Integrating biological knowledge for mechanistic inference in the host-associated microbiome.

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Poly-omic risk scores predict inflammatory bowel disease diagnosis.

Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

Gut Microbiome and Lipidome Signatures in Irritable Bowel Syndrome Patients from a Low-Income, Food-Desert Area: A Pilot Study.

Irritable bowel syndrome (IBS) is a common gastroenterological disorder with triggers such as fructose. We showed that our IBS patients suffering from socioeconomic challenges have a significantly high consumption of high-fructose corn syrup (HFCS). Here, we characterize gut microbial dysbiosis and fatty acid changes, with respect to IBS, HFCS consumption, and socioeconomic factors. Fecal samples from IBS patients and healthy controls were subjected to microbiome and lipidome analyses. We assessed phylogenetic diversity and community composition of the microbiomes, and used linear discriminant analysis effect size (LEfSe), analysis of compositions of microbiomes (ANCOM) on highly co-occurring subcommunities (modules), least absolute shrinkage and selection operator (LASSO) on phylogenetic isometric log-ratio transformed (PhILR) taxon abundances to identify differentially abundant taxa. Based on a Procrustes randomization test, the microbiome and lipidome datasets correlated significantly (p = 0.002). Alpha diversity correlated with economic factors (p < 0.001). Multiple subsets of the phylogenetic tree were associated with HFCS consumption (p < 0.001). In IBS patients, relative abundances of potentially beneficial bacteria such as Monoglobaceae, Lachnospiraceae, and Ruminococcaceae were lower (p = 0.007), and Eisenbergiella, associated with inflammatory disorders, was higher. In IBS patients, certain saturated fatty acids were higher and unsaturated fatty acids were lower (p < 0.05). Our study aims first to underscore the influence of HFCS consumption and socioeconomic factors on IBS pathophysiology, and provides new insights that inform patient care.

A Pilot Study Exploring Temporal Development of Gut Microbiome/Metabolome in Breastfed Neonates during the First Week of Life.

Purpose: Exclusive breastfeeding promotes gut microbial compositions associated with lower rates of metabolic and autoimmune diseases. Its cessation is implicated in increased microbiome-metabolome discordance, suggesting a vulnerability to dietary changes. Formula supplementation is common within our low-income, ethnic-minority community. We studied exclusively breastfed (EBF) neonates' early microbiome-metabolome coupling in efforts to build foundational knowledge needed to target this inequality. Methods: Maternal surveys and stool samples from seven EBF neonates at first transitional stool (0-24 hours), discharge (30-48 hours), and at first appointment (days 3-5) were collected. Survey included demographics, feeding method, medications, medical history and tobacco and alcohol use. Stool samples were processed for 16S rRNA gene sequencing and lipid analysis by gas chromatography-mass spectrometry. Alpha and beta diversity analyses and Procrustes randomization for associations were carried out. Results: Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria were the most abundant taxa. Variation in microbiome composition was greater between individuals than within (p=0.001). Palmitic, oleic, stearic, and linoleic acids were the most abundant lipids. Variation in lipid composition was greater between individuals than within (p=0.040). Multivariate composition of the metabolome, but not microbiome, correlated with time (p=0.030). Total lipids, saturated lipids, and unsaturated lipids concentrations increased over time (p=0.012, p=0.008, p=0.023). Alpha diversity did not correlate with time (p=0.403). Microbiome composition was not associated with each samples' metabolome (p=0.450). Conclusion: Neonate gut microbiomes were unique to each neonate; respective metabolome profiles demonstrated generalizable temporal developments. The overall variability suggests potential interplay between influences including maternal breastmilk composition, amount consumed and living environment.

SCNIC: Sparse correlation network investigation for compositional data.

Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. To address these challenges, we developed SCNIC: Sparse Cooccurrence Network Investigation for compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.

Impacts of breast cancer and chemotherapy on gut microbiome, cognitive functioning, and mood relative to healthy controls.

Women diagnosed with breast cancer undergoing chemotherapy experience cognitive impairment, symptoms of anxiety and depression, and physical side effects including disruption in the diversity and community composition of the gut microbiome. To date, there is limited research exploring the associations among these specific challenges. The present cross-sectional study explored the associations of self-reported cognitive functioning, depression, and anxiety symptoms, and gut microbiome diversity and community composition in women who were diagnosed with and undergoing chemotherapy treatment for breast cancer (BC) compared to cancer-free healthy controls (HC). The BC group displayed higher rates of cognitive dysfunction (p < 0.001) and depressive symptoms (p < 0.05) relative to HC. There was a significant difference in microbiome community composition between BC and HC, particularly characterized by a decreased relative abundance of the mucin-degrading genus Akkermansia in BC compared to HC (p < 0.05). Association models identified significant associations among group, cognitive, depression, and microbiome variables (p < 0.001). Overall, the study identified that BC participants experienced significant differences in self-reported cognitive functioning, self-reported depression symptoms, microbiome community composition, and mucin-degrading bacteria of the gut-mucosal barrier, relative to HC. The present study is consistent with the hypothesis that gut microbiome community composition impacts a woman's experience with breast cancer and treatment suggesting that microbiome-based interventions have potential for improving quality of life outcomes in individuals with breast cancer.

