Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

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.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Effects of a ketogenic and low-fat diet on the human metabolome, microbiome, and foodome in adults at risk for Alzheimer's disease

INTRODUCTION

The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention.

METHODS

Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals' microbiome and metabolome throughout the dietary interventions.

RESULTS

Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility.

DISCUSSION

Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.

Whole-body modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Oral mucosal breaks trigger anti-citrullinated bacterial and human protein antibody responses in rheumatoid arthritis

Periodontal disease is more common in individuals with rheumatoid arthritis (RA) who have detectable anti-citrullinated protein antibodies (ACPAs), implicating oral mucosal inflammation in RA pathogenesis. Here, we performed paired analysis of human and bacterial transcriptomics in longitudinal blood samples from RA patients. We found that patients with RA and periodontal disease experienced repeated oral bacteremias associated with transcriptional signatures of ISG15+HLADRhi and CD48highS100A2pos monocytes, recently identified in inflamed RA synovia and blood of those with RA flares. The oral bacteria observed transiently in blood were broadly citrullinated in the mouth, and their in situ citrullinated epitopes were targeted by extensively somatically hypermutated ACPAs encoded by RA blood plasmablasts. Together, these results suggest that (i) periodontal disease results in repeated breaches of the oral mucosa that release citrullinated oral bacteria into circulation, which (ii) activate inflammatory monocyte subsets that are observed in inflamed RA synovia and blood of RA patients with flares and (iii) activate ACPA B cells, thereby promoting affinity maturation and epitope spreading to citrullinated human antigens.

Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades

BACKGROUND

Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention.

RESULTS

Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality.

CONCLUSIONS

This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.

Insulin-regulated serine and lipid metabolism drive peripheral neuropathy

Diabetes represents a spectrum of disease in which metabolic dysfunction damages multiple organ systems including liver, kidneys and peripheral nerves1,2. Although the onset and progression of these co-morbidities are linked with insulin resistance, hyperglycaemia and dyslipidaemia3-7, aberrant non-essential amino acid (NEAA) metabolism also contributes to the pathogenesis of diabetes8-10. Serine and glycine are closely related NEAAs whose levels are consistently reduced in patients with metabolic syndrome10-14, but the mechanistic drivers and downstream consequences of this metabotype remain unclear. Low systemic serine and glycine are also emerging as a hallmark of macular and peripheral nerve disorders, correlating with impaired visual acuity and peripheral neuropathy15,16. Here we demonstrate that aberrant serine homeostasis drives serine and glycine deficiencies in diabetic mice, which can be diagnosed with a serine tolerance test that quantifies serine uptake and disposal. Mimicking these metabolic alterations in young mice by dietary serine or glycine restriction together with high fat intake markedly accelerates the onset of small fibre neuropathy while reducing adiposity. Normalization of serine by dietary supplementation and mitigation of dyslipidaemia with myriocin both alleviate neuropathy in diabetic mice, linking serine-associated peripheral neuropathy to sphingolipid metabolism. These findings identify systemic serine deficiency and dyslipidaemia as novel risk factors for peripheral neuropathy that may be exploited therapeutically.

Enhancing untargeted metabolomics using metadata-based source annotation

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes.

Acetate reprograms gut microbiota during alcohol consumption

Liver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease.

Diet and feeding pattern modulate diurnal dynamics of the ileal microbiome and transcriptome

Compositional oscillations of the gut microbiome are essential for normal peripheral circadian rhythms, both of which are disrupted in diet-induced obesity (DIO). Although time-restricted feeding (TRF) maintains circadian synchrony and protects against DIO, its impact on the dynamics of the cecal gut microbiome is modest. Thus, other regions of the gut, particularly the ileum, the nexus for incretin and bile acid signaling, may play an important role in entraining peripheral circadian rhythms. We demonstrate the effect of diet and feeding rhythms on the ileal microbiome composition and transcriptome in mice. The dynamic rhythms of ileal microbiome composition and transcriptome are dampened in DIO. TRF partially restores diurnal rhythms of the ileal microbiome and transcriptome, increases GLP-1 release, and alters the ileal bile acid pool and farnesoid X receptor (FXR) signaling, which could explain how TRF exerts its metabolic benefits. Finally, we provide a web resource for exploration of ileal microbiome and transcriptome circadian data.

