Batch effect exerts a bigger influence on the rat urinary metabolome and gut microbiota than uraemia: a cautionary tale

The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening

To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.

Impact of high-altitude acclimatization and de-acclimatization on the intestinal microbiota of rats in a natural high-altitude environment

Introduction

Intestinal microorganisms play an important role in the health of both humans and animals, with their composition being influenced by changes in the host's environment.

Methods

We evaluated the longitudinal changes in the fecal microbial community of rats at different altitudes across various time points. Rats were airlifted to high altitude (3,650 m) and acclimatized for 42 days (HAC), before being by airlifted back to low altitude (500 m) and de-acclimatized for 28 days (HADA); meanwhile, the control group included rats living at low altitude (500 m; LA). We investigated changes in the gut microbiota at 12 time points during high-altitude acclimatization and de-acclimatization, employing 16S rRNA gene sequencing technology alongside physiological indices, such as weight and daily autonomous activity time.

Results

A significant increase in the Chao1 index was observed on day 14 in the HAC and HADA groups compared to that in the LA group, indicating clear differences in species richness. Moreover, the principal coordinate analysis revealed that the bacterial community structures of HAC and HADA differed from those in LA. Long-term high-altitude acclimatization and de- acclimatization resulted in the reduced abundance of the probiotic Lactobacillus. Altitude and age significantly influenced intestinal microbiota composition, with changes in ambient oxygen content and atmospheric partial pressure being considered key causal factors of altitude-dependent alterations in microbiota composition. High-altitude may be linked to an increase in anaerobic bacterial abundance and a decrease in non-anaerobic bacterial abundance.

Discussion

In this study, the hypobaric hypoxic conditions at high-altitude increased the abundance of anaerobes, while reducing the abundance of probiotics; these changes in bacterial community structure may, ultimately, affect host health. Overall, gaining a comprehensive understanding of the intestinal microbiota alterations during high-altitude acclimatization and de-acclimatization is essential for the development of effective prevention and treatment strategies to better protect the health of individuals traveling between high- and low-altitude areas.

Promises and Limitations of Current Models for Understanding Barrett's Esophagus and Esophageal Adenocarcinoma

BACKGROUND & AIMS

This review was developed to provide a thorough and effective update on models relevant to esophageal metaplasia, dysplasia, and carcinogenesis, focusing on the advantages and limitations of different models of Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC).

METHODS

This expert review was written on the basis of a thorough review of the literature combined with expert interpretation of the state of the field. We emphasized advances over the years 2012-2023 and provided detailed information related to the characterization of established human esophageal cell lines.

RESULTS

New insights have been gained into the pathogenesis of BE and EAC using patient-derived samples and single-cell approaches. Relevant animal models include genetic as well as surgical mouse models and emphasize the development of lesions at the squamocolumnar junction in the mouse stomach. Rat models are generated using surgical approaches that directly connect the small intestine and esophagus. Large animal models have the advantage of including features in human esophagus such as esophageal submucosal glands. Alternatively, cell culture approaches remain important in the field and allow for personalized approaches, and scientific rigor can be ensured by authentication of cell lines.

CONCLUSIONS

Research in BE and EAC remains highly relevant given the morbidity and mortality associated with cancers of the tubular esophagus and gastroesophageal junction. Careful selection of models and inclusion of human samples whenever possible will ensure relevance to human health and disease.

Meta-analysis identifying gut microbial biomarkers of Qinghai-Tibet Plateau populations and the functionality of microbiota-derived butyrate in high-altitude adaptation

