Diurnal rhythmicity of infant fecal microbiota and metabolites: A randomized controlled interventional trial with infant formula.

Microbiota assembly in the infant gut is influenced by diet. Breastfeeding and human breastmilk oligosaccharides promote the colonization of beneficial bifidobacteria. Infant formulas are supplemented with bifidobacteria or complex oligosaccharides, notably galacto-oligosaccharides (GOS), to mimic breast milk. To compare microbiota development across feeding modes, this randomized controlled intervention study (German Clinical Trial DRKS00012313) longitudinally sampled infant stool during the first year of life, revealing similar fecal bacterial communities between formula- and breast-fed infants (N = 210) but differences across age. Infant formula containing GOS sustained high levels of bifidobacteria compared with formula containing B. longum and B. breve or placebo. Metabolite and bacterial profiling revealed 24-h oscillations and circadian networks. Rhythmicity in bacterial diversity, specific taxa, and functional pathways increased with age and was strongest following breastfeeding and GOS supplementation. Circadian rhythms in dominant taxa were further maintained ex vivo in a chemostat model. Hence, microbiota rhythmicity develops early in life and is impacted by diet.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Systematic Evaluation of Clinical, Nutritional, and Fecal Microbial Factors for Their Association With Colorectal Polyps.

INTRODUCTION: The identification of risk factors for precursor lesions of colorectal cancer (CRC) holds great promise in the context of prevention. With this study, we aimed to identify patient characteristics associated with colorectal polyps (CPs) and polyp features of potential malignant progression. Furthermore, a potential association with gut microbiota in this context was investigated. METHODS: In this single-center study, a total of 162 patients with CPs and 91 control patients were included. Multiple variables including information on lifestyle, diet, serum parameters, and gut microbiota, analyzed by 16S-rRNA gene amplicon sequencing and functional imputations (Picrust2), were related to different aspects of CPs. RESULTS: We observed that elevated serum alkaline phosphatase (AP) levels were significantly associated with the presence of high-grade dysplastic polyps. This association was further seen for patients with CRC. Thereby, AP correlated with other parameters of liver function. We did not observe significant changes in the gut microbiota between patients with CP and their respective controls. However, a trend toward a lower alpha-diversity was seen in patients with CRC. Interestingly, AP was identified as a possible clinical effect modifier of stool sample beta diversity. DISCUSSION: We show for the first time an increased AP in premalignant CP. Furthermore, AP showed a significant influence on the microbial composition of the intestine. Relatively elevated liver enzymes, especially AP, may contribute to the detection of precancerous dysplastic or neoplastic changes in colorectal lesions. The association between elevated AP, premalignant CP, and the microbiome merits further study.

Gut Microbial Disruption in Critically Ill Patients with COVID-19-Associated Pulmonary Aspergillosis.

OBJECTIVES: COVID-19 disease can be exacerbated by Aspergillus superinfection (CAPA). However, the causes of CAPA are not yet fully understood. Recently, alterations in the gut microbiome have been associated with a more complicated and severe disease course in COVID-19 patients, most likely due to immunological mechanisms. The aim of this study was to investigate a potential association between severe CAPA and alterations in the gut and bronchial microbial composition. METHODS: We performed 16S rRNA gene amplicon sequencing of stool and bronchial samples from a total of 16 COVID-19 patients with CAPA and 26 patients without CAPA. All patients were admitted to the intensive care unit. Results were carefully tested for potentially confounding influences on the microbiome during hospitalization. RESULTS: We found that late in COVID-19 disease, CAPA patients exhibited a trend towards reduced gut microbial diversity. Furthermore, late-stage patients with CAPA superinfection exhibited an increased abundance of Staphylococcus epidermidis in the gut which was not found in late non-CAPA cases or early in the disease. The analysis of bronchial samples did not yield significant results. CONCLUSIONS: This is the first study showing that alterations in the gut microbiome accompany severe CAPA and possibly influence the host's immunological response. In particular, an increase in Staphylococcus epidermidis in the intestine could be of importance.