Abstract 3054: Pan-cancer characterization of the tumor mycobiome and its clinical effects

While the study of the tumor microbiome and its effects on cancer biology has expanded considerably over the last few years, most of this research focused on bacteria and viruses, leaving behind the fungal kingdom. Recently, a few studies have demonstrated that specific fungi may promote tumor progression, stressing the importance of comprehensively studying the tumor mycobiome and its effects. To address this, we have characterized the mycobiome in 1,183 human tumors and their adjacent tissues, originating from eight major solid tumor types. Staining and imaging demonstrated the presence of fungi in both cancer and immune cells, with tumor-type specific distribution patterns. Quantitative PCR of the fungal 5.8s rDNA revealed the presence of fungal DNA in all tumor types. To characterize the tumor mycobiome and address potential contamination during tissue handling and processing, we subjected all samples, as well as 295 negative controls of different types, to sequencing of the ITS2 region that is situated between fungal rRNA genes. We found cancer-type specific mycobial signatures with relatively high similarity between tumors and their adjacent tissues. While the fungal mycobiome had a lower species richness as compared to the bacterial microbiome of the same tumors, fungi showed significant co-occurrences with specific bacteria, suggesting the existence of ecological niches within the tumors. We also found significant correlations with clinical parameters such as patient’s age, tumor stage, progression-free survival, overall survival, and response to immune checkpoint blockade therapy. Characterization of the tumor mycobiome may add a biologically relevant, previously overlooked, component to be considered in the study of cancer, including its effects on tumor initiation, progression, diagnosis, and response to therapy.

Citation Format: Lian Narunsky Haziza, Gregory D. Sepich-Poore, Ilana Livyatan, Omer Asraf, Cameron Martino, Deborah Nejman, Nancy Gavert, Jason E. Stajich, Guy Amit, Antonio González, Stephen Wandro, Gili Perry, Ruthie Ariel, Arnon Meltser, Justin P. Shaffer, Qiyun Zhu, Nora Balint-Lahat, Iris Barshack, Maya Dadian, Einav N. Gal-Yam, Sandip P. Pate, Amir Bashan, Austin D. Swafford, Yitzhak Pilpel, Rob Knight, Ravid Straussman. Pan-cancer characterization of the tumor mycobiome and its clinical effects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3054.

A comparison of six DNA extraction protocols for 16S, ITS and shotgun metagenomic sequencing of microbial communities

Microbial communities contain a broad phylogenetic diversity of organisms; however, the majority of methods center on describing bacteria and archaea. Fungi are important symbionts in many ecosystems and are potentially important members of the human microbiome, beyond those that can cause disease. To expand our analysis of microbial communities to include data from the fungal internal transcribed spacer (ITS) region, five candidate DNA extraction kits were compared against our standardized protocol for describing bacteria and archaea using 16S rRNA gene amplicon- and shotgun metagenomics sequencing. The results are presented considering a diverse panel of host-associated and environmental sample types and comparing the cost, processing time, well-to-well contamination, DNA yield, limit of detection and microbial community composition among protocols. Across all criteria, the MagMAX Microbiome kit was found to perform best. The PowerSoil Pro kit performed comparably but with increased cost per sample and overall processing time. The Zymo MagBead, NucleoMag Food and Norgen Stool kits were included.

Early microbial markers of periodontal and cardiometabolic diseases in ORIGINS

Periodontitis affects up to 50% of individuals worldwide, and 8.5% are diagnosed with diabetes. The high-comorbidity rate of these diseases may suggest, at least in part, a shared etiology and pathophysiology. Changes in oral microbial communities have been documented in the context of severe periodontitis and diabetes, both independently and together. However, much less is known about the early oral microbial markers of these diseases. We used a subset of the ORIGINS project dataset, which collected detailed periodontal and cardiometabolic information from 787 healthy individuals, to identify early microbial markers of periodontitis and its association with markers of cardiometabolic health. Using state-of-the-art compositional data analysis tools, we identified the log-ratio of Treponema to Corynebacterium bacteria to be a novel Microbial Indicator of Periodontitis (MIP), and found that this MIP correlates with poor periodontal health and cardiometabolic markers early in disease pathogenesis in both subgingival plaque and saliva.

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data

Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses.

