Microbiome analyses can be challenging because microbial strains are numerous, and often, confounding factors in the data set are also numerous. Many tools reduce, summarize, and visualize these ...high-dimensional data to provide insight at the community level. However, they lose the detailed information about each taxon and can be misleading (for example, the well-known horseshoe effect in ordination plots). Thus, multiple methods at different levels of resolution are required to capture the full range of microbial patterns. Here we present Calour, a user-friendly data exploration tool for microbiome analyses. Calour provides a study-centric data model to store and manipulate sample-by-feature tables (with features typically being operational taxonomic units) and their associated metadata. It generates an interactive heatmap, allowing visualization of microbial patterns and exploration using microbial knowledge databases. We demonstrate the use of Calour by exploring publicly available data sets, including the gut and skin microbiota of habitat-switched fire salamander larvae, gut microbiota of Trichuris muris-infected mice, skin microbiota of different human body sites, gut microbiota of various ant species, and a metabolome study of mice exposed to intermittent hypoxia and hypercapnia. In these cases, Calour reveals novel patterns and potential contaminants of subgroups of microbes that are otherwise hard to find. Calour is open source under the Berkeley Software Distribution (BSD) license and available from https://github.com/biocore/calour.
Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.
Abstract
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.
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
(
= 24) and Ldlr
(
= 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
,
, 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.
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.
The majority of seafood is farmed, with most finfish coming from freshwater ponds. Ponds are often fertilized to promote microbial productivity as a natural feed source to fish. To understand if pond ...fertilization with livestock manure induces a probiotic or prebiotic effect, we communally reared tilapia (Oreochromis shiranus), and North African catfish (Clarias gariepinus), for 4 weeks under seven manure treatments including layer chicken, broiler chicken, guinea fowl, quail, pig, cow, vs. commercial feed to evaluate microbial community dynamics of the manure, pond water, and fish feces using 16S and 18S rRNA marker genes along with metagenome sequencing. Catfish growth, but not tilapia, was positively associated with plankton abundance (p = 0.0006, R2 = 0.4887) and greatest in ponds fertilized with quail manure (ANOVA, p < 0.05). Manure was unique and influenced the 16S microbiome in pond water, tilapia gut, and catfish gut and 18S community in pond water and catfish guts (PERMANOVA, p = 0.001). On average, 18.5%, 18.6%, and 45.3% of manure bacteria sOTUs, (sub‐operational taxonomic units), were present in the water column, catfish feces, and tilapia feces which comprised 3.7%, 12.8%, and 10.9% of the total microbial richness of the communities, respectively. Antibiotic resistance genes were highest in the manure and water samples followed by tilapia feces and lowest in catfish feces (p < 0.0001). In this study, we demonstrate how the bacterial and eukaryotic microbial composition of fish ponds are influenced by specific livestock manure inputs and that the gut microbiome of tilapia is more sensitive and responsive than catfish to these changes. We conclude that animal manure used as fertilizer induces a primarily prebiotic effect on the pond ecosystem rather than a direct probiotic effect on fish.
This multiomic study aimed to understand how manure based fertilization of fish ponds influences the microbial ecology of the aquaculture system along with fish performance. We demonstrate for the first time to the authors knowledge, how livestock manure fertilizer induces a primarily prebiotic effect and potentially smaller probiotic effect on the microbial ecology of fish ponds. We also demonstrate how these effects are specific to the fish species used in aquaculture.
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
and
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.
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.
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
, and
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.
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
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.
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.
Microbial strains of variable functional capacities coexist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their ...gene contents from metagenomic data. Here we present Strain‐level Pangenome Decomposition Analysis (StrainPanDA), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. We systematically validate the accuracy and robustness of StrainPanDA using synthetic data sets. To demonstrate the power of gene‐centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain‐specific functions (e.g., nutrient utilization and pathogenicity). Finally, StrainPanDA has minimal requirements for computing resources and can be scaled to process multiple species in a community in parallel. In short, StrainPanDA can be applied to metagenomic data sets to detect the association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.
Strain‐level Pangenome Decomposition Analysis (StrainPanDA) uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. StrainPanDA allows accurate and robust inference of strain composition and gene content profiles on synthetic data sets. Linked reconstruction of strain composition and gene content profiles provided by StrainPanDA furthers our understanding of the relationship between microbial adaptation and strain‐specific functions (e.g., nutrient utilization and pathogenicity).
Highlights
Strain‐level Pangenome Decomposition Analysis (StrainPanDA) uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities.
StrainPanDA allows accurate and robust inference of strain composition and gene content profiles on synthetic data sets.
Linked reconstruction of strain composition and gene content profiles provided by StrainPanDA furthers our understanding of the relationship between microbial adaptation and strain‐specific functions (e.g., nutrient utilization and pathogenicity).
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.
The unit tested ELISP source code for seqel is freely available from https://github.com/rnaer/seqel along with documentation.
zhenjiang.xu@gmail.com.
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
and
and dental plaque-specific
,
, and
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
/
increased at night and
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.
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