MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.     

A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice

Defining the functional status of host-associated microbial ecosystems has proven challenging owing to the vast number of predicted genes within the microbiome and relatively poor understanding of community dynamics and community–host interaction. Metabolomic approaches, in which a large number of small molecule metabolites can be defined in a biological sample, offer a promising avenue to ‘fingerprint'' microbiota functional status. Here, we examined the effects of the human gut microbiota on the fecal and urinary metabolome of a humanized (HUM) mouse using an optimized ultra performance liquid chromatography–mass spectrometry-based method. Differences between HUM and conventional mouse urine and fecal metabolomic profiles support host-specific aspects of the microbiota''s metabolomic contribution, consistent with distinct microbial compositions. Comparison of microbiota composition and metabolome of mice humanized with different human donors revealed that the vast majority of metabolomic features observed in donor samples are produced in the corresponding HUM mice, and individual-specific features suggest ‘personalized'' aspects of functionality can be reconstituted in mice. Feeding the mice a defined, custom diet resulted in modification of the metabolite signatures, illustrating that host diet provides an avenue for altering gut microbiota functionality, which in turn can be monitored via metabolomics. Using a defined model microbiota consisting of one or two species, we show that simplified communities can drive major changes in the host metabolomic profile. Our results demonstrate that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.     

Effects of long-term antibiotic treatment on mice urinary aromatic amino acid profiles

The gut microbiota–host co-metabolites are good indicators for representing the cross-talk between host and gut microbiota in a bi-direct manner. There is increasing evidence that levels of aromatic amino acids (AAAs) are associated with the alteration of intestinal microbial community though the effects of long-term microbial disturbance remain unclear. Here we monitored the gut microbiota composition and host–microbiota co-metabolites AAA profiles of mice after gentamicin and ceftriaxone treatments for nearly 4 months since their weaning to reveal the relationship between host and microbiome in long- term microbial disturbances. The study was performed employing targeted LC-MS measurement of AAA-related metabolites and 16S RNA sequence of mice cecal contents. The results showed obvious decreased gut microbial diversity and decreased Firmicutes/Bacteroidetes ratio in the cecal contents after long-term antibiotics treatment. The accumulated AAA (tyrosine, phenylalanine and tryptophan) and re-distribution of their downstreaming metabolites that produced under the existence of intestinal flora were found in mice treated with antibiotics for 4 months. Our results suggested that the long-term antibiotic treatment significantly changed the composition of the gut microbiota and destroyed the homeostasis in the intestinal metabolism. And the urinary AAA could be an indicator for exploring interactions between host and gut microbiota.     

Where Next for Microbiome Research?

The development of high-throughput sequencing technologies has transformed our capacity to investigate the composition and dynamics of the microbial communities that populate diverse habitats. Over the past decade, these advances have yielded an avalanche of metagenomic data. The current stage of “van Leeuwenhoek”–like cataloguing, as well as functional analyses, will likely accelerate as DNA and RNA sequencing, plus protein and metabolic profiling capacities and computational tools, continue to improve. However, it is time to consider: what’s next for microbiome research? The short pieces included here briefly consider the challenges and opportunities awaiting microbiome research.

This Perspective is part of the “Where next?” Series.

Soon, we will enter an era when “the number of population genomes deposited in public databases will dwarf those from isolates and single cells” (Gene Tyson). Clearly, as all authors noted in the following, our focus will move from describing the composition of microbial communities to elucidating the principles that govern their assembly, dynamics, and functions. How will such principles be discovered? Elhanan Borenstein proposes that a systems biology–based approach, particularly the development of mathematical and computational models of the interactions between the specific community components, will be critical for understanding the function and dynamics of microbiomes. Evolutionary biologists Howard Ochman and Andrew Moeller want to decipher how microbial assemblies evolve but challenge us to also consider the role of microbial communities in organismal evolution, and they make the exciting prediction that microbes will be implicated in the evolution of eusociality and cooperation. Brett Finlay underscores the need for deciphering the mechanistic bases—particularly the chemical/metabolite signals—for interactions between members of microbial communities and their hosts. He emphasizes how this knowledge will enable creation of new tools to manipulate the microbiota, a key challenge for future investigation. Heidi Kong also encourages deciphering the mechanisms that underlie associations between particular skin surfaces and disorders and their respective microbiota. Jeffrey Gordon considers several intriguing opportunities as well as challenges that manipulation of the gut microbiota presents for improved human nutrition and health. Finally, Karen Nelson, Karim Dabbagh and Hamilton Smith suggest that using synthetic genomes to create novel microbes or even synthetic microbiomes offers a new way to engineer the microbiota. Overall, future microbiome research regarding the molecules and mechanisms mediating interactions between members of microbial communities and their hosts should lead to discovery of exciting new biology and transformative therapeutics.     