Clean room microbiome complexity impacts planetary protection bioburden

BACKGROUND

The Spacecraft Assembly Facility (SAF) at the NASA's Jet Propulsion Laboratory is the primary cleanroom facility used in the construction of some of the planetary protection (PP)-sensitive missions developed by NASA, including the Mars 2020 Perseverance Rover that launched in July 2020. SAF floor samples (n=98) were collected, over a 6-month period in 2016 prior to the construction of the Mars rover subsystems, to better understand the temporal and spatial distribution of bacterial populations (total, viable, cultivable, and spore) in this unique cleanroom.

RESULTS

Cleanroom samples were examined for total (living and dead) and viable (living only) microbial populations using molecular approaches and cultured isolates employing the traditional NASA standard spore assay (NSA), which predominantly isolated spores. The 130 NSA isolates were represented by 16 bacterial genera, of which 97% were identified as spore-formers via Sanger sequencing. The most spatially abundant isolate was Bacillus subtilis, and the most temporally abundant spore-former was Virgibacillus panthothenticus. The 16S rRNA gene-targeted amplicon sequencing detected 51 additional genera not found in the NSA method. The amplicon sequencing of the samples treated with propidium monoazide (PMA), which would differentiate between viable and dead organisms, revealed a total of 54 genera: 46 viable non-spore forming genera and 8 viable spore forming genera in these samples. The microbial diversity generated by the amplicon sequencing corresponded to ~86% non-spore-formers and ~14% spore-formers. The most common spatially distributed genera were Sphinigobium, Geobacillus, and Bacillus whereas temporally distributed common genera were Acinetobacter, Geobacilllus, and Bacillus. Single-cell genomics detected 6 genera in the sample analyzed, with the most prominent being Acinetobacter.

CONCLUSION

This study clearly established that detecting spores via NSA does not provide a complete assessment for the cleanliness of spacecraft-associated environments since it failed to detect several PP-relevant genera that were only recovered via molecular methods. This highlights the importance of a methodological paradigm shift to appropriately monitor bioburden in cleanrooms for not only the aeronautical industry but also for pharmaceutical, medical industries, etc., and the need to employ molecular sequencing to complement traditional culture-based assays. Video abstract.

Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data

Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial differences (beta diversity), followed by principal-coordinate analysis (PCoA). Uniform Manifold Approximation and Projection (UMAP) is an alternative method that can reduce the dimensionality of beta diversity distance matrices. Here, we demonstrate the benefits and limitations of using UMAP for dimensionality reduction on microbiome data. Using real data, we demonstrate that UMAP can improve the representation of clusters, especially when the clusters are composed of multiple subgroups. Additionally, we show that UMAP provides improved correlation of biological variation along a gradient with a reduced number of coordinates of the resulting embedding. Finally, we provide parameter recommendations that emphasize the preservation of global geometry. We therefore conclude that UMAP should be routinely used as a complementary visualization method for microbiome beta diversity studies. IMPORTANCE UMAP provides an additional method to visualize microbiome data. The method is extensible to any beta diversity metric used with PCoA, and our results demonstrate that UMAP can indeed improve visualization quality and correspondence with biological and technical variables of interest. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/knightlab-analyses/umap-microbiome-benchmarking; additionally, we have provided a QIIME 2 plugin for UMAP at https://github.com/biocore/q2-umap.

QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org. © 2020 The Authors.

Basic Protocol: Using QIIME 2 with microbiome data

Support Protocol: Further microbiome analyses

Intermittent Hypoxia and Hypercapnia Alter Diurnal Rhythms of Luminal Gut Microbiome and Metabolome

