a Cross-sectional Human Observational Study (Preprint)

BACKGROUND

On the national level, nutritional monitoring requires the assessment of reliable representative dietary intake data. To achieve this, standardized tools need to be developed, validated, and kept up-to-date with recent developments in food products and the nutritional behavior of the population. Recently, the human intestinal microbiome has been identified as an essential mediator between nutrition and host health. Despite growing interest in this connection, only a few associations between the microbiome, nutrition, and health have been clearly established. Available studies paint an inconsistent picture, partly due to a lack of standardization.

OBJECTIVE

First, we aim to verify if food consumption, as well as energy and nutrient intake of the German population, can be recorded validly by means of the dietary recall software GloboDiet, which will be applied in the German National Nutrition Monitoring. Second, we aim to obtain high-quality data using standard methods on the microbiome, combined with dietary intake data and additional fecal sample material, and to also assess the functional activity of the microbiome by measuring microbial metabolites.

METHODS

Healthy female and male participants aged between 18 and 79 years were recruited. Anthropometric measurements included body height and weight, BMI, and bioelectrical impedance analysis. For validation of the GloboDiet software, current food consumption was assessed with a 24-hour recall. Nitrogen and potassium concentrations were measured from 24-hour urine collections to enable comparison with the intake of protein and potassium estimated by the GloboDiet software. Physical activity was measured over at least 24 hours using a wearable accelerometer to validate the estimated energy intake. Stool samples were collected in duplicate for a single time point and used for DNA isolation and subsequent amplification and sequencing of the 16S rRNA gene to determine microbiome composition. For the identification of associations between nutrition and the microbiome, the habitual diet was determined using a food frequency questionnaire covering 30 days.

RESULTS

In total, 117 participants met the inclusion criteria. The study population was equally distributed between the sexes and 3 age groups (18-39, 40-59, and 60-79 years). Stool samples accompanying habitual diet data (30-day food frequency questionnaire) are available for 106 participants. Current diet data and 24-hour urine samples for the validation of GloboDiet are available for 109 participants, of which 82 cases also include physical activity data.

CONCLUSIONS

We completed the recruitment and sample collection of the ErNst study with a high degree of standardization. Samples and data will be used to validate the GloboDiet software for the German National Nutrition Monitoring and to compare microbiome composition and nutritional patterns.

TRIAL REGISTRATION

German Register of Clinical Studies DRKS00015216; https://drks.de/search/de/trial/DRKS00015216.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42529.

A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions

BACKGROUND

Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one's own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed.

RESULTS

We performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies.

CONCLUSIONS

Based on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery. Video Abstract.

Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome’s interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

EasyMap - An Interactive Web Tool for Evaluating and Comparing Associations of Clinical Variables and Microbiome Composition

One of the most common tasks in microbiome studies is comparing microbial profiles across various groups of people (e.g., sick vs. healthy). Routinely, researchers use multivariate linear regression models to address these challenges, such as linear regression packages, MaAsLin2, LEfSe, etc. In many cases, it is unclear which metadata variables should be included in the linear model, as many human-associated variables are correlated with one another. Thus, multiple models are often tested, each including a different set of variables, however the challenge of selecting the metadata variables in the final model remains. Here, we present EasyMap, an interactive online tool allowing for (1) running multiple multivariate linear regression models, on the same features and metadata; (2) visualizing the associations between microbial features and clinical metadata found in each model; and (3) comparing across the various models to identify the critical metadata variables and select the optimal model. EasyMap provides a side-by-side visualization of association results across the various models, each with additional metadata variables, enabling us to evaluate the impact of each metadata variable on the associated feature. EasyMap’s interface enables filtering associations by significance, focusing on specific microbes and finding the robust associations that are found across multiple models. While EasyMap was designed to analyze microbiome data, it can handle any other tabular data with numeric features and metadata variables. EasyMap takes the common task of multivariate linear regression to the next level, with an intuitive and simple user interface, allowing for wide comparisons of multiple models to identify the robust microbial feature associations. EasyMap is available at http://yassour.rcs.huji.ac.il/easymap.

