Partial order relation-based gene ontology embedding improves protein function prediction.

Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding. However, existing GO term embedding methods, which mainly take into account ancestral co-occurrence information, have yet to capture the full topological information in the GO-directed acyclic graph (DAG). In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is critical for diverse biological tasks including computational protein annotation.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Effects of vaginal microbiota transfer on the neurodevelopment and microbiome of cesarean-born infants: A blinded randomized controlled trial.

The microbiomes of cesarean-born infants differ from vaginally delivered infants and are associated with increased disease risks. Vaginal microbiota transfer (VMT) to newborns may reverse C-section-related microbiome disturbances. Here, we evaluated the effect of VMT by exposing newborns to maternal vaginal fluids and assessing neurodevelopment, as well as the fecal microbiota and metabolome. Sixty-eight cesarean-delivered infants were randomly assigned a VMT or saline gauze intervention immediately after delivery in a triple-blind manner (ChiCTR2000031326). Adverse events were not significantly different between the two groups. Infant neurodevelopment, as measured by the Ages and Stages Questionnaire (ASQ-3) score at 6 months, was significantly higher with VMT than saline. VMT significantly accelerated gut microbiota maturation and regulated levels of certain fecal metabolites and metabolic functions, including carbohydrate, energy, and amino acid metabolisms, within 42 days after birth. Overall, VMT is likely safe and may partially normalize neurodevelopment and the fecal microbiome in cesarean-delivered infants.

Meta-analysis reveals Helicobacter pylori mutual exclusivity and reproducible gastric microbiome alterations during gastric carcinoma progression.

Accumulating evidence shows that the gastric bacterial community may contribute to the development of gastric cancer (GC). However, the reported alterations of gastric microbiota were not always consistent among the literature. To assess reproducible signals in gastric microbiota during the progression of GC across studies, we performed a meta-analysis of nine publicly available 16S datasets with standard tools of the state-of-the-art. Despite study-specific batch effect, significant changes in the composition of the gastric microbiome were found during the progression of gastric carcinogenesis, especially when the Helicobacter pylori (HP) reads were removed from analyses to mitigate its compositional effect as they accounted for extremely large proportions of sequencing depths in many gastric samples. Differential microbes, including Fusobacterium, Leptotrichia, and several lactic acid bacteria such as Bifidobacterium, Lactobacillus, and Streptococcus anginosus, which were frequently and significantly enriched in GC patients compared with gastritis across studies, had good discriminatory capacity to distinguish GC samples from gastritis. Oral microbes were significantly enriched in GC compared to precancerous stages. Intriguingly, we observed mutual exclusivity of different HP species across studies. In addition, the comparison between gastric fluid and mucosal microbiome suggested their convergent dysbiosis during gastric disease progression. Taken together, our systematic analysis identified novel and consistent microbial patterns in gastric carcinogenesis.

Systematic Evaluation of the Viable Microbiome in the Human Oral and Gut Samples with Spike-in Gram+/- Bacteria.