Obstructive sleep apnea (OSA), characterized by intermittent hypoxia and hypercapnia (IHC), affects the composition of the gut microbiome and metabolome. The gut microbiome has diurnal oscillations that play a crucial role in regulating circadian and overall metabolic homeostasis. Thus, we hypothesized that IHC adversely alters the gut luminal dynamics of key microbial families and metabolites. The objective of this study was to determine the diurnal dynamics of the fecal microbiome and metabolome of Apoe-/- mice after a week of IHC exposure. Individually housed, 10-week-old Apoe-/- mice on an atherogenic diet were split into two groups. One group was exposed to daily IHC conditions for 10 h (Zeitgeber time 2 [ZT2] to ZT12), while the other was maintained in room air. Six days after the initiation of the IHC conditions, fecal samples were collected every 4 h for 24 h (6 time points). We performed 16S rRNA gene amplicon sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS) to assess changes in the microbiome and metabolome. IHC induced global changes in the cyclical dynamics of the gut microbiome and metabolome. Ruminococcaceae, Lachnospiraceae, S24-7, and Verrucomicrobiaceae had the greatest shifts in their diurnal oscillations. In the metabolome, bile acids, glycerolipids (phosphocholines and phosphoethanolamines), and acylcarnitines were greatly affected. Multi-omic analysis of these results demonstrated that Ruminococcaceae and tauro-β-muricholic acid (TβMCA) cooccur and are associated with IHC conditions and that Coriobacteriaceae and chenodeoxycholic acid (CDCA) cooccur and are associated with control conditions. IHC significantly change the diurnal dynamics of the fecal microbiome and metabolome, increasing members and metabolites that are proinflammatory and proatherogenic while decreasing protective ones. IMPORTANCE People with obstructive sleep apnea are at a higher risk of high blood pressure, type 2 diabetes, cardiac arrhythmias, stroke, and sudden cardiac death. We wanted to understand whether the gut microbiome changes induced by obstructive sleep apnea could potentially explain some of these medical problems. By collecting stool from a mouse model of this disease at multiple time points during the day, we studied how obstructive sleep apnea changed the day-night patterns of microbes and metabolites of the gut. Since the oscillations of the gut microbiome play a crucial role in regulating metabolism, changes in these oscillations can explain why these patients can develop so many metabolic problems. We found changes in microbial families and metabolites that regulate many metabolic pathways contributing to the increased risk for heart disease seen in patients with obstructive sleep apnea.

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment

BACKGROUND

SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.

METHODS

We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.

RESULTS

Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.

CONCLUSIONS

These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

Effects of dietary microbial feed supplement on production efficacy in lactating dairy cows

This study evaluated the effects of a microbial feed supplement (MFS; Galaxis, Ascus Biosciences Inc.) comprising 2 native rumen microbes on performance parameters in mid-lactation dairy cows. Forty-six lactating primiparous and multiparous Holstein cows [629 ± 62 kg of body weight, mean ± standard deviation (SD); parity 1.64 ± 0.49; 119 ± 38 days in milk; 45.11 ± 3.81 and 52.73 ± 4.77 kg/d of milk yield for primiparous and multiparous, respectively] were enrolled in a study containing 3 experimental periods (P). During all periods, enrolled cows were fed the same base total mixed ration (TMR) ad libitum once daily. During P1 (7 d), baseline data were obtained for covariate analysis. At the beginning of P2 (60 d), cows were assigned to 1 of 2 dietary treatment groups in a randomized complete block design to balance for milk yield (MY), parity, and days in milk: (1) a control diet (CON; base TMR; n = 23), or (2) a control diet supplemented with 5 g/d of MFS (MFS; n = 23). Sample size was determined based on previous, unpublished results involving this MFS; a 3-kg difference between groups with a SD of 3.5 kg could be detected with sufficient power (0.81) using a total sample size of 46 cows. Treatment was top-dressed and hand-mixed into the top one-third of the TMR. During P3 (7 d), no treatment was administered, and all cows were fed the base TMR. When analyzing all cows in the data set, MFS had little to no effect on performance. However, modeling revealed that the fixed effect of covariate milk production level had a significant effect on the response of MY and ECM, and further investigation of the data revealed that treatment effectiveness in P2 correlated with milk production during P1. Cows were retrospectively categorized into 2 milk production groups (MPG) balanced for parity: MPG1 (i.e., <53 kg/d of ECM during P1; n = 34) or MPG2 (i.e., ≥53 kg/d of ECM during P1; n = 12). Energy-corrected milk was increased by 4.4% in MFS-administered MPG1 cows compared with CON cows during P2. Although there were no significant effects of MFS on production variables for MPG2 cows, MY tended to be decreased by 3.9% in MFS-administered cows compared with CON cows. Further investigation is needed to understand production level response differences and the effect of supplemented native rumen microbes on animal health and productivity.

Assessment of the microbiome during bacteriophage therapy in combination with systemic antibiotics to treat a case of staphylococcal device infection

BACKGROUND

Infectious bacterial diseases exhibiting increasing resistance to antibiotics are a serious global health issue. Bacteriophage therapy is an anti-microbial alternative to treat patients with serious bacterial infections. However, the impacts to the host microbiome in response to clinical use of phage therapy are not well understood.