Stool Microbiome and Metabolome Differences between Colorectal Cancer Patients and Healthy Adults

In this study we used stool profiling to identify intestinal bacteria and metabolites that are differentially represented in humans with colorectal cancer (CRC) compared to healthy controls to identify how microbial functions may influence CRC development. Stool samples were collected from healthy adults (n = 10) and colorectal cancer patients (n = 11) prior to colon resection surgery at the University of Colorado Health-Poudre Valley Hospital in Fort Collins, CO. The V4 region of the 16s rRNA gene was pyrosequenced and both short chain fatty acids and global stool metabolites were extracted and analyzed utilizing Gas Chromatography-Mass Spectrometry (GC-MS). There were no significant differences in the overall microbial community structure associated with the disease state, but several bacterial genera, particularly butyrate-producing species, were under-represented in the CRC samples, while a mucin-degrading species, Akkermansia muciniphila, was about 4-fold higher in CRC (p<0.01). Proportionately higher amounts of butyrate were seen in stool of healthy individuals while relative concentrations of acetate were higher in stools of CRC patients. GC-MS profiling revealed higher concentrations of amino acids in stool samples from CRC patients and higher poly and monounsaturated fatty acids and ursodeoxycholic acid, a conjugated bile acid in stool samples from healthy adults (p<0.01). Correlative analysis between the combined datasets revealed some potential relationships between stool metabolites and certain bacterial species. These associations could provide insight into microbial functions occurring in a cancer environment and will help direct future mechanistic studies. Using integrated “omics” approaches may prove a useful tool in identifying functional groups of gastrointestinal bacteria and their associated metabolites as novel therapeutic and chemopreventive targets.     

Natural experiments and long-term monitoring are critical to understand and predict marine host–microbe ecology and evolution

Marine multicellular organisms host a diverse collection of bacteria, archaea, microbial eukaryotes, and viruses that form their microbiome. Such host-associated microbes can significantly influence the host’s physiological capacities; however, the identity and functional role(s) of key members of the microbiome (“core microbiome”) in most marine hosts coexisting in natural settings remain obscure. Also unclear is how dynamic interactions between hosts and the immense standing pool of microbial genetic variation will affect marine ecosystems’ capacity to adjust to environmental changes. Here, we argue that significantly advancing our understanding of how host-associated microbes shape marine hosts’ plastic and adaptive responses to environmental change requires (i) recognizing that individual host–microbe systems do not exist in an ecological or evolutionary vacuum and (ii) expanding the field toward long-term, multidisciplinary research on entire communities of hosts and microbes. Natural experiments, such as time-calibrated geological events associated with well-characterized environmental gradients, provide unique ecological and evolutionary contexts to address this challenge. We focus here particularly on mutualistic interactions between hosts and microbes, but note that many of the same lessons and approaches would apply to other types of interactions.

This Essay argues that in order to truly understand how marine hosts benefit from the immense diversity of microbes, we need to expand towards long-term, multi-disciplinary research focussing on few areas of the world’s ocean that we refer to as “natural experiments,” where processes can be studied at scales that far exceed those captured in laboratory experiments.     

Bronchiectasis as a Comorbidity of Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis

Background

Bronchiectasis revealed by chest computed tomography in COPD patients and its comorbid effect on prognosis have not been addressed by large-sized studies. Understanding the presence of bronchiectasis in COPD is important for future intervention and preventing disease progression.

Methods

Observational studies were identified from electronic literature searches in Cochrane library, PubMed, ScienceDirect databases, American Thoracic Society and European Respiratory Society meeting abstracts. A systematic review and meta-analysis of studies was performed to summarize the factors associated with bronchiectasis in COPD patients. Primary outcomes included the risks for exacerbation frequency, isolation of a potentially pathogenic microorganism, severe airway obstruction and mortality. Odds ratios (ORs) were pooled by random effects models.