Comparative analysis of DNA extraction and PCR product purification methods for cervicovaginal microbiome analysis using cpn60 microbial profiling

Background

The microbiota of the lower female genital tract plays an important role in women’s health. Microbial profiling using the chaperonin60 (cpn60) universal target (UT) improves resolution of vaginal species associated with negative health outcomes compared to the more commonly used 16S ribosomal DNA target. However, the choice of DNA extraction and PCR product purification methods may bias sequencing-based microbial studies and should be optimized for the sample type and molecular target used. In this study, we compared two commercial DNA extraction kits and two commercial PCR product purification kits for the microbial profiling of cervicovaginal samples using the cpn60 UT.

Methods

DNA from cervicovaginal secretions and vaginal lavage samples as well as mock community standards were extracted using either the specialized QIAamp DNA Microbiome Kit, or the standard DNeasy Blood & Tissue kit with enzymatic pre-treatment for enhanced lysis of gram-positive bacteria. Extracts were PCR amplified using well-established cpn60 primer sets and conditions. Products were then purified using a column-based method (QIAquick PCR Purification Kit) or a gel-based PCR clean-up method using the QIAEX II Gel Extraction Kit. Purified amplicons were sequenced with the MiSeq platform using standard procedures. The overall quality of each method was evaluated by measuring DNA yield, alpha diversity, and microbial composition.

Results

DNA extracted from cervicovaginal samples using the DNeasy Blood and Tissue kit, pre-treated with lysozyme and mutanolysin, resulted in increased DNA yield, bacterial diversity, and species representation compared to the QIAamp DNA Microbiome kit. The column-based PCR product purification approach also resulted in greater average DNA yield and wider species representation compared to a gel-based clean-up method. In conclusion, this study presents a fast, effective sample preparation method for high resolution cpn60 based microbial profiling of cervicovaginal samples.

Gut Microbiome Composition in Obese and Non-Obese Persons: A Systematic Review and Meta-Analysis

Whether the gut microbiome in obesity is characterized by lower diversity and altered composition at the phylum or genus level may be more accurately investigated using high-throughput sequencing technologies. We conducted a systematic review in PubMed and Embase including 32 cross-sectional studies assessing the gut microbiome composition by high-throughput sequencing in obese and non-obese adults. A significantly lower alpha diversity (Shannon index) in obese versus non-obese adults was observed in nine out of 22 studies, and meta-analysis of seven studies revealed a non-significant mean difference (−0.06, 95% CI −0.24, 0.12, I² = 81%). At the phylum level, significantly more Firmicutes and fewer Bacteroidetes in obese versus non-obese adults were observed in six out of seventeen, and in four out of eighteen studies, respectively. Meta-analyses of six studies revealed significantly higher Firmicutes (5.50, 95% 0.27, 10.73, I² = 81%) and non-significantly lower Bacteroidetes (−4.79, 95% CI −10.77, 1.20, I² = 86%). At the genus level, lower relative proportions of Bifidobacterium and Eggerthella and higher Acidaminococcus, Anaerococcus, Catenibacterium, Dialister, Dorea, Escherichia-Shigella, Eubacterium, Fusobacterium, Megasphera, Prevotella, Roseburia, Streptococcus, and Sutterella were found in obese versus non-obese adults. Although a proportion of studies found lower diversity and differences in gut microbiome composition in obese versus non-obese adults, the observed heterogeneity across studies precludes clear answers.

Potential Role of Akkermansia muciniphila in Parkinson's Disease and Other Neurological/Autoimmune Diseases

The composition of the gut microbiota, including Akkermansia muciniphila (A. muciniphila), is altered in many neurological diseases and may be involved in the pathophysiological processes of Parkinson's disease (PD). A. muciniphila, a mucin-degrading bacterium, is a potential next-generation microbe that has anti-inflammatory properties and is responsible for keeping the body healthy. As the role of A. muciniphila in PD has become increasingly apparent, we discuss the potential link between A. muciniphila and various neurological diseases (including PD) in the current review.