RESULTS

Our paper demonstrates a largely unchanged microbiota profile during 4 weeks of phage therapy when added to systemic antibiotics in a single patient with Staphylococcus aureus device infection. Metabolomic analyses suggest potential indirect cascading ecological impacts to the host (skin) microbiome. We did not detect genomes of the three phages used to treat the patient in metagenomic samples taken from saliva, stool, and skin; however, phages were detected using endpoint-PCR in patient serum.

CONCLUSION

Results from our proof-of-principal study supports the use of bacteriophages as a microbiome-sparing approach to treat bacterial infections. Video abstract.

Influence of Intermittent Hypoxia/Hypercapnia on Atherosclerosis, Gut Microbiome, and Metabolome

Obstructive sleep apnea (OSA), a common sleep disorder characterized by intermittent hypoxia and hypercapnia (IHC), increases atherosclerosis risk. However, the contribution of intermittent hypoxia (IH) or intermittent hypercapnia (IC) in promoting atherosclerosis remains unclear. Since gut microbiota and metabolites have been implicated in atherosclerosis, we examined whether IH or IC alters the microbiome and metabolome to induce a pro-atherosclerotic state. Apolipoprotein E deficient mice (ApoE-/- ), treated with IH or IC on a high-fat diet (HFD) for 10 weeks, were compared to Air controls. Atherosclerotic lesions were examined, gut microbiome was profiled using 16S rRNA gene amplicon sequencing and metabolome was assessed by untargeted mass spectrometry. In the aorta, IC-induced atherosclerosis was significantly greater than IH and Air controls (aorta, IC 11.1 ± 0.7% vs. IH 7.6 ± 0.4%, p < 0.05 vs. Air 8.1 ± 0.8%, p < 0.05). In the pulmonary artery (PA), however, IH, IC, and Air were significantly different from each other in atherosclerotic formation with the largest lesion observed under IH (PA, IH 40.9 ± 2.0% vs. IC 20.1 ± 2.6% vs. Air 12.2 ± 1.5%, p < 0.05). The most differentially abundant microbial families (p < 0.001) were Peptostreptococcaceae, Ruminococcaceae, and Erysipelotrichaceae. The most differentially abundant metabolites (p < 0.001) were tauro-β-muricholic acid, ursodeoxycholic acid, and lysophosphoethanolamine (18:0). We conclude that IH and IC (a) modulate atherosclerosis progression differently in distinct vascular beds with IC, unlike IH, facilitating atherosclerosis in both aorta and PA and (b) promote an atherosclerotic luminal gut environment that is more evident in IH than IC. We speculate that the resulting changes in the gut metabolome and microbiome interact differently with distinct vascular beds.

A comparison of DNA/RNA extraction protocols for high-throughput sequencing of microbial communities

One goal of microbial ecology researchers is to capture the maximum amount of information from all organisms in a sample. The recent COVID-19 pandemic, caused by the RNA virus SARS-CoV-2, has highlighted a gap in traditional DNA-based protocols, including the high-throughput methods the authors previously established as field standards. To enable simultaneous SARS-CoV-2 and microbial community profiling, the authors compared the relative performance of two total nucleic acid extraction protocols with the authors' previously benchmarked protocol. The authors included a diverse panel of environmental and host-associated sample types, including body sites commonly swabbed for COVID-19 testing. Here the authors present results comparing the cost, processing time, DNA and RNA yield, microbial community composition, limit of detection and well-to-well contamination between these protocols.

Context-aware dimensionality reduction deconvolutes gut microbial community dynamics

The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.

Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized ICU patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset in a meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome throughout their stay, SARS-CoV-2 was less frequently detected there (11%). Despite surface contamination in almost all patient rooms, no health care workers providing COVID-19 patient care contracted the disease. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types, and had higher prevalence in positive surface and human samples, even when comparing to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities contribute to viral prevalence both in the host and hospital environment.

A comparison of DNA/RNA extraction protocols for high-throughput sequencing of microbial communities

One goal among microbial ecology researchers is to capture the maximum amount of information from all organisms in a sample. The recent COVID-19 pandemic, caused by the RNA virus SARS-CoV-2, has highlighted a gap in traditional DNA-based protocols, including the high-throughput methods we previously established as field standards. To enable simultaneous SARS-CoV-2 and microbial community profiling, we compare the relative performance of two total nucleic acid extraction protocols and our previously benchmarked protocol. We included a diverse panel of environmental and host-associated sample types, including body sites commonly swabbed for COVID-19 testing. Here we present results comparing the cost, processing time, DNA and RNA yield, microbial community composition, limit of detection, and well-to-well contamination, between these protocols.