The intestinal clock drives the microbiome to maintain gastrointestinal homeostasis.

Diurnal (i.e., 24-hour) oscillations of the gut microbiome have been described in various species including mice and humans. However, the driving force behind these rhythms remains less clear. In this study, we differentiate between endogenous and exogenous time cues driving microbial rhythms. Our results demonstrate that fecal microbial oscillations are maintained in mice kept in the absence of light, supporting a role of the host's circadian system rather than representing a diurnal response to environmental changes. Intestinal epithelial cell-specific ablation of the core clock gene Bmal1 disrupts rhythmicity of microbiota. Targeted metabolomics functionally link intestinal clock-controlled bacteria to microbial-derived products, in particular branched-chain fatty acids and secondary bile acids. Microbiota transfer from intestinal clock-deficient mice into germ-free mice altered intestinal gene expression, enhanced lymphoid organ weights and suppressed immune cell recruitment. These results highlight the importance of functional intestinal clocks for microbiota composition and function, which is required to balance the host's gastrointestinal homeostasis.

Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut.

Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with "healthy" factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with "unhealthy" factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health.

Namco: a microbiome explorer.

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.

Offering Fiber-Enriched Foods Increases Fiber Intake in Adults With or Without Cardiometabolic Risk: A Randomized Controlled Trial.

INTRODUCTION: Previous efforts to increase fiber intake in the general population were disappointing despite growing awareness of the multiple benefits of a high fiber intake. Aim of the study was to investigate the acceptance and consumption of fiber-enriched foods. METHODS: One hundred and fifteen middle-aged healthy individuals with and without elevated waist circumference (> 102 cm in males and > 88 cm in females) were recruited and randomized to an intervention or an age- and sex-matched control group. Subjects assigned to the intervention group were invited to select fiber-enriched foods from a broad portfolio of products to increase fiber intake by 10 g/day. Control subjects could choose items from the same food basket without fiber enrichment. The primary outcome was the increase in dietary fiber intake, and secondary outcomes were changes in cardiometabolic risk factors, microbiota composition, food choices, and consumer acceptance of the fiber-enriched foods. RESULTS: Compared to baseline, daily fiber intake increased from 22.5 ± 8.0 to 34.0 ± 9.6 g/day after 4 weeks (p < 0.001) and to 36.0 ± 8.9 g/day after 12 weeks (p < 0.001) in the intervention group, whereas fiber intake remained unchanged in the control group. Participants rated the taste of the food products as pleasant without group differences. In both groups, the most liked foods included popular convenience foods such as pretzel breadstick, pizza salami, and pizza vegetarian. After 12 weeks of intervention, there were minor improvements in plasma lipids and parameters of glucose metabolism in both the intervention and control group compared to baseline, but no differences between the two groups. Increased fiber consumption resulted in an increased (p < 0.001) relative abundance of Tannerellaceae. CONCLUSIONS: Fiber-enrichment of popular foods increases fiber intake in a middle-aged population with and without cardiometabolic risk and may provide a simple, novel strategy to increase fiber intake in the population.

Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders.

The intestine harbours a complex array of microorganisms collectively known as the gut microbiota. The past two decades have witnessed increasing interest in studying the gut microbiota in health and disease, largely driven by rapid innovation in high-throughput multi-omics technologies. As a result, microbial dysbiosis has been linked to many human pathologies, including type 2 diabetes mellitus and inflammatory bowel disease. Integrated analyses of multi-omics data, including metagenomics and metabolomics along with measurements of host response and cataloguing of bacterial isolates, have identified many bacteria and bacterial products that are correlated with disease. Nevertheless, insight into the mechanisms through which microbes affect intestinal health requires going beyond correlation to causation. Current understanding of the contribution of the gut microbiota to disease causality remains limited, largely owing to the heterogeneity of microbial community structures, interindividual differences in disease evolution and incomplete understanding of the mechanisms that integrate microbiota-derived signals into host signalling pathways. In this Review, we provide a broad insight into the microbiome signatures linked to inflammatory and metabolic disorders, discuss outstanding challenges in this field and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism-host interactions.