PMA (propidium monoazide) is one of the few methods that are compatible with metagenomic sequencing to characterize the live/intact microbiota. However, its efficiency in complex communities such as saliva and feces is still controversial. An effective method for depleting host and dead bacterial DNA in human microbiome samples is lacking. Here, we systematically evaluate the efficiency of osmotic lysis and PMAxx treatment (lyPMAxx) in characterizing the viable microbiome with four live/dead Gram+/Gram- microbial strains in simple synthetic and spiked-in complex communities. We show that lyPMAxx-quantitative PCR (qPCR)/sequencing eliminated more than 95% of the host and heat-killed microbial DNA and had a much smaller effect on the live microbes in both simple mock and spiked-in complex communities. The overall microbial load and the alpha diversity of the salivary and fecal microbiome were decreased by lyPMAxx, and the relative abundances of the microbes were changed. The relative abundances of Actinobacteria, Fusobacteria, and Firmicutes in saliva were decreased by lyPMAxx, as was that of Firmicutes in feces. We also found that the frequently used sample storage method, freezing with glycerol, killed or injured 65% and 94% of the living microbial cells in saliva and feces, respectively, with the Proteobacteria phylum affected most in saliva and the Bacteroidetes and Firmicutes phyla affected most in feces. By comparing the absolute abundance variation of the shared species among different sample types and individuals, we found that sample habitat and personal differences affected the response of microbial species to lyPMAxx and freezing. IMPORTANCE The functions and phenotypes of microbial communities are largely defined by viable microbes. Through advanced nucleic acid sequencing technologies and downstream bioinformatic analyses, we gained an insight into the high-resolution microbial community composition of human saliva and feces, yet we know very little about whether such community DNA sequences represent viable microbes. PMA-qPCR was used to characterize the viable microbes in previous studies. However, its efficiency in complex communities such as saliva and feces is still controversial. By spiking-in four live/dead Gram+/Gram- bacterial strains, we demonstrate that lyPMAxx can effectively discriminate between live and dead microbes in the simple synthetic community and complex human microbial communities (saliva and feces). In addition, freezing storage was found to kill or injure the microbes in saliva and feces significantly, as measured with lyPMAxx-qPCR/sequencing. This method has a promising prospect in the viable/intact microbiota detection of complex human microbial communities.

Systematic evaluation of antimicrobial food preservatives on glucose metabolism and gut microbiota in healthy mice.

Certain antimicrobial preservatives (APs) have been shown to perturb gut microbiota. So far, it is not yet fully known that whether similar effects are observable for a more diverse set of APs. It also remains elusive if biogenic APs are superior to synthetic APs in terms of safety. To help fill these knowledge gaps, the effects of eleven commonly used synthetic and biogenic APs on the gut microbiota and glucose metabolism were evaluated in the wild-type healthy mice. Here, we found that APs induced glucose intolerance and perturbed gut microbiota, irrespective of their origin. In addition, biogenic APs are not always safer than synthetic ones. The biogenic AP nisin unexpectedly induced the most significant effects, which might be partially mediated by glucagon-like peptide 1 related glucoregulatory hormones secretion perturbation.

Diurnal and eating-associated microbial patterns revealed via high-frequency saliva sampling.

The oral microbiome is linked to oral and systemic health, but its fluctuation under frequent daily activities remains elusive. Here, we sampled saliva at 10- to 60-min intervals to track the high-resolution microbiome dynamics during the course of human activities. This dense time series data showed that eating activity markedly perturbed the salivary microbiota, with tongue-specific Campylobacter concisus and Oribacterium sinus and dental plaque-specific Lautropia mirabilis, Rothia aeria, and Neisseria oralis increased after every meal in a temporal order. The observation was reproducible in multiple subjects and across an 11-mo period. The microbiome composition showed significant diurnal oscillation patterns at different taxonomy levels with Prevotella/Alloprevotella increased at night and Bergeyella HMT 206/Haemophilus slowly increased during the daytime. We also identified microbial co-occurring patterns in saliva that are associated with the intricate biogeography of the oral microbiome. Microbial source tracking analysis showed that the contributions of distinct oral niches to the salivary microbiome were dynamically affected by daily activities, reflecting the role of saliva in exchanging microbes with other oral sites. Collectively, our study provides insights into the temporal microbiome variation in saliva and highlights the need to consider daily activities and diurnal factors in design of oral microbiome studies.

Microbial and Nonvolatile Chemical Diversities of Chinese Dark Teas Are Differed by Latitude and Pile Fermentation.