Accession numbers

Raw sequence data were deposited at the European Nucleotide Archive (accession#: ERP124610) and raw and processed data are available at Qiita (Study ID: 12201). All processing and analysis code is available on GitHub ( github.com/justinshaffer/Extraction_test_MagMAX ).

Methods summary

To allow for downstream applications involving RNA-based organisms such as SARS-CoV-2, we compared the two extraction protocols designed to extract DNA and RNA against our previously established protocol for extracting only DNA for microbial community analyses. Across 10 diverse sample types, one of the two protocols was equivalent or better than our established DNA-based protocol. Our conclusion is based on per-sample comparisons of DNA and RNA yield, the number of quality sequences generated, microbial community alpha- and beta-diversity and taxonomic composition, the limit of detection, and extent of well-to-well contamination.

SARS-CoV-2 Infection Depends on Cellular Heparan Sulfate and ACE2

We show that SARS-CoV-2 spike protein interacts with both cellular heparan sulfate and angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain (RBD). Docking studies suggest a heparin/heparan sulfate-binding site adjacent to the ACE2-binding site. Both ACE2 and heparin can bind independently to spike protein in vitro, and a ternary complex can be generated using heparin as a scaffold. Electron micrographs of spike protein suggests that heparin enhances the open conformation of the RBD that binds ACE2. On cells, spike protein binding depends on both heparan sulfate and ACE2. Unfractionated heparin, non-anticoagulant heparin, heparin lyases, and lung heparan sulfate potently block spike protein binding and/or infection by pseudotyped virus and authentic SARS-CoV-2 virus. We suggest a model in which viral attachment and infection involves heparan sulfate-dependent enhancement of binding to ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin presents new therapeutic opportunities.

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SARS-CoV-2 spike protein interacts with heparan sulfate and ACE2 through the RBD

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Heparan sulfate promotes Spike-ACE2 interaction

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SARS-CoV-2 infection is co-dependent on heparan sulfate and ACE2

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Heparin and non-anticoagulant derivatives block SARS-CoV-2 binding and infection

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro , bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.

SARS-CoV-2 Infection Depends on Cellular Heparan Sulfate and ACE2

We show that SARS-CoV-2 spike protein interacts with cell surface heparan sulfate and angiotensin converting enzyme 2 (ACE2) through its Receptor Binding Domain. Docking studies suggest a putative heparin/heparan sulfate-binding site adjacent to the domain that binds to ACE2. In vitro, binding of ACE2 and heparin to spike protein ectodomains occurs independently and a ternary complex can be generated using heparin as a template. Contrary to studies with purified components, spike protein binding to heparan sulfate and ACE2 on cells occurs codependently. Unfractionated heparin, non-anticoagulant heparin, treatment with heparin lyases, and purified lung heparan sulfate potently block spike protein binding and infection by spike protein-pseudotyped virus and SARS-CoV-2 virus. These findings support a model for SARS-CoV-2 infection in which viral attachment and infection involves formation of a complex between heparan sulfate and ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin may represent new therapeutic opportunities.

Visualizing 'omic feature rankings and log-ratios using Qurro

Many tools for dealing with compositional ' 'omics' data produce feature-wise values that can be ranked in order to describe features' associations with some sort of variation. These values include differentials (which describe features' associations with specified covariates) and feature loadings (which describe features' associations with variation along a given axis in a biplot). Although prior work has discussed the use of these 'rankings' as a starting point for exploring the log-ratios of particularly high- or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a 'rank plot') alongside a plot of selected features' log-ratios within samples (a 'sample plot'). Qurro's interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here, we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.

Consumption of Fermented Plant Foods Is Associated with Systematic Differences in the Human Gut Microbiome and Metabolome

Objectives

Fermented foods have gained much attention due to their proposed gut health benefits from recent clinical trials. However, very few studies have explored the effects of fermented foods, especially of plant origin, on gut microbiota composition and functional capacity in large human cohorts. Thus, the objective of this study was to assess whether self-reported fermented plant food consumption is associated with compositional or functional microbiome changes in a subset of individuals in the American Gut Project (AGP) cohort.