A Metagenomic Investigation of Spatial and Temporal Changes in Sewage Microbiomes across a University Campus.

Wastewater microbial communities are not static and can vary significantly across time and space, but this variation and the factors driving the observed spatiotemporal variation often remain undetermined. We used a shotgun metagenomic approach to investigate changes in wastewater microbial communities across 17 locations in a sewer network, with samples collected from each location over a 3-week period. Fecal material-derived bacteria constituted a relatively small fraction of the taxa found in the collected samples, highlighting the importance of environmental sources to the sewage microbiome. The prokaryotic communities were highly variable in composition depending on the location within the sampling network, and this spatial variation was most strongly associated with location-specific differences in sewage pH. However, we also observed substantial temporal variation in the composition of the prokaryotic communities at individual locations. This temporal variation was asynchronous across sampling locations, emphasizing the importance of independently considering both spatial and temporal variation when assessing the wastewater microbiome. The spatiotemporal patterns in viral community composition closely tracked those of the prokaryotic communities, allowing us to putatively identify the bacterial hosts of some of the dominant viruses in these systems. Finally, we found that antibiotic resistance gene profiles also exhibit a high degree of spatiotemporal variability, with most of these genes unlikely to be derived from fecal bacteria. Together, these results emphasize the dynamic nature of the wastewater microbiome, the challenges associated with studying these systems, and the utility of metagenomic approaches for building a multifaceted understanding of these microbial communities and their functional attributes. IMPORTANCE Sewage systems harbor extensive microbial diversity, including microbes derived from both human and environmental sources. Studies of the sewage microbiome are useful for monitoring public health and the health of our infrastructure, but the sewage microbiome can be highly variable in ways that are often unresolved. We sequenced DNA recovered from wastewater samples collected over a 3-week period at 17 locations in a single sewer system to determine how these communities vary across time and space. Most of the wastewater bacteria, and the antibiotic resistance genes they harbor, were not derived from human feces, but human usage patterns did impact how the amounts and types of bacteria and bacterial genes we found in these systems varied over time. Likewise, the wastewater communities, including both bacteria and their viruses, varied depending on location within the sewage network, highlighting the challenges and opportunities in efforts to monitor and understand the sewage microbiome.

Characterization of gut microbiome and metabolome in Helicobacter pylori patients in an underprivileged community in the United States.

BACKGROUND: Helicobacter pylori (H. pylori), a bacterium that infects approximately half of the world's population, is associated with various gastrointestinal diseases, including peptic ulcers, non-ulcer dyspepsia, gastric adenocarcinoma, and gastric lymphoma. As the burden of antibiotic resistance increases, the need for new adjunct therapies designed to facilitate H. pylori eradication and reduce negative distal outcomes associated with infection has become more pressing. Characterization of the interactions between H. pylori, the fecal microbiome, and fecal fatty acid metabolism, as well as the mechanisms underlying these interactions, may offer new therapeutic approaches. AIM: To characterize the gut microbiome and metabolome in H. pylori patients in a socioeconomically challenged and underprivileged inner-city community. METHODS: Stool samples from 19 H. pylori patients and 16 control subjects were analyzed. 16S rRNA gene sequencing was performed on normalized pooled amplicons using the Illumina MiSeq System using a MiSeq reagent kit v2. Alpha and beta diversity analyses were performed in QIIME 2. Non-targeted fatty acid analysis of the samples was carried out using gas chromatography-mass spectrometry, which measures the total content of 30 fatty acids in stool after conversion into their corresponding fatty acid methyl esters. Multi-dimensional scaling (MDS) was performed on Bray-Curtis distance matrices created from both the metabolomics and microbiome datasets and a Procrustes test was performed on the metabolomics and microbiome MDS coordinates. RESULTS: Fecal microbiome analysis showed that alpha diversity was lowest in H. pylori patients over 40 years of age compared to control subjects of similar age group. Beta diversity analysis of the samples revealed significant differences in microbial community structure between H. pylori patients and control subjects across all ages. Thirty-eight and six taxa had lower and higher relative abundance in H. pylori patients, respectively. Taxa that were enriched in H. pylori patients included Atopobium, Gemellaceae, Micrococcaceae, Gemellales and Rothia (R. mucilaginosa). Notably, relative abundance of the phylum Verrucomicrobia was decreased in H. pylori patients compared to control subjects. Procrustes analysis showed a significant relationship between the microbiome and metabolome datasets. Stool samples from H. pylori patients showed increases in several fatty acids including the polyunsaturated fatty acids (PUFAs) 22:4n6, 22:5n3, 20:3n6 and 22:2n6, while decreases were noted in other fatty acids including the PUFA 18:3n6. The pattern of changes in fatty acid concentration correlated to the Bacteroidetes:Firmicutes ratio determined by 16S rRNA gene analysis. CONCLUSION: This exploratory study demonstrates H. pylori-associated changes to the fecal microbiome and fecal fatty acid metabolism. Such changes may have implications for improving eradication rates and minimizing associated negative distal outcomes.