Understanding the microbial and chemical diversities, as well as what affects these diversities, is important for modern manufacturing of traditional fermented foods. In this work, Chinese dark teas (CDTs) that are traditional microbial fermented beverages with relatively high sample diversity were collected. Microbial DNA amplicon sequencing and mass spectrometry-based untargeted metabolomics show that the CDT microbial ß diversity, as well as the nonvolatile chemical α and ß diversities, is determined by the primary impact factors of geography and manufacturing procedures, in particular, latitude and pile fermentation after blending. A large number of metabolites sharing between CDTs and fungi were discovered by Feature-based Molecular Networking (FBMN) on the Global Natural Products Social Molecular Networking (GNPS) web platform. These molecules, such as prenylated cyclic dipeptides and B-vitamins, are functionally important for nutrition, biofunctions, and flavor. Molecular networking has revealed patterns in metabolite profiles on a chemical family level in addition to individual structures.

Green Banana Flour Contributes to Gut Microbiota Recovery and Improves Colonic Barrier Integrity in Mice Following Antibiotic Perturbation.

Green banana flour (GBF) is rich in resistant starch that has been used as a prebiotic to exert beneficial effects on gut microbiota. In this study, GBF was evaluated for its capacity to restore gut microbiota and intestinal barrier integrity from antibiotics (Abx) perturbation by comparing it to natural recovery (NR) treatment. C57B/L 6 J mice were exposed to 3 mg ciprofloxacin and 3.5 mg metronidazole once a day for 2 weeks to induce gut microbiota dysbiosis model. Then, GBF intervention at the dose of 400 mg/kg body weight was conducted for 2 weeks. The results showed that mice treated with Abx displayed increased gut permeability and intestinal barrier disruption, which were restored more quickly with GBF than NR treatment by increasing the secretion of mucin. Moreover, GBF treatment enriched beneficial Bacteroidales S24-7, Lachnospiraceae, Bacteroidaceae, and Porphyromonadaceae that accelerated the imbalanced gut microbiota restoration to its original state. This study puts forward novel insights into the application of GBF as a functional food ingredient to repair gut microbiota from Abx perturbation.

Alterations in gut microbiota and metabolites associated with altitude-induced cardiac hypertrophy in rats during hypobaric hypoxia challenge.

The gut microbiota is involved in host responses to high altitude. However, the dynamics of intestinal microecology and their association with altitude-related illness are poorly understood. Here, we used a rat model of hypobaric hypoxia challenge to mimic plateau exposure and monitored the gut microbiome, short-chain fatty acids (SCFAs), and bile acids (BAs) over 28 d. We identified weight loss, polycythemia, and pathological cardiac hypertrophy in hypoxic rats, accompanied by a large compositional shift in the gut microbiota, which is mainly driven by the bacterial families of Prevotellaceae, Porphyromonadaceae, and Streptococcaceae. The aberrant gut microbiota was characterized by increased abundance of the Parabacteroides, Alistipes, and Lactococcus genera and a larger Bacteroides to Prevotella ratio. Trans-omics analyses showed that the gut microbiome was significantly correlated with the metabolic abnormalities of SCFAs and BAs in feces, suggesting an interaction network remodeling of the microbiome-metabolome after the hypobaric hypoxia challenge. Interestingly, the transplantation of fecal microbiota significantly increased the diversity of the gut microbiota, partially inhibited the increased abundance of the Bacteroides and Alistipes genera, restored the decrease of plasma propionate, and moderately ameliorated cardiac hypertrophy in hypoxic rats. Our results provide an insight into the longitudinal changes in intestinal microecology during the hypobaric hypoxia challenge. Abnormalities in the gut microbiota and microbial metabolites contribute to the development of high-altitude heart disease in rats.

Gut Microbiome-Targeted Modulations Regulate Metabolic Profiles and Alleviate Altitude-Related Cardiac Hypertrophy in Rats.