Methods

Using a multi-omics approach (e.g., 16S rRNA amplicon sequencing, metagenomic sequencing, and untargeted mass spectrometry), we analyzed stool samples from 6811 healthy individuals from the AGP including 115 individuals specifically recruited for their fermented plant food consumption for a targeted four-week longitudinal study.

Results

We observed subtle, yet statistically significant differences between fermented plant food consumers and non-consumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented plant food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting compound. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented plant food consumers.

Conclusions

Collectively, we found modest, yet persistent signatures associated with fermented plant food consumption that appear present in multiple omic types, which motivates further investigation of how different types of fermented foods may impact the human gut microbiome and overall health.

Funding Sources

Danone Nutricia Research.

Metagenomic and volatile profiles of ripened cheese obtained from dairy ewes fed a dietary hemp seed supplementation

Chemical and organoleptic properties of dairy products largely depend on the action of microorganisms that tend to be selected in cheese during ripening in response to the availability of specific substrates. The aim of this work was to evaluate the effects of a diet enriched with hemp seeds on the microbiota composition of fresh and ripened cheese produced from milk of lactating ewes. Thirty-two half-bred ewes were involved in the study, in which half (control group) received a standard diet, and the other half (experimental group) took a diet enriched with 5% hemp seeds (on a DM basis) for 35 d. The dietary supplementation significantly increased the lactose in milk, but no variations in total fat, proteins, caseins, and urea were observed. Likewise, no changes in total fat, proteins, or ash were detected in the derived cheeses. The metagenomic approach was used to characterize the microbiota of raw milk and cheese. The phyla Proteobacteria and Firmicutes were in equally high abundance in both control and experimental raw milk samples, whereas Bacteroidetes was less abundant. The scenario changed when considering the dairy products. In all cheese samples, Firmicutes was clearly predominant, with Streptococcaceae being the most abundant family in the experimental group. The reduction of taxa observed during ripening was in accordance with the increment (relative abundance) of the starter culture Lactococcus lactis and Streptococcus thermophilus, which together dominate the microbial community. The analysis of the volatile profile in ripened cheeses led to the identification of 3 major classes of compounds: free fatty acids, ketones, and aldehydes, which indicate a prevalence of lipolysis compared with the other biochemical mechanisms that characterize the cheese ripening.

Is pizza sutable to type 1 diabetes? A real life identification of best compromise between taste and low glycemic index in patients on insulin pump

Opposed to whole wheat (WWP), traditional pizza (TP) is loved by patients with type 1 diabetes mellitus (T1DM) despite causing hyperglycemia. 50 well-trained T1DM patients had higher glucose levels after TP than after WWP or mixed flour pizza, which however was tasty, digestible and metabolically appropriate to break diet monotony.

Air pollution exposure is associated with the gut microbiome as revealed by shotgun metagenomic sequencing

Animal work indicates exposure to air pollutants may alter the composition of the gut microbiota. This study examined relationships between air pollutants and the gut microbiome in young adults residing in Southern California. Our results demonstrate significant associations between exposure to air pollutants and the composition of the gut microbiome using whole-genome sequencing. Higher exposure to 24-hour O3 was associated with lower Shannon diversity index, higher Bacteroides caecimuris, and multiple gene pathways, including L-ornithine de novo biosynthesis as well as pantothenate and coenzyme A biosynthesis I. Among other pollutants, higher NO2 exposure was associated with fewer taxa, including higher Firmicutes. The percent variation in gut bacterial composition that was explained by air pollution exposure was up to 11.2% for O3 concentrations, which is large compared to the effect size for many other covariates reported in healthy populations. This study provides the first evidence of significant associations between exposure to air pollutants and the compositional and functional profile of the human gut microbiome. These results identify O3 as an important pollutant that may alter the human gut microbiome.

Consumption of Fermented Foods Is Associated with Systematic Differences in the Gut Microbiome and Metabolome

Lifestyle factors, such as diet, strongly influence the structure, diversity, and composition of the microbiome. While we have witnessed over the last several years a resurgence of interest in fermented foods, no study has specifically explored the effects of their consumption on gut microbiota in large cohorts. To assess whether the consumption of fermented foods is associated with a systematic signal in the gut microbiome and metabolome, we used a multi-omic approach (16S rRNA amplicon sequencing, metagenomic sequencing, and untargeted mass spectrometry) to analyze stool samples from 6,811 individuals from the American Gut Project, including 115 individuals specifically recruited for their frequency of fermented food consumption for a targeted 4-week longitudinal study. We observed subtle but statistically significant differences between consumers and nonconsumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting molecule. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented food consumers. Collectively, we found modest yet persistent signatures associated with fermented food consumption that appear present in multiple -omic types which motivate further investigation of how different types of fermented food impact the gut microbiome and overall health.IMPORTANCE Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful.