Microbial enrichment and storage for metagenomics of vaginal, skin, and saliva samples

Few validated protocols are available for large-scale collection, storage, and analysis of microbiome samples from the vagina, skin, and mouth. To prepare for a large-scale study on the female microbiome by remote self-sampling, we investigated the impact of sample collection, storage, and host DNA depletion on microbiome profiling. Vaginal, skin, and saliva samples were analyzed using 16S rRNA gene amplicon and metagenomic shotgun sequencing, and qPCR. Of the two tested storage buffers, the eNAT buffer could keep the microbial composition stable during various conditions. All three tested host DNA-depletion approaches showed a bias against Gram-negative taxa. However, using the HostZERO Microbial DNA and QIAamp DNA Microbiome kits, samples still clustered according to body site and not by depletion approach. Therefore, our study showed the effectiveness of these methods in depleting host DNA. Yet, a suitable approach is recommended for each habitat studied based on microbial composition.

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Lysis buffer keeps the microbial composition stable during various storage conditions

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Host DNA depletion introduces a larger bias toward Gram-negative taxa

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The HostZERO Microbial DNA kit performed best in human DNA depletion for metagenomics

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Body site-specific approach based on microbial composition is needed to minimize bias

Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis

In any research field, data access and data integration are major challenges that even large, well-established consortia face. Although data sharing initiatives are increasing, joint data analyses on nutrition and microbiomics in health and disease are still scarce. We aimed to identify observational studies with data on nutrition and gut microbiome composition from the Intestinal Microbiomics (INTIMIC) Knowledge Platform following the findable, accessible, interoperable, and reusable (FAIR) principles. An adapted template from the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) consortium was used to collect microbiome-specific information and other related factors. In total, 23 studies (17 longitudinal and 6 cross-sectional) were identified from Italy (7), Germany (6), Netherlands (3), Spain (2), Belgium (1), and France (1) or multiple countries (3). Of these, 21 studies collected information on both dietary intake (24 h dietary recall, food frequency questionnaire (FFQ), or Food Records) and gut microbiome. All studies collected stool samples. The most often used sequencing platform was Illumina MiSeq, and the preferred hypervariable regions of the 16S rRNA gene were V3–V4 or V4. The combination of datasets will allow for sufficiently powered investigations to increase the knowledge and understanding of the relationship between food and gut microbiome in health and disease.

Effect of a probiotic on blood pressure in grade 1 hypertension (HYPRO): protocol of a randomized controlled study

Background

Arterial hypertension is a major risk factor for cardiovascular disease and leads to target organ damage including stroke, heart failure, and kidney disease. About 1.5 billion people worldwide have hypertension, and it is estimated that it causes about 8 million deaths each year. Although there are several drugs available to lower blood pressure (BP), a great proportion of treated patients does not reach recommended treatment targets. Typical antihypertensive drugs target the vessels, the kidneys, and the heart. However, our gut microbiota also influences cardiovascular health, and gut dysbiosis is associated with hypertension. In this study protocol, we investigate the potential BP-lowering effect of a probiotic in patients with grade 1 hypertension.

Methods

This study is an exploratory, randomized, double-blind, placebo-controlled, parallel-group study. One hundred ten patients with grade 1 hypertension (treated or untreated) will be randomized to either the probiotic Vivomixx® or placebo. The primary endpoint is the nocturnal systolic BP measured by ambulatory blood pressure monitoring after 8 weeks adjusted for the baseline value. The secondary endpoints are changes from baseline in nocturnal diastolic BP, antihypertensive medication, fecal microbiome composition, fecal and serum metabolome, immune cell phenotypes, glucose variability after three standardized breakfasts, and health-related quality of life (PROMIS-29). We also assess the safety profile of the intervention.

Discussion

We postulate that various administrated bacteria (Lactobacilli, Bifidobacteria, and Streptococcus thermophilus) convert dietary components into active metabolites that positively affect immune cell function. A reduction of pro-inflammatory immune cell function could promote a BP-lowering effect.

Trial registration

ClinicalTrials.gov NCT03906578. Registered on 08 April 2019

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-020-04973-0.