The extreme environmental conditions of a plateau seriously threaten human health. The relationship between gut microbiota and human health at high altitudes has been extensively investigated. However, no universal gut microbiota biomarkers have been identified in the plateau population, limiting research into gut microbiota and high-altitude adaptation. 668 16s rRNA samples were analyzed using meta-analysis to reduce batch effects and uncover microbiota biomarkers in the plateau population. Furthermore, the robustness of these biomarkers was validated. Mendelian randomization (MR) results indicated that Tibetan gut microbiota may mediate a reduced erythropoietic response. Functional analysis and qPCR revealed that butyrate may be a functional metabolite in high-altitude adaptation. A high-altitude rat model showed that butyrate reduced intestinal damage caused by high altitudes. According to cell experiments, butyrate may downregulate hypoxia-inducible factor-1α (HIF-1α) expression and blunt cellular responses to hypoxic stress. Our research found universally applicable biomarkers and investigated their potential roles in promoting human health at high altitudes.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

A prebiotic diet modulates the oral microbiome composition and results in the attenuation of oropharyngeal candidiasis in mice

Oral bacteria can influence the ability of Candida albicans to cause oropharyngeal candidiasis (OPC). We recently reported that a Lactobacillus johnsonii-enriched oral microbiota reduced C. albicans virulence in an immunosuppressed OPC mouse model. As a follow-up, in this work, we aimed to enrich the resident oral Lactobacillus communities with a prebiotic diet to further assess their effect on the severity of OPC. We tested the effect of a prebiotic xylo-oligosaccharides (XOS)-enriched diet in the oral global bacterial composition and severity of OPC. We assessed changes in the oral microbiome composition via 16S-rRNA gene high-throughput sequencing, validated by qPCR. The impact of the prebiotic diet on Candida infection was assessed by quantifying changes in oral fungal and bacterial biomass and scoring tongue lesions. Contrary to expectations, oral Lactobacillus communities were not enriched by the XOS-supplemented diet. Yet, XOS modulated the oral microbiome composition, increasing Bifidobacterium abundance and reducing enterococci and staphylococci. In the OPC model, the XOS diet attenuated Candida virulence and bacterial dysbiosis, increasing lactobacilli and reducing enterococci on the oral mucosa. We conclude that XOS attenuates Candida virulence by promoting a bacterial microbiome structure more resilient to Candida infection. IMPORTANCE This is the first study on the effects of a prebiotic diet on the oral mucosal bacterial microbiome and an oropharyngeal candidiasis (OPC) mouse model. We found that xylo-oligosaccharides change the oral bacterial community composition and attenuate OPC. Our results contribute to the understanding of the impact of the oral bacterial communities on Candida virulence.

A Refined, Controlled 16S rRNA Gene Sequencing Approach Reveals Limited Detection of Cerebrospinal Fluid Microbiota in Children with Bacterial Meningitis

Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. Additionally, these refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads, such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples with low bacterial loads by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. Several bench and computational approaches were taken to address potential sources of error for low bacterial load samples. We compared DNA yields and sequencing results after applying three different DNA extraction approaches to an artificially constructed mock-bacterial community. We also compared two postsequencing computational contaminant removal strategies, decontam R and full contaminant sequence removal. All three extraction techniques followed by decontam R yielded similar results for the mock community. We then applied these methods to 22 CSF samples from children diagnosed with meningitis, which has low bacterial loads relative to other clinical infection samples. The refined 16S HTS pipelines identified the cultured bacterial genus as the dominant organism for only 3 of these samples. We found that all three DNA extraction techniques followed by decontam R generated similar DNA yields for mock communities at the low bacterial loads representative of CSF samples. However, the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches. Although we did not find current DNA-based diagnostics to be useful for pediatric meningitis samples, the utility of these methods for CSF shunt infection remains undefined. Future advances in sample processing methods to minimize or eliminate contamination will be required to improve the sensitivity and specificity of these methods for pediatric meningitis. IMPORTANCE Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. These refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. We found that the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches.

Gut Dysbiosis in Experimental Kidney Disease: A Meta-Analysis of Rodent Repository Data

SIGNIFICANCE STATEMENT

Alterations in gut microbiota contribute to the pathophysiology of a diverse range of diseases, leading to suggestions that chronic uremia may cause intestinal dysbiosis that contributes to the pathophysiology of CKD. Various small, single-cohort rodent studies have supported this hypothesis. In this meta-analysis of publicly available repository data from studies of models of kidney disease in rodents, cohort variation far outweighed any effect of experimental kidney disease on the gut microbiota. No reproducible changes in animals with kidney disease were seen across all cohorts, although a few trends observed in most experiments may be attributable to kidney disease. The findings suggest that rodent studies do not provide evidence for the existence of "uremic dysbiosis" and that single-cohort studies are unsuitable for producing generalizable results in microbiome research.

