Microbiome-based risk prediction in incident heart failure: a community challenge

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

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

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

Esophageal microbiome in active eosinophilic esophagitis and changes induced by different therapies

Eosinophilic esophagitis (EoE) is a chronic, immune-mediated inflammatory esophageal disease triggered by food antigens. Cumulative evidence supports the implication of microbiota and the innate immune system in the pathogenesis of EoE. Changes in the esophageal microbiome were investigated by applying 16S rRNA gene sequencing on esophageal biopsies of adult patients with active EoE at baseline (n = 30), and after achieving remission with either proton pump inhibitors (PPI, n = 10), swallowed topical corticosteroids (STC, n = 10) or food-elimination diets (FED, n = 10). Ten non-EoE biopsies were also characterized as controls. Compared to controls, no differences in alpha (intra-sample) diversity were found in EoE microbiota overall. However, it decreased significantly among patients who underwent FED. As for beta (inter-sample) diversity, non-EoE controls separated from EoE baseline samples. Post-treatment samples from patients treated with PPI and FED had a more similar microbiota composition, while those receiving STC were closer to controls. Differential testing of microbial relative abundance displayed significant changes for Filifactor, Parvimonas and Porphyromonas genera. Analysis of predicted functions indicated alterations in metabolic pathways and abundance of sulphur-cytochrome oxidoreductases. Our findings demonstrate changes in microbiota associated with EoE, as well as a treatment effect on the microbiome.

A comparison of the gut microbiome between long-term users and non-users of proton pump inhibitors

BACKGROUND

Proton pump inhibitor (PPI) use is associated with an increased risk of Clostridium difficile infection (CDI), though the mechanism is unclear. PPI induced alterations to the gut microbiome may facilitate the emergence of CDI, though the effects of PPIs on gut microbiota are not well characterised. [Correction added on 10 March 2016, after first online publication: microflora has been changed to microbiota throughout the article.]

AIM

To compare the faecal microbiomes of long-term PPI users to those with no history of PPI use.

METHODS

We used a population-based database to identify individuals with ≥5 years of continuous PPI use along with non-PPI using controls. Stool samples were subjected to microbiological analysis, with hierarchical clustering at genus level, along with alpha and beta diversity measures comparing the two groups. Metadata was accounted for using quantile regression to eliminate potential confounding variables in taxonomic abundance comparisons.

RESULTS

Sixty-one subjects (32 PPI, 29 controls) were analysed. While no significant differences in alpha diversity were found between the PPI users and controls, a moderate shift of the PPI users away from the non-PPI user cluster in the beta diversity was observed. After controlling for pertinent confounders, we discovered a decrease in Bacteroidetes and an increase in Firmicutes at the phylum level. We also performed species classifications and found Holdemania filiformis and Pseudoflavonifractor capillosus to be increased and decreased in the PPI cohort, respectively.

CONCLUSIONS

Long-term PPIs use has an effect on the gut microbiome. The alteration in the ratio of Firmicutes to Bacteroidetes may pre-dispose to the development of CDI.

Disturbance of the gut microbiota in early-life selectively affects visceral pain in adulthood without impacting cognitive or anxiety-related behaviors in male rats

Disruption of bacterial colonization during the early postnatal period is increasingly being linked to adverse health outcomes. Indeed, there is a growing appreciation that the gut microbiota plays a role in neurodevelopment. However, there is a paucity of information on the consequences of early-life manipulations of the gut microbiota on behavior. To this end we administered an antibiotic (vancomycin) from postnatal days 4-13 to male rat pups and assessed behavioral and physiological measures across all aspects of the brain-gut axis. In addition, we sought to confirm and expand the effects of early-life antibiotic treatment using a different antibiotic strategy (a cocktail of pimaricin, bacitracin, neomycin; orally) during the same time period in both female and male rat pups. Vancomycin significantly altered the microbiota, which was restored to control levels by 8 weeks of age. Notably, vancomycin-treated animals displayed visceral hypersensitivity in adulthood without any significant effect on anxiety responses as assessed in the elevated plus maze or open field tests. Moreover, cognitive performance in the Morris water maze was not affected by early-life dysbiosis. Immune and stress-related physiological responses were equally unaffected. The early-life antibiotic-induced visceral hypersensitivity was also observed in male rats given the antibiotic cocktail. Both treatments did not alter visceral pain perception in female rats. Changes in visceral pain perception in males were paralleled by distinct decreases in the transient receptor potential cation channel subfamily V member 1, the α-2A adrenergic receptor and cholecystokinin B receptor. In conclusion, a temporary disruption of the gut microbiota in early-life results in very specific and long-lasting changes in visceral sensitivity in male rats, a hallmark of stress-related functional disorders of the brain-gut axis such as irritable bowel disorder.

Correlation of rRNA gene amplicon pyrosequencing and bacterial culture for microbial compositional analysis of faecal samples from elderly Irish subjects

AIMS

The aim of this investigation was to establish the degree of correlation between measurements from culture-dependent microbiological techniques and from next generation sequencing technologies.

METHODS AND RESULTS

Data generated by both techniques were collected from faecal samples from 185 elderly Irish people involved in the ongoing ELDERMET study (http://eldermet.ucc.ie). The results for three groups of intestinal bacteria were compared. Bifidobacterium sp., Lactobacillus sp. and Enterobacteriaceae were enumerated on selective media through culture-dependent techniques, whereas proportions of these bacteria were determined through sequencing technology against the background of other bacteria. The Spearman's rank correlation coefficient determined a good correlation between results from culture-dependent microbiology and culture-independent techniques for all three bacterial groups assessed (correlation coefficients for Bifidobacterium sp., Lactobacillus sp. and Enterobacteriaceae were 0·380, 0·366 and 0·437, respectively).

CONCLUSION

Correlation between the two methods implies that a single method is capable of profiling intestinal Bifidobacterium, Lactobacillus and Enterobacteriaceae populations. However, both methods have advantages that justify their use in tandem.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first extensive study to compare bacterial counts from culture-dependent microbiological techniques and from next generation sequencing technologies.