The effect varied scanning electron microscopy desiccation techniques has on demineralized dentin.

The study objective was to assess (a) the effect of a rubbing-application of ethylenediaminetetraacetic acid (EDTA) or citric acid (CA) has on the ultrastructure of surface dentin and (b) the effect of two scanning electron microscopy (SEM) desiccation preparation techniques have on the collagen surface produced. Treatment regions on proximal root surfaces of extracted human teeth were root planned to expose dentin. Cotton pellets soaked in either 30% CA or 24% EDTA solution were rubbed on the treatment region then processed for SEM using one of two desiccation techniques, that is, (a) critically point dried from liquid CO2 (control) or (b) air-dried from tetramethylsilane (experimental). Specimens were coated with gold/palladium and viewed/photographed with an SEM. Specimens of the control groups displayed tufted fibrils (CA > EDTA) with many dentin tubules being partially obscured by overhanging fibrils. Air-dried specimens of both treatment groups displayed a flat intact monolayer devoid of a matted meshwork of fibrous collagen. Discrete fibril "sprigs," emanating from the surface monolayer, were characteristic of the EDTA group only. The rubbing-application of EDTA on dentin produces a tufted fibril surface somewhat similar to that produced by CA. Air-drying desiccation of both resulted in marked distortion with fibril collapse/coalescence of the tufted collagen matrix.

The effects of scanning electron microscopy desiccation preparation on demineralized dentin surfaces.

This study assessed the effect various scanning electron microscopy (SEM) desiccation preparation techniques have on a tufted collagen surface produced using an acid-burnished (rubbed) demineralization application technique. Citric acid- soaked cotton pellets (30%) were burnished on the dentin treatment region, rinsed in water, and then fixed. Four SEM desiccation preparation techniques were employed: (1) air-dried from glutaraldehyde; (2) air-dried from ethanol; (3) critical point dried from liquid carbon dioxide (control); or (4) air-dried from tetramethylsilane. Control specimens all displayed a characteristic tufted fibril surface. In all experimental groups, fibrils collapsed, forming an intact, undulating nondescript surface monolayer. All air-drying SEM desiccation preparation procedures appear to cause artifactual distortion of a tufted dentin collagen surface.

The management of large mucogingival defects with an "epithelial embossed" connective tissue graft.

The subepithelial connective tissue graft (SECTG) is a favorite surgical technique for the treatment of mucogingival defects. However, complete root coverage of Miller Class I and II defects is often not achieved with this procedure, especially when the defects are deep or wide. The purpose of this report is to describe a surgical technique used to manage such mucogingival defects. The technique uses a uniquely obtained SECTG with "embossed epithelium" that is designed to fit the defect site. This is employed to prolong protection of the underlying healing process. In addition, this technique avoids flap advancement, thereby allowing for the development of a wider zone of attached gingiva at the treatment site.

"Burnishing" demineralization of root surfaces: ultrastructural surface characteristics.

PURPOSE: The purpose of this study was to compare the surface topography of roots treated with a resin bonding demineralizing agent using either a "placed" or "burnished" application technique. MATERIALS AND METHODS: Fifteen roots of human teeth were sectioned in half and a treatment area prepared on the coronal portion of each proximal section. This area was root planed to expose dentin. Treatment areas were demineralized with (1) a commercially available demineralizing agent (10% citric acid with 3% ferric chloride) (Amalgambond; Parkell) or (2) 30% citric acid solution. Cotton pellets saturated in either solution were placed or burnished (vigorously rubbed) on the treatment area for 3 min. Sections were prepared for SEM analysis using liquid CO2 dehydration. RESULTS: Areas of cementum and dentin were evident on most treatment areas. Specimens of both placed groups lacked a smear layer and exhibited a cracked-eroded, flat surface of matted or ridged fibrous material. Specimens in both burnished groups also lacked a smear layer, yet in stark contrast, exhibited an abundant array of deeply tufted fibril material similar to that of a "shag carpet". Two types of tufted fibril patterns were present: a lace-like array of shorter fibrils seen on dentin, and a voluminous mass of longer fibrils seen on cementum. CONCLUSION: Root cementum and dentin, treated with either demineralizing agent using the burnishing application technique, were ultrastructurally similar in that both displayed an abundant array of deeply tufted fibril material. This differed from the flat/matted fibril material seen using the placed application technique.