Cell-Free Microbial DNA Analysis: Effects of Blood Plasma and Serum Quantity, Biobanking Protocols, and Isolation Kits.

Recent studies highlight the presence of bacterial sequences in the human blood, suggesting potential clinical significance for circulating microbial signatures. These sequences could presumably serve in the diagnosis, prediction, or monitoring of various health conditions. Ensuring the similarity of samples before bacterial analysis is crucial, especially when combining samples from different biobanks prepared under varying conditions (such as different DNA extraction kits, centrifugation conditions, blood collection tubes, etc.). In this study, we aimed to analyze the impact of different sample collection and nucleic acid extraction criteria (blood collection tube, centrifugation, input volume, and DNA extraction kit) on circulating bacterial composition. Blood samples from four healthy individuals were collected into three different sample collection tubes: K2EDTA plasma tube, sodium citrate plasma tube, and gel tube for blood serum. Tubes were centrifugated at standard and double centrifugation conditions. DNA extraction was performed using 100, 200, and 500 µL plasma/serum input volumes. DNA extraction was performed using three different isolation kits: Norgen plasma/serum cell-free circulating DNA purification micro kit, Applied Biosystems MagMAX cell-free DNA isolation kit, and Qiagen QIAamp MinElute cell-free circulating DNA mini kit. All samples were subjected to 16S rRNA V1-V2 library preparation and sequencing. In total, 216 DNA and 18 water control samples were included in the study. According to PERMANOVA, PCoA, Mann-Whitney, and FDR tests the effect of the DNA extraction kit on the microbiota composition was the greatest, whereas the type of blood collection tube, centrifugation type, and sample input volume for the extraction had minor effects. Samples extracted with the Norgen DNA extraction kit were enriched with Gram-negative bacteria, whereas samples extracted with the Qiagen and MagMAX kits were enriched with Gram-positive bacteria. Bacterial profiles of samples prepared with the Qiagen and MagMAX DNA extraction kits were more similar, whereas samples prepared with the Norgen DNA extraction kit were significantly different from other groups.

Uterine Microbiome: Does the Sampling Technique Matter?

Studies have proven the significance of microbial communities in various parts of the human body for health. In recent years it has been discovered that the uterine cavity is not sterile, and endometrium has its own microbiome which appears to have an impact on female fertility and gynecological pathologies. Lactobacillus has shown to dominate the microbial profile in the uterus and is considered an indicator of a healthy uterine environment. Yet, many argue that the Lactobacillus dominance is due to vaginal contamination during the sampling process. To date there is no clearly defined healthy endometrial microbial profile, which is largely due to the fact that determining the microbial community from the endometrium is complicated, and there is currently no consensus on sampling methods for the endometrial microbiome. As a result, this restricts ability to replicate discoveries made in other cohorts. Here we aim to give an overview of the sampling methods used and discuss what impedes the endometrial microbiome studies as well as how to reach a consensus on the study design. This knowledge could be incorporated into the future research and the knowledge on endometrial microbiome could be included into the diagnostics and treatment of female reproductive health.

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.

Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort.

Microbiome research is starting to move beyond the exploratory phase towards interventional trials and therefore well-characterized cohorts will be instrumental for generating hypotheses and providing new knowledge. As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. Here we analyze stool data from deep metagenomic sequencing together with rich phenotyping, including 71 diseases, 136 medications, 21 dietary questions, 5 medical procedures, and 19 other factors. We identify numerous relationships (n = 3262) with different microbiome features. In this study, we extend the understanding of microbiome-host interactions using electronic health data and show that long-term antibiotic usage, independent from recent administration, has a significant impact on the microbiome composition, partly explaining the common associations between diseases.

Differences in microbial profile of endometrial fluid and tissue samples in women with in vitro fertilization failure are driven by Lactobacillus abundance.