BACKGROUND

Rodent studies have popularized the notion that uremia may induce pathological changes in the gut microbiota that contribute to kidney disease progression. Although single-cohort rodent studies have yielded insights into host-microbiota relationships in various disease processes, their relevance is limited by cohort and other effects. We previously reported finding metabolomic evidence that batch-to-batch variations in the microbiome of experimental animals are significant confounders in an experimental study.

METHODS

To attempt to identify common microbial signatures that transcend batch variability and that may be attributed to the effect of kidney disease, we downloaded all data describing the molecular characterization of the gut microbiota in rodents with and without experimental kidney disease from two online repositories comprising 127 rodents across ten experimental cohorts. We reanalyzed these data using the DADA2 and Phyloseq packages in R, a statistical computing and graphics system, and analyzed data both in a combined dataset of all samples and at the level of individual experimental cohorts.

RESULTS

Cohort effects accounted for 69% of total sample variance ( P <0.001), substantially outweighing the effect of kidney disease (1.9% of variance, P =0.026). We found no universal trends in microbial population dynamics in animals with kidney disease, but observed some differences (increased alpha diversity, a measure of within-sample bacterial diversity; relative decreases in Lachnospiraceae and Lactobacillus ; and increases in some Clostridia and opportunistic taxa) in many cohorts that might represent effects of kidney disease on the gut microbiota .

CONCLUSIONS

These findings suggest that current evidence that kidney disease causes reproducible patterns of dysbiosis is inadequate. We advocate meta-analysis of repository data as a way of identifying broad themes that transcend experimental variation.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.

Batch and sampling time exert a larger influence on the fungal community than gastrointestinal location in model animals: A meaningful case study

Fungi play a fundamental role in the intestinal ecosystem and health, but our knowledge of fungal composition and distribution in the whole gastrointestinal tract (GIT) is very limited. The physiological similarity between humans and pigs in terms of digestive and associated metabolic processes places, the pig in a superior position over other non-primate models. Here, we aimed to characterize the diversity and composition of fungi in the GIT of pigs. Using high-throughput sequencing, we evaluated the fungal community in different locations of GIT of 11 pigs with 128.41 ± 1.25 kg body weight acquired successively. Among them, five pigs are sacrificed in April 2019 (Batch 1) and the other six are sacrificed in January 2020 (Batch 2). All subjects with similar genetic backgrounds, housing, management, and diet. Finally, no significant difference is found in the α-diversity (Richness) of the fungal community among all intestinal segments. Basidiomycota and Ascomycota are the two predominant fungal phyla, but Batch 1 harbored a notably high abundance of Basidiomycota and Batch 2 harbored a high abundance of Ascomycota. Moreover, the two batches harbored completely different fungal compositions and core fungal genera. FUNGuild (Fungal Functional Guild) analysis revealed that most of the fungal species present in the GIT are saprotroph, plant pathogen, and animal endosymbiont. Our study is the first to report that even under the same condition, large variations in fungal composition in the host GIT still occur from batch-to-batch and sampling time. The implications of our observations serve as references to the development of better models of the human gut.