Results

Fourteen observational studies were eligible for the study. Compared with COPD without bronchiectasis, comorbid bronchiectasis in COPD increased the risk of exacerbation (1.97, 95% CI, 1.29–3.00), isolation of a potentially pathogenic microorganism (4.11, 95%CI, 2.16–7.82), severe airway obstruction (1.31, 95% CI, 1.09–1.58) and mortality (1.96, 95% CI, 1.04–3.70).

Conclusions

The presence of bronchiectasis in patients with COPD was associated with exacerbation frequency, isolation of a potentially pathogenic microorganism, severe airway obstruction and mortality.     

Morpho-Functional 1H-MRI of the Lung in COPD: Short-Term Test-Retest Reliability

Purpose

Non-invasive end-points for interventional trials and tailored treatment regimes in chronic obstructive pulmonary disease (COPD) for monitoring regionally different manifestations of lung disease instead of global assessment of lung function with spirometry would be valuable. Proton nuclear magnetic resonance imaging (1H-MRI) allows for a radiation-free assessment of regional structure and function. The aim of this study was to evaluate the short-term reproducibility of a comprehensive morpho-functional lung MRI protocol in COPD.

Materials and Methods

20 prospectively enrolled COPD patients (GOLD I-IV) underwent 1H-MRI of the lung at 1.5T on two consecutive days, including sequences for morphology, 4D contrast-enhanced perfusion, and respiratory mechanics. Image quality and COPD-related morphological and functional changes were evaluated in consensus by three chest radiologists using a dedicated MRI-based visual scoring system. Test-retest reliability was calculated per each individual lung lobe for the extent of large airway (bronchiectasis, wall thickening, mucus plugging) and small airway abnormalities (tree in bud, peripheral bronchiectasis, mucus plugging), consolidations, nodules, parenchymal defects and perfusion defects. The presence of tracheal narrowing, dystelectasis, pleural effusion, pulmonary trunk ectasia, right ventricular enlargement and, finally, motion patterns of diaphragma and chest wall were addressed.

Results

Median global scores [10(Q1:8.00;Q3:16.00) vs.11(Q1:6.00;Q3:15.00)] as well as category subscores were similar between both timepoints, and kappa statistics indicated “almost perfect” global agreement (ĸ = 0.86, 95%CI = 0.81–0.91). Most subscores showed at least “substantial” agreement of MRI1 and MRI2 (ĸ = 0.64–1.00), whereas the agreement for the diagnosis of dystelectasis/effusion (ĸ = 0.42, 95%CI = 0.00–0.93) was “moderate” and of tracheal abnormalities (ĸ = 0.21, 95%CI = 0.00–0.75) “fair”. Most MRI acquisitions showed at least diagnostic quality at MRI1 (276 of 278) and MRI2 (259 of 264).

Conclusion

Morpho-functional 1H-MRI can be obtained with reproducible image quality and high short-term test-retest reliability for COPD-related morphological and functional changes of the lung. This underlines its potential value for the monitoring of regional lung characteristics in COPD trials.     

Religion and Completed Suicide: a Meta-Analysis

Introduction

Suicide is a major public health concern and a leading cause of death around the world. How religion influences the risk of completed suicide in different settings across the world requires clarification in order to best inform suicide prevention strategies.

Methods

A meta-analysis using search results from Pubmed and Web of Science databases was conducted following PRISMA protocol and using the keywords “religion” or “religious” or “religiosity” or “spiritual” or “spirituality” plus “suicide” or “suicidality” or “suicide attempt”. Random and fixed effects models were used to generate pooled ORs and I² values. Sub-analyses were conducted among the following categories: young age (<45yo), older age (≥45yo), western culture, eastern culture, and religious homogeneity.

Results

Nine studies that altogether evaluated 2339 suicide cases and 5252 comparison participants met all selection criteria and were included in the meta-analysis. The meta-analysis suggested an overall protective effect of religiosity from completed suicide with a pooled OR of 0.38 (95% CI: 0.21–0.71) and I² of 91%. Sub-analyses similarly revealed significant protective effects for studies performed in western cultures (OR = 0.29, 95% CI: 0.18–0.46), areas with religious homogeneity (OR = 0.18, 95% CI: 0.13–0.26), and among older populations (OR = 0.42, 95% CI: 0.21–0.84). High heterogeneity of our meta-analysis was attributed to three studies in which the methods varied from the other six.