INTRODUCTION: The endometrial microbiota has been linked to several gynecological disorders, including infertility. It has been shown that the microbial profile of endometrium could have a role in fertilization and pregnancy outcomes. In this study we aim to assess the microbial community of endometrial tissue (ET) and endometrial fluid (EF) samples in women receiving in vitro fertilization (IVF) treatment. We also search for possible associations between chronic endometritis (CE) and endometrial microbiota. MATERIAL AND METHODS: This was a cohort study involving 25 women aged between 28 and 42 years with both primary and secondary infertility and with at least one IVF failure. The ET and EF sample collection was carried out between September 2016 and November 2018. Each of the participants provided two types of samples-tissue and fluid samples (50 samples in total). A 16S rRNA sequencing was performed on both of the sample types for microbial profile evaluation. CE was diagnosed based on a CD138 immunohistochemistry where CE diagnosis was confirmed in the presence of one or more plasma cells. Microbial profiles of women with and without CE were compared in both sample types separately. RESULTS: We report no differences in the microbial composition and alpha diversity (pObserved  = 0.07, pShannon  = 0.65, pInverse Simpson  = 0.59) between the EF and ET samples of IVF patients. We show that the abundance of the genus Lactobacillus influences the variation in microbial beta diversity between and fluid samples (r2  = 0.34; false discovery rate [FDR] <9.9 × 10-5 ). We report that 32% (8/25) of the participants had differences in Lactobacillus dominance in the paired samples and these samples also present a different microbial diversity (pShannon  = 0.06, FDRweighted UniFrac  = 0.01). These results suggest that the microbial differences between ET and fluid samples are driven by the abundance of genus Lactobacillus. The microbiome of CE and without CE (ie non-CE) women in our sample set of IVF patients was similar. CONCLUSIONS: Our findings show that genus Lactobacillus dominance is an important factor influencing the microbial composition of ET and fluid samples.

The Nutritional Supplement L-Alpha Glycerylphosphorylcholine Promotes Atherosclerosis.

L-alpha glycerylphosphorylcholine (GPC), a nutritional supplement, has been demonstrated to improve neurological function. However, a new study suggests that GPC supplementation increases incident stroke risk thus its potential adverse effects warrant further investigation. Here we show that GPC promotes atherosclerosis in hyperlipidemic Apoe-/- mice. GPC can be metabolized to trimethylamine N-oxide, a pro-atherogenic agent, suggesting a potential molecular mechanism underlying the observed atherosclerosis progression. GPC supplementation shifted the gut microbial community structure, characterized by increased abundance of Parabacteroides, Ruminococcus, and Bacteroides and decreased abundance of Akkermansia, Lactobacillus, and Roseburia, as determined by 16S rRNA gene sequencing. These data are consistent with a reduction in fecal and cecal short chain fatty acids in GPC-fed mice. Additionally, we found that GPC supplementation led to an increased relative abundance of choline trimethylamine lyase (cutC)-encoding bacteria via qPCR. Interrogation of host inflammatory signaling showed that GPC supplementation increased expression of the proinflammatory effectors CXCL13 and TIMP-1 and activated NF-κB and MAPK signaling pathways in human coronary artery endothelial cells. Finally, targeted and untargeted metabolomic analysis of murine plasma revealed additional metabolites associated with GPC supplementation and atherosclerosis. In summary, our results show GPC promotes atherosclerosis through multiple mechanisms and that caution should be applied when using GPC as a nutritional supplement.

Using fecal immunochemical tubes for the analysis of the gut microbiome has the potential to improve colorectal cancer screening.

Colorectal cancer (CRC) is a challenging public health problem which successful treatment depends on the stage at diagnosis. Recently, CRC-specific microbiome signatures have been proposed as a marker for CRC detection. Since many countries have initiated CRC screening programs, it would be useful to analyze the microbiome in the samples collected in fecal immunochemical test (FIT) tubes for fecal occult blood testing. Therefore, we investigated the impact of FIT tubes and stabilization buffer on the microbial community structure evaluated in stool samples from 30 volunteers and compared the detected communities to those of fresh-frozen samples, highlighting previously published cancer-specific communities. Altogether, 214 samples were analyzed by 16S rRNA gene sequencing, including positive and negative controls. Our results indicated that the variation between individuals was greater than the differences introduced by the collection strategy. The vast majority of the genera were stable for up to 7 days. None of the changes observed between fresh-frozen samples and FIT tube specimens were related to previously identified CRC-specific bacteria. Overall, we show that FIT tubes can be used for profiling the microbiota in CRC screening programs. This circumvents the need to collect additional samples and can possibly improve the sensitivity of CRC detection.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation.