An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome

Intestinal microbiota plays a key role in shaping host homeostasis by regulating metabolism, immune responses and behavior. Its dysregulation has been associated with metabolic, immune and neuropsychiatric disorders and is accompanied by changes in bacterial metabolic regulation. Although proteomics is well suited for analysis of individual microbes, metaproteomics of fecal samples is challenging due to the physical structure of the sample, presence of contaminating host proteins and coexistence of hundreds of taxa. Furthermore, there is a lack of consensus regarding preparation of fecal samples, as well as downstream bioinformatic analyses following metaproteomics data acquisition. Here we assess sample preparation and data analysis strategies applied to mouse feces in a typical mass spectrometry-based metaproteomic experiment. We show that subtle changes in sample preparation protocols may influence interpretation of biological findings. Two-step database search strategies led to significant underestimation of false positive protein identifications. Unipept software provided the highest sensitivity and specificity in taxonomic annotation of the identified peptides of unknown origin. Comparison of matching metaproteome and metagenome data revealed a positive correlation between protein and gene abundances. Notably, nearly all functional categories of detected protein groups were differentially abundant in the metaproteome compared to what would be expected from the metagenome, highlighting the need to perform metaproteomics when studying complex microbiome samples.

The Gut Microbiome in Hypertension: Recent Advances and Future Perspectives

The pathogenesis of hypertension is known to involve a diverse range of contributing factors including genetic, environmental, hormonal, hemodynamic and inflammatory forces, to name a few. There is mounting evidence to suggest that the gut microbiome plays an important role in the development and pathogenesis of hypertension. The gastrointestinal tract, which houses the largest compartment of immune cells in the body, represents the intersection of the environment and the host. Accordingly, lifestyle factors shape and are modulated by the microbiome, modifying the risk for hypertensive disease. One well-studied example is the consumption of dietary fibers, which leads to the production of short-chain fatty acids and can contribute to the expansion of anti-inflammatory immune cells, consequently protecting against the progression of hypertension. Dietary interventions such as fasting have also been shown to impact hypertension via the microbiome. Studying the microbiome in hypertensive disease presents a variety of unique challenges to the use of traditional model systems. Integrating microbiome considerations into preclinical research is crucial, and novel strategies to account for reciprocal host-microbiome interactions, such as the wildling mouse model, may provide new opportunities for translation. The intricacies of the role of the microbiome in hypertensive disease is a matter of ongoing research, and there are several technical considerations which should be accounted for moving forward. In this review we provide insights into the host-microbiome interaction and summarize the evidence of its importance in the regulation of blood pressure. Additionally, we provide recommendations for ongoing and future research, such that important insights from the microbiome field at large can be readily integrated in the context of hypertension.

Batch effects account for the main findings of an in utero human intestinal bacterial colonization study

A recent study by Rackaityte et al. reported evidence for a low level of bacterial colonization, specifically of Micrococcus luteus, in the intestine of second trimester human fetuses. We have re-analyzed their sequence data and identified a batch effect which violates the underlying assumptions of the bioinformatic method used for contamination removal. This batch effect resulted in Micrococcus not being identified as a contaminant in the original work and being falsely assigned to the fetal samples. We further provide evidence that the micrographs presented by Rackaityte et al. are unlikely to show Micrococci or other bacteria as the size of the particles shown exceeds that of related bacterial cells. Finally, phylogenetic analysis showed that the microbes cultured from the fetal samples differed significantly from those detected by sequencing. Overall, our findings show that the presence of Micrococcus in the fetal gut is not supported by the primary sequence data. Our findings underline important aspects of the nature of contamination for both sequencing and culture approaches in microbiome studies and the appropriate use of automated contamination identification tools.

Investigating the cow skin and teat canal microbiomes of the bovine udder using different sampling and sequencing approaches