Gut bacterial dysbiosis and instability is associated with the onset of complications and mortality in COVID-19.

There is a growing debate about the involvement of the gut microbiome in COVID-19, although it is not conclusively understood whether the microbiome has an impact on COVID-19, or vice versa, especially as analysis of amplicon data in hospitalized patients requires sophisticated cohort recruitment and integration of clinical parameters. Here, we analyzed fecal and saliva samples from SARS-CoV-2 infected and post COVID-19 patients and controls considering multiple influencing factors during hospitalization. 16S rRNA gene sequencing was performed on fecal and saliva samples from 108 COVID-19 and 22 post COVID-19 patients, 20 pneumonia controls and 26 asymptomatic controls. Patients were recruited over the first and second corona wave in Germany and detailed clinical parameters were considered. Serial samples per individual allowed intra-individual analysis. We found the gut and oral microbiota to be altered depending on number and type of COVID-19-associated complications and disease severity. The occurrence of individual complications was correlated with low-risk (e.g., Faecalibacterium prausznitzii) and high-risk bacteria (e.g., Parabacteroides ssp.). We demonstrated that a stable gut bacterial composition was associated with a favorable disease progression. Based on gut microbial profiles, we identified a model to estimate mortality in COVID-19. Gut microbiota are associated with the occurrence of complications in COVID-19 and may thereby influencing disease severity. A stable gut microbial composition may contribute to a favorable disease progression and using bacterial signatures to estimate mortality could contribute to diagnostic approaches. Importantly, we highlight challenges in the analysis of microbial data in the context of hospitalization.

High-Fructose Diet Alters Intestinal Microbial Profile and Correlates with Early Tumorigenesis in a Mouse Model of Barrett's Esophagus.

Esophageal adenocarcinoma (EAC) is mostly prevalent in industrialized countries and has been associated with obesity, commonly linked with a diet rich in fat and refined sugars containing high fructose concentrations. In meta-organisms, dietary components are digested and metabolized by the host and its gut microbiota. Fructose has been shown to induce proliferation and cell growth in pancreas and colon cancer cell lines and also alter the gut microbiota. In a previous study with the L2-IL-1B mouse model, we showed that a high-fat diet (HFD) accelerated EAC progression from its precursor lesion Barrett's esophagus (BE) through changes in the gut microbiota. Aiming to investigate whether a high-fructose diet (HFrD) also alters the gut microbiota and favors EAC carcinogenesis, we assessed the effects of HFrD on the phenotype and intestinal microbial communities of L2-IL1B mice. Results showed a moderate acceleration in histologic disease progression, a mild effect on the systemic inflammatory response, metabolic changes in the host, and a shift in the composition, metabolism, and functionality of intestinal microbial communities. We conclude that HFrD alters the overall balance of the gut microbiota and induces an acceleration in EAC progression in a less pronounced manner than HFD.

Alteration of Intestinal Microbiome of Clostridioides difficile-Infected Hamsters during the Treatment with Specific Cow Antibodies.

Clostridioides difficile infection (CDI) often develops after pretreatment with antibiotics, which can lead to damage of the intestinal microbiome. The approach of this study was to use specific polyclonal antibodies isolated from the milk of immunized cows to treat CDI, in contrast to the standard application of nonspecific antibiotics. To gain a deeper understanding of the role of the microbiome in the treatment of CDI with bovine antibodies, stool and intestinal fluid samples of hamsters were collected in large quantities from various treatments (>400 samples). The results show that the regeneration of the microbiome instantly begins with the start of the antibody treatment, in contrast to the Vancomycin-treated group where the diversity decreased significantly during the treatment duration. All antibody-treated hamsters that survived the initial phase also survived the entire study period. The results also show that the regeneration of the microbiome was not an antibody-induced regeneration, but a natural regeneration that occurred because no microbiota-inactivating substances were administered. In conclusion, the treatment with bovine antibodies is a functional therapy for both the acute treatment and the prevention of recurrence in hamsters and could meet the urgent need for CDI treatment alternatives in humans.