It is well known that humans physiologically or pathologically respond to high altitude, with these responses accompanied by alterations in the gut microbiome. To investigate whether gut microbiota modulation can alleviate high-altitude-related diseases, we administered probiotics, prebiotics, and synbiotics in rat model with altitude-related cardiac impairment after hypobaric hypoxia challenge and observed that all three treatments alleviated cardiac hypertrophy as measured by heart weight-to-body weight ratio and gene expression levels of biomarkers in heart tissue. The disruption of gut microbiota induced by hypobaric hypoxia was also ameliorated, especially for microbes of Ruminococcaceae and Lachnospiraceae families. Metabolome revealed that hypobaric hypoxia significantly altered the plasma short-chain fatty acids (SCFAs), bile acids (BAs), amino acids, neurotransmitters, and free fatty acids, but not the overall fecal SCFAs and BAs. The treatments were able to restore homeostasis of plasma amino acids and neurotransmitters to a certain degree, but not for the other measured metabolites. This study paves the way to further investigate the underlying mechanisms of gut microbiome in high-altitude related diseases and opens opportunity to target gut microbiome for therapeutic purpose. IMPORTANCE Evidence suggests that gut microbiome changes upon hypobaric hypoxia exposure; however, it remains elusive whether this microbiome change is a merely derivational reflection of host physiological alteration, or it synergizes to exacerbate high-altitude diseases. We intervened gut microbiome in the rat model of prolonged hypobaric hypoxia challenge and found that the intervention could alleviate the symptoms of pathological cardiac hypertrophy, gut microbial dysbiosis, and metabolic disruptions of certain metabolites in gut and plasma induced by hypobaric hypoxia. Our study suggests that gut microbiome may be a causative factor for high-altitude-related pathogenesis and a target for therapeutic intervention.

Gut microbiota from green tea polyphenol-dosed mice improves intestinal epithelial homeostasis and ameliorates experimental colitis.

BACKGROUND: Alteration of the gut microbiota may contribute to the development of inflammatory bowel disease (IBD). Epigallocatechin-3-gallate (EGCG), a major bioactive constituent of green tea, is known to be beneficial in IBD alleviation. However, it is unclear whether the gut microbiota exerts an effect when EGCG attenuates IBD. RESULTS: We first explored the effect of oral or rectal EGCG delivery on the DSS-induced murine colitis. Our results revealed that anti-inflammatory effect and colonic barrier integrity were enhanced by oral, but not rectal, EGCG. We observed a distinct EGCG-mediated alteration in the gut microbiome by increasing Akkermansia abundance and butyrate production. Next, we demonstrated that the EGCG pre-supplementation induced similar beneficial outcomes to oral EGCG administration. Prophylactic EGCG attenuated colitis and significantly enriched short-chain fatty acids (SCFAs)-producing bacteria such as Akkermansia and SCFAs production in DSS-induced mice. To validate these discoveries, we performed fecal microbiota transplantation (FMT) and sterile fecal filtrate (SFF) to inoculate DSS-treated mice. Microbiota from EGCG-dosed mice alleviated the colitis over microbiota from control mice and SFF shown by superiorly anti-inflammatory effect and colonic barrier integrity, and also enriched bacteria such as Akkermansia and SCFAs. Collectively, the attenuation of colitis by oral EGCG suggests an intimate involvement of SCFAs-producing bacteria Akkermansia, and SCFAs, which was further demonstrated by prophylaxis and FMT. CONCLUSIONS: This study provides the first data indicating that oral EGCG ameliorated the colonic inflammation in a gut microbiota-dependent manner. Our findings provide novel insights into EGCG-mediated remission of IBD and EGCG as a potential modulator for gut microbiota to prevent and treat IBD. Video Abstract.

A Pilot Study of Microbial Succession in Human Rib Skeletal Remains during Terrestrial Decomposition.