There is a need for standardized, efficient, and practical sampling methods to support large population-based studies of the internal and external epithelial microbiomes of the bovine udder. The primary objective of this study was to evaluate different sampling devices for the isolation of microbial DNA originating from the internal and external teat epithelium. Secondary objectives were to survey and compare the microbial diversity of external and teat canal epithelial microbiomes using amplicon and shotgun metagenomic sequencing approaches. To address these objectives, we enrolled a convenience sample of 24 Holstein dairy cows and collected samples from the external epithelium at the base of udder, the external teat barrel epithelium, the external teat apex epithelium, and the teat canal epithelium. Extracted DNA was quantified and subjected to PCR amplification of the V4 hypervariable region of the 16S rRNA gene and sequenced on the Illumina MiSeq platform (Illumina Inc., San Diego, CA). A subset of samples was subjected to a shallow shotgun metagenomic assay on the Illumina HiSeq platform. For samples collected from the external teat epithelium, we found that gauze squares consistently yielded more DNA than swabs, and Simpson's reciprocal index of diversity was higher for gauze than for swabs. The teat canal epithelial samples exhibited significantly lower diversity than the external sampling locations, but there were no significant differences in diversity between teat apex, teat barrel, and base of the udder samples. There were, however, differences in the microbial distribution and abundances of specific bacteria across external epithelial surfaces. The proportion of shotgun sequence reads classified as Bos taurus was highly variable between sampling locations, ranging from 0.33% in teat apex samples to 99.91% in teat canal samples. These results indicate that gauze squares should be considered for studying the microbiome of the external epithelium of the bovine udder, particularly if DNA yield must be maximized. Further, the relative proportion of host to non-host DNA present in samples collected from the internal and external teat epithelium should be considered when designing studies that utilize shotgun metagenomic sequencing.

Metagenomic Analysis of the Faecal Microbiome of Rats with 1, 2-Dimethylhydrazine Induced Colon Cancer and Prophylactic Whole-Cell Carotenoid Intervention

Rapidly evolving sequencing technologies have enabled efficient sequencing of complex genomes and metagenomes. Here, we have presented our metagenomic analysis of rat faeces isolated DNA, sequenced using long-read sequencing technology. The microbiome changes in the rat faeces after sixteen weeks of prolonged administration of subcutaneous 1,2 dimethylhydrazine to induce colon carcinogenesis and oral carotenoid-rich whole-cell lyophilised Dunaliella salina supplement. The faecal pellets were aseptically collected, and DNA was isolated and sequenced subsequently. The post-sequencing analysis revealed that the rat gut microbiome is highly complex and diverse. There was a significant difference between the microbiome of rats that received Dunaliella salina supplement in comparison with rats treated with 1,2 dimethylhydrazine and control rats. We observed the dominance of Bacteroidetes over Firmicutes in both cases of administration. The dominance was notably contributed by individuals like B. vulgatus, B. dorei, B. fragilis, P. ruminicola, and P. copri. The presence of protozoans like Trypanosoma, Trichomonas, and Leishmania was also identified among other commensal eukaryotes. Moreover, there was an abundant presence of bacteriophages targeting probiotic organisms like Lactobacillus among the identified DNA viruses.

Facility-dependent metabolic phenotype and gut bacterial composition in CD-1 mice from a single vendor: A brief report

Utilization of murine models remains a valuable tool in biomedical research, yet, disease phenotype of mice across studies can vary considerably. With advances in next generation sequencing, it is increasingly recognized that inconsistencies in host phenotype can be attributed, at least in part, to differences in gut bacterial composition. Research with inbred murine strains demonstrates that housing conditions play a significant role in variations of gut bacterial composition, however, few studies have assessed whether observed variation influences host phenotype in response to an intervention. Our study initially sought to examine the effects of a long-term (9-months) dietary intervention (i.e., diets with distinct fatty acid compositions) on the metabolic health, in particular glucose homeostasis, of genetically-outbred male and female CD-1 mice. Yet, mice were shipped from two different husbandry facilities of the same commercial vendor (Cohort A and B, respectively), and we observed throughout the study that diet, sex, and aging differentially influenced the metabolic phenotype of mice depending on their husbandry facility of origin. Examination of the colonic bacteria of mice revealed distinct bacterial compositions, including 23 differentially abundant genera and an enhanced alpha diversity in mice of Cohort B compared to Cohort A. We also observed that a distinct metabolic phenotype was linked with these differentially abundant bacteria and indices of alpha diversity. Our findings support that metabolic phenotypic variation of mice of the same strain but shipped from different husbandry facilities may be influenced by their colonic bacterial community structure. Our work is an important precautionary note for future research of metabolic diseases via mouse models, particularly those that seek to examine factors such diet, sex, and aging.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.