The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and nonlinear associations. Additionally, the modeling strategy carried out allowed us to compare the stability of model performance and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modeling strategies and the value of interpretable machine learning.IMPORTANCE Recent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider the microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes-associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using the microbiome in personalized medicine promising.

The Gut Microbiome in Polycystic Ovary Syndrome and Its Association with Metabolic Traits.

CONTEXT: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. OBJECTIVE: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. DESIGN: Prospective, case-control study using the Northern Finland Birth Cohort 1966. SETTING: General community. PARTICIPANTS: A total of 102 PCOS women and 201 age- and body mass index (BMI)-matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. INTERVENTION: (s): None. MAIN OUTCOME MEASURE(S): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. RESULTS: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P = .979, unweighted UniFrac P = .175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P = .018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. CONCLUSION: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health.

IL-22 Paucity in APECED Is Associated With Mucosal and Microbial Alterations in Oral Cavity.

Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) is caused by recessive mutations in the AIRE gene. The hallmark of the disease is the production of highly neutralizing autoantibodies against type I interferons and IL-22. Considering the importance of IL-22 in maintaining mucosal barrier integrity and shaping its microbial community, we sought to study potential changes in the oral cavity in this model of human IL-22 paucity. We found that besides known Th22 cell deficiency, APECED patients have significantly fewer circulating MAIT cells with potential IL-22 secreting capacity. Saliva samples from APECED patients revealed local inflammation, the presence of autoantibodies against IFN-α and IL-22, and alterations in the oral microbiota. Moreover, gene expression data of buccal biopsy samples suggested impaired antimicrobial response and cell proliferation, both of which are processes regulated by IL-22. Our data complement the knowledge gained from mouse models and support the concept of IL-22 being a critical homeostatic cytokine in human mucosal sites.

Interactions between Roseburia intestinalis and diet modulate atherogenesis in a murine model.

Humans with metabolic and inflammatory diseases frequently harbour lower levels of butyrate-producing bacteria in their gut. However, it is not known whether variation in the levels of these organisms is causally linked with disease development and whether diet modifies the impact of these bacteria on health. Here we show that a prominent gut-associated butyrate-producing bacterial genus (Roseburia) is inversely correlated with atherosclerotic lesion development in a genetically diverse mouse population. We use germ-free apolipoprotein E-deficient mice colonized with synthetic microbial communities that differ in their capacity to generate butyrate to demonstrate that Roseburia intestinalis interacts with dietary plant polysaccharides to: impact gene expression in the intestine, directing metabolism away from glycolysis and toward fatty acid utilization; lower systemic inflammation; and ameliorate atherosclerosis. Furthermore, intestinal administration of butyrate reduces endotoxaemia and atherosclerosis development. Together, our results illustrate how modifiable diet-by-microbiota interactions impact cardiovascular disease, and suggest that interventions aimed at increasing the representation of butyrate-producing bacteria may provide protection against atherosclerosis.

Using the natural variation of mouse populations to understand host-gut microbiome interactions.

One approach to understanding gut microbiome-host interactions, described in this review, is to examine how natural variation in a model organism, where environmental factors can be controlled, affects the microbiome and, in turn, how the microbiome is associated with physiological or clinical traits. A variation of this approach, termed "systems genetics" is to characterize both the microbiome and the host using various high throughput technologies, such as metabolomics or gene expression of the microbiome and the host. By relating variation in the microbiome and host functions to such "molecular phenotypes", hypotheses can be generated and then experimentally tested. To model human gut microbiome-host interactions in this way, the mouse is particularly useful given the extensive body of genetic resources and experimental tools that are available.