The bones of decomposing vertebrates are colonized by a succession of diverse microbial communities. If this succession is similar across individuals, microbes may provide clues about the postmortem interval (PMI) during forensic investigations in which human skeletal remains are discovered. Here, we characterize the human bone microbial decomposer community to determine whether microbial succession is a marker for PMI. Six human donor subjects were placed outdoors to decompose on the soil surface at the Southeast Texas Applied Forensic Science facility. To also assess the effect of seasons, three decedents were placed each in the spring and summer. Once ribs were exposed through natural decomposition, a rib was collected from each body for eight time points at 3 weeks apart. We discovered a core bone decomposer microbiome dominated by taxa in the phylum Proteobacteria and evidence that these bone-invading microbes are likely sourced from the surrounding decomposition environment, including skin of the cadaver and soils. Additionally, we found significant overall differences in bone microbial community composition between seasons. Finally, we used the microbial community data to develop random forest models that predict PMI with an accuracy of approximately ±34 days over a 1- to 9-month time frame of decomposition. Typically, anthropologists provide PMI estimates based on qualitative information, giving PMI errors ranging from several months to years. Previous work has focused on only the characterization of the bone microbiome decomposer community, and this is the first known data-driven, quantitative PMI estimate of terrestrially decomposed human skeletal remains using microbial abundance information. IMPORTANCE Microbes are known to facilitate vertebrate decomposition, and they can do so in a repeatable, predictable manner. The succession of microbes in the skin and associated soil can be used to predict time since death during the first few weeks of decomposition. However, when remains are discovered after months or years, often the only evidence are skeletal remains. To determine if microbial succession in bone would be useful for estimating time since death after several months, human subjects were placed to decompose in the spring and summer seasons. Ribs were collected after 1 to 9 months of decomposition, and the bone microbial communities were characterized. Analysis revealed a core bone decomposer microbial community with some differences in microbial assembly occurring between seasons. These data provided time since death estimates of approximately ±34 days over 9 months. This may provide forensic investigators with a tool for estimating time since death of skeletal remains, for which there are few current methods.

PM2RA: A Framework for Detecting and Quantifying Relationship Alterations in Microbial Community.

The dysbiosis of gut microbiota is associated with the pathogenesis of human diseases. However, observing shifts in the microbe abundance cannot fully reveal underlying perturbations. Examining the relationship alterations (RAs) in the microbiome between health and disease statuses provides additional hints about the pathogenesis of human diseases, but no methods were designed to detect and quantify the RAs between different conditions directly. Here, we present profile monitoring for microbial relationship alteration (PM2RA), an analysis framework to identify and quantify the microbial RAs. The performance of PM2RA was evaluated with synthetic data, and it showed higher specificity and sensitivity than the co-occurrence-based methods. Analyses of real microbial datasets showed that PM2RA was robust for quantifying microbial RAs across different datasets in several diseases. By applying PM2RA, we identified several novel or previously reported microbes implicated in multiple diseases. PM2RA is now implemented as a web-based application available at http://www.pm2ra-xingyinliulab.cn/.

SEQEL: a tool for biological sequence manipulation in Emacs.

Summary: Sequences are arguably the most common biological data. An easy-to-use tool can greatly facilitate daily manipulation and analysis of biological sequences. Here, we present SEQEL, a tool providing a convenient environment for editing, formatting and rendering of DNA, RNA and protein sequences. This is accomplished by extending the commonly used text editor, Emacs, which is available for Windows, Linux and Mac OS. Availability and Implementation: The unit tested ELISP source code for seqel is freely available from https://github.com/rnaer/seqel along with documentation. Contact: zhenjiang.xu@gmail.com.

Vitamin D metabolites and the gut microbiome in older men.

The vitamin D receptor is highly expressed in the gastrointestinal tract where it transacts gene expression. With current limited understanding of the interactions between the gut microbiome and vitamin D, we conduct a cross-sectional analysis of 567 older men quantifying serum vitamin D metabolites using LC-MSMS and defining stool sub-Operational Taxonomic Units from16S ribosomal RNA gene sequencing data. Faith's Phylogenetic Diversity and non-redundant covariate analyses reveal that the serum 1,25(OH)2D level explains 5% of variance in α-diversity. In ß-diversity analyses using unweighted UniFrac, 1,25(OH)2D is the strongest factor assessed, explaining 2% of variance. Random forest analyses identify 12 taxa, 11 in the phylum Firmicutes, eight of which are positively associated with either 1,25(OH)2D and/or the hormone-to-prohormone [1,25(OH)2D/25(OH)D] "activation ratio." Men with higher levels of 1,25(OH)2D and higher activation ratios, but not 25(OH)D itself, are more likely to possess butyrate producing bacteria that are associated with better gut microbial health.