Network analysis methods for studying microbial communities: A mini review.

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation.

BACKGROUND: The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study. RESULTS: Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant's gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 (Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 (Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5. CONCLUSIONS: The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies. Video abstract.

Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing.

Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling.

16S rRNA gene amplicon sequencing is a popular approach for studying microbiomes. However, some basic concepts have still not been investigated comprehensively. We studied the occurrence of spurious sequences using defined microbial communities based on data either from the literature or generated in three sequencing facilities and analyzed via both operational taxonomic units (OTUs) and amplicon sequence variants (ASVs) approaches. OTU clustering and singleton removal, a commonly used approach, delivered approximately 50% (mock communities) to 80% (gnotobiotic mice) spurious taxa. The fraction of spurious taxa was generally lower based on ASV analysis, but varied depending on the gene region targeted and the barcoding system used. A relative abundance of 0.25% was found as an effective threshold below which the analysis of spurious taxa can be prevented to a large extent in both OTU- and ASV-based analysis approaches. Using this cutoff improved the reproducibility of analysis, i.e., variation in richness estimates was reduced by 38% compared with singleton filtering using six human fecal samples across seven sequencing runs. Beta-diversity analysis of human fecal communities was markedly affected by both the filtering strategy and the type of phylogenetic distances used for comparison, highlighting the importance of carefully analyzing data before drawing conclusions on microbiome changes. In summary, handling of artifact sequences during bioinformatic processing of 16S rRNA gene amplicon data requires careful attention to avoid the generation of misleading findings. We propose the concept of effective richness to facilitate the comparison of alpha-diversity across studies.

Comparing Circadian Rhythmicity in the Human Gut Microbiome.

Targeted sequencing of 16S rRNA genes enables the analysis of microbiomes. Here, we describe a protocol for the collection, storage, and preparation of fecal samples. We describe how we cluster similar sequences and assign bacterial taxonomies. Using diversity analysis and machine learning, we can extract disease-associated features. We also describe a circadian analysis to identify the presence or absence of rhythms in taxonomies. Differences in rhythmicity between cohorts can contribute to determining disease-associated bacterial signatures. For complete details on the use and execution of this protocol, please refer to Reitmeier et al. (2020).

Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes.

Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.

Investigation of Adiposity Measures and Operational Taxonomic unit (OTU) Data Transformation Procedures in Stool Samples from a German Cohort Study Using Machine Learning Algorithms.

The analysis of the gut microbiome with respect to health care prevention and diagnostic purposes is increasingly the focus of current research. We analyzed around 2000 stool samples from the KORA (Cooperative Health Research in the Region of Augsburg) cohort using high-throughput 16S rRNA gene amplicon sequencing representing a total microbial diversity of 2089 operational taxonomic units (OTUs). We evaluated the combination of three different components to assess the reflection of obesity related to microbiota profiles: (i) four prediction methods (i.e., partial least squares (PLS), support vector machine regression (SVMReg), random forest (RF), and M5Rules); (ii) five OTU data transformation approaches (i.e., no transformation, relative abundance without and with log-transformation, as well as centered and isometric log-ratio transformations); and (iii) predictions from nine measurements of obesity (i.e., body mass index, three measures of body shape, and five measures of body composition). Our results showed a substantial impact of all three components. The applications of SVMReg and PLS in combination with logarithmic data transformations resulted in considerably predictive models for waist circumference-related endpoints. These combinations were at best able to explain almost 40% of the variance in obesity measurements based on stool microbiota data (i.e., OTUs) only. A reduced loss in predictive performance was seen after sex-stratification in waist-height ratio compared to other waist-related measurements. Moreover, our analysis showed that the contribution of OTUs less prevalent and abundant is minor concerning the predictive power of our models.