Conclusion

Religion plays a protective role against suicide in a majority of settings where suicide research is conducted. However, this effect varies based on the cultural and religious context. Therefore, public health professionals need to strongly consider the current social and religious atmosphere of a given population when designing suicide prevention strategies.     

Distinct signatures of host–microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility. Gut microbial and liver metabolomics were followed by a combinatory approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and ultra performance liquid chromatography time of tlight MS/MS with subsequent multivariate statistical analysis, revealing distinctive host and microbial metabolome patterns between the C57J and the C57N strain. Many taurine-conjugated bile acids (TBAs) were significantly elevated in the cecum and decreased in liver samples from the C57J phenotype likely displaying different energy utilization behavior by the bacterial community and the host. Furthermore, several metabolite groups could specifically be associated with the C57N phenotype involving fatty acids, eicosanoids and urobilinoids. The mass differences based metabolite network approach enabled to extend the range of known metabolites to important bile acids (BAs) and novel taurine conjugates specific for both strains. In summary, our study showed clear alterations of the metabolome in the gastrointestinal tract and liver within a HFD-induced obesity mouse model in relation to the host–microbial nutritional adaptation.     

Phylogenetic and Metabolic Tracking of Gut Microbiota during Perinatal Development

The colonization and development of gut microbiota immediately after birth is highly variable and depends on several factors, such as delivery mode and modality of feeding during the first months of life. A cohort of 31 mother and neonate pairs, including 25 at-term caesarean (CS) and 6 vaginally (V) delivered neonates (DNs), were included in this study and 121 meconium/faecal samples were collected at days 1 through 30 following birth. Operational taxonomic units (OTUs) were assessed in 69 stool samples by phylogenetic microarray HITChip and inter- and intra-individual distributions were established by inter-OTUs correlation matrices and OTUs co-occurrence or co-exclusion networks. ¹H-NMR metabolites were determined in 70 stool samples, PCA analysis was performed on 55 CS DNs samples, and metabolome/OTUs co-correlations were assessed in 45 CS samples, providing an integrated map of the early microbiota OTUs-metabolome. A microbiota “core” of OTUs was identified that was independent of delivery mode and lactation stage, suggesting highly specialized communities that act as seminal colonizers of microbial networks. Correlations among OTUs, metabolites, and OTUs-metabolites revealed metabolic profiles associated with early microbial ecological dynamics, maturation of milk components, and host physiology.     

A Method for Selectively Enriching Microbial DNA from Contaminating Vertebrate Host DNA

DNA samples derived from vertebrate skin, bodily cavities and body fluids contain both host and microbial DNA; the latter often present as a minor component. Consequently, DNA sequencing of a microbiome sample frequently yields reads originating from the microbe(s) of interest, but with a vast excess of host genome-derived reads. In this study, we used a methyl-CpG binding domain (MBD) to separate methylated host DNA from microbial DNA based on differences in CpG methylation density. MBD fused to the Fc region of a human antibody (MBD-Fc) binds strongly to protein A paramagnetic beads, forming an effective one-step enrichment complex that was used to remove human or fish host DNA from bacterial and protistan DNA for subsequent sequencing and analysis. We report enrichment of DNA samples from human saliva, human blood, a mock malaria-infected blood sample and a black molly fish. When reads were mapped to reference genomes, sequence reads aligning to host genomes decreased 50-fold, while bacterial and Plasmodium DNA sequences reads increased 8–11.5-fold. The Shannon-Wiener diversity index was calculated for 149 bacterial species in saliva before and after enrichment. Unenriched saliva had an index of 4.72, while the enriched sample had an index of 4.80. The similarity of these indices demonstrates that bacterial species diversity and relative phylotype abundance remain conserved in enriched samples. Enrichment using the MBD-Fc method holds promise for targeted microbiome sequence analysis across a broad range of sample types.     

Testing in Microbiome-Profiling Studies with MiRKAT,the Microbiome Regression-Based Kernel Association Test

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for.