Alteration in gut microbiota is associated with dysregulation of cytokines and glucocorticoid therapy in systemic lupus erythematosus.

A growing corpus of evidence implicates the involvement of the commensal microbiota and immune cytokines in the initiation and progression of systemic lupus erythematosus (SLE). Glucocorticoids have been widely used in the treatment of SLE patients, however, glucocorticoid treatment carries a higher risk of other diseases. Using the 16S rRNA technique, we investigated the differences between the gut microbiota associated with the immune cytokines of SLE and relevant glucocorticoid treatment in a female cohort of 20 healthy control subjects (HC), 17 subjects with SLE (SLE-G), and 20 SLE patients having undergone glucocorticoid treatment (SLE+G). We observed that the diversity and structure of the microbial community in SLE+G patients were significantly changed compared to that of SLE-G patients, whereas the gut microbial community of the SLE+G group showed a similarity with the HC group, which implicate that the shift in the gut microbiome could represent a return to homeostasis. Furthermore, the up-regulations of immune cytokines in SLE-G were identified as closely related to gut dysbiosis, which indicates that the overrepresented genera in SLE patients may play roles in regulating expression level of these immune cytokines. This associated analysis of gut microbiota, glucocorticoid therapy, and immune factors might provide novel and insightful clues revealing the pathogenesis of SLE patients.

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

Intermittent Hypoxia and Hypercapnia Reproducibly Change the Gut Microbiome and Metabolome across Rodent Model Systems.

Studying perturbations in the gut ecosystem using animal models of disease continues to provide valuable insights into the role of the microbiome in various pathological conditions. However, understanding whether these changes are consistent across animal models of different genetic backgrounds, and hence potentially translatable to human populations, remains a major unmet challenge in the field. Nonetheless, in relatively limited cases have the same interventions been studied in two animal models in the same laboratory. Moreover, such studies typically examine a single data layer and time point. Here, we show the power of utilizing time series microbiome (16S rRNA amplicon profiling) and metabolome (untargeted liquid chromatography-tandem mass spectrometry [LC-MS/MS]) data to relate two different mouse models of atherosclerosis-ApoE-/- (n = 24) and Ldlr-/- (n = 16)-that are exposed to intermittent hypoxia and hypercapnia (IHH) longitudinally (for 10 and 6 weeks, respectively) to model chronic obstructive sleep apnea. Using random forest classifiers trained on each data layer, we show excellent accuracy in predicting IHH exposure within ApoE-/- and Ldlr-/- knockout models and in cross-applying predictive features found in one animal model to the other. The key microbes and metabolites that reproducibly predicted IHH exposure included bacterial species from the families Mogibacteriaceae, Clostridiaceae, bile acids, and fatty acids, providing a refined set of biomarkers associated with IHH. The results highlight that time series multiomics data can be used to relate different animal models of disease using supervised machine learning techniques and can provide a pathway toward identifying robust microbiome and metabolome features that underpin translation from animal models to human disease. IMPORTANCE Reproducibility of microbiome research is a major topic of contemporary interest. Although it is often possible to distinguish individuals with specific diseases within a study, the differences are often inconsistent across cohorts, often due to systematic variation in analytical conditions. Here we study the same intervention in two different mouse models of cardiovascular disease (atherosclerosis) by profiling the microbiome and metabolome in stool specimens over time. We demonstrate that shared microbial and metabolic changes are involved in both models with the intervention. We then introduce a pipeline for finding similar results in other studies. This work will help find common features identified across different model systems that are most likely to apply in humans.