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort.

BACKGROUND: The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study. RESULTS: We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota-metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state. CONCLUSIONS: Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host-gut microbiota relationships.

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure.

A typical genome-wide association study tests correlation between a single phenotype and each genotype one at a time. However, single-phenotype analysis might miss unmeasured aspects of complex biological networks. Analyzing many phenotypes simultaneously may increase the power to capture these unmeasured aspects and detect more variants. Several multivariate approaches aim to detect variants related to more than one phenotype, but these current approaches do not consider the effects of population structure. As a result, these approaches may result in a significant amount of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA for generalized analysis of molecular variance for mixed-model analysis, which is capable of simultaneously analyzing many phenotypes and correcting for population structure. In a simulated study using data implanted with true genetic effects, GAMMA accurately identifies these true effects without producing false positives induced by population structure. In simulations with this data, GAMMA is an improvement over other methods which either fail to detect true effects or produce many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mice and show that GAMMA identifies several variants that are likely to have true biological mechanisms.

Sex differences and hormonal effects on gut microbiota composition in mice.

We previously reported quantitation of gut microbiota in a panel of 89 different inbred strains of mice, and we now examine the question of sex differences in microbiota composition. When the total population of 689 mice was examined together, several taxa exhibited significant differences in abundance between sexes but a larger number of differences were observed at the single strain level, suggesting that sex differences can be obscured by host genetics and environmental factors. We also examined a subset of mice on chow and high fat diets and observed sex-by-diet interactions. We further investigated the sex differences using gonadectomized and hormone treated mice from 3 different inbred strains. Principal coordinate analysis with unweighted UniFrac distances revealed very clear effects of gonadectomy and hormone replacement on microbiota composition in all 3 strains. Moreover, bile acid analyses showed gender-specific differences as well as effects of gonodectomy, providing one possible mechanism mediating sex differences in microbiota composition.

Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk.

Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (n > 4,000) independently predicted incident (3 years) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced sub-maximal stimulus-dependent platelet activation from multiple agonists through augmented Ca(2+) release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk.

Non-lethal Inhibition of Gut Microbial Trimethylamine Production for the Treatment of Atherosclerosis.

Trimethylamine (TMA) N-oxide (TMAO), a gut-microbiota-dependent metabolite, both enhances atherosclerosis in animal models and is associated with cardiovascular risks in clinical studies. Here, we investigate the impact of targeted inhibition of the first step in TMAO generation, commensal microbial TMA production, on diet-induced atherosclerosis. A structural analog of choline, 3,3-dimethyl-1-butanol (DMB), is shown to non-lethally inhibit TMA formation from cultured microbes, to inhibit distinct microbial TMA lyases, and to both inhibit TMA production from physiologic polymicrobial cultures (e.g., intestinal contents, human feces) and reduce TMAO levels in mice fed a high-choline or L-carnitine diet. DMB inhibited choline diet-enhanced endogenous macrophage foam cell formation and atherosclerotic lesion development in apolipoprotein e(-/-) mice without alterations in circulating cholesterol levels. The present studies suggest that targeting gut microbial production of TMA specifically and non-lethal microbial inhibitors in general may serve as a potential therapeutic approach for the treatment of cardiometabolic diseases.

Genetic and environmental control of host-gut microbiota interactions.

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.

Unraveling the environmental and genetic interactions in atherosclerosis: Central role of the gut microbiota.

Recent studies have convincingly linked gut microbiota to traits relevant to atherosclerosis, such as insulin resistance, dyslipidemia and inflammation, and have revealed novel disease pathways involving microbe-derived metabolites. These results have important implications for understanding how environmental and genetic factors act together to influence cardiovascular disease (CVD) risk. Thus, dietary constituents are not only absorbed and metabolized by the host but they also perturb the gut microbiota, which in turn influence host metabolism and inflammation. It also appears that host genetics helps to shape the gut microbiota community. Here, we discuss challenges in understanding these interactions and the role they play in CVD.