Cryptochrome mediated magnetic sensitivity in Arabidopsis occurs independently of light-induced electron transfer to the flavin

Arabidopsis cryptochrome-dependent magnetosensitivity occurs via a reaction that does not require light. This excludes radical pairs formed during light-triggered electron transfer to the flavin.

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

The rumen microbiome is critical to nutrient utilization and feed efficiency in cattle. Consequently, the objective of this study was to identify microbial and biochemical factors in Angus steers affecting divergences in feed efficiency using 16S amplicon sequencing and untargeted metabolomics. Based on calculated average residual feed intake (RFI), steers were divided into high- and low-RFI groups. Features were ranked in relation to RFI through supervised machine learning on microbial and metabolite compositions. Residual feed intake was associated with several features of the bacterial community in the rumen. Decreased bacterial α- (P = 0.03) and β- diversity (P < 0.001) was associated with Low-RFI steers. RFI was associated with several serum metabolites. Low-RFI steers had greater abundances of pantothenate (P = 0.02) based on fold change (high/low RFI). Machine learning on RFI was predictive of both rumen bacterial composition and serum metabolomic signature (AUC ≥ 0.7). Log-ratio proportions of the bacterial classes Flavobacteriia over Fusobacteriia were enriched in low-RFI steers (F = 6.8, P = 0.01). Reductions in Fusobacteriia and/or greater proportions of pantothenate-producing bacteria, such as Flavobacteriia, may result in improved nutrient utilization in low-RFI steers. Flavobacteriia and Pantothenate may potentially serve as novel biomarkers to predict or evaluate feed efficiency in Angus steers.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates

Dietary habits have been associated with alterations of the human gut resident microorganisms contributing to obesity, diabetes and cancer¹. In Western diets, red meat is a frequently eaten food², but long-term consumption has been associated with increased risk of disease^3,4. Red meat is enriched in N-glycolylneuraminic acid (Neu5Gc) that cannot be synthesized by humans⁵. However, consumption can cause Neu5Gc incorporation into cell surface glycans⁶, especially in carcinomas^4,7. As a consequence, an inflammatory response is triggered when Neu5Gc-containing glycans encounter circulating anti-Neu5Gc antibodies^8,9. Although bacteria can use free sialic acids as a nutrient source^10-12, it is currently unknown if gut microorganisms contribute to releasing Neu5Gc from food. We found that a Neu5Gc-rich diet induces changes in the gut microbiota, with Bacteroidales and Clostridiales responding the most. Genome assembling of mouse and human shotgun metagenomic sequencing identified bacterial sialidases with previously unobserved substrate preference for Neu5Gc-containing glycans. X-ray crystallography revealed key amino acids potentially contributing to substrate preference. Additionally, we verified that mouse and human sialidases were able to release Neu5Gc from red meat. The release of Neu5Gc from red meat using bacterial sialidases could reduce the risk of inflammatory diseases associated with red meat consumption, including colorectal cancer⁴ and atherosclerosis¹³.

Temporal Stability of the Ruminal Bacterial Communities in Beef Steers

Nutritional studies involving ruminants have traditionally relied on relatively short transition or wash-out periods between dietary treatments, typically two to four weeks. However, little is known about adequate adaptation periods required to reach stabilization of the rumen microbiome that could provide more accurate results from nutritional studies in ruminants. This study determined the rumen bacterial communities and rumen environment parameters over ten weeks following transition from a forage-based to concentrate-based diet. Several α-diversity metrics, including observed OTUs and Simpson’s Evenness fluctuated throughout the trial, but were typically either greatest (observed OTUs) or lowest (Simpson’s) at week 5 of the trial contrasted from weeks 1 and 10 (P < 0.05). At week 4, several orders associated with the shift to the final bacterial community composition, including Pasteurellales, Aeromonadales, and Bacteroidales. At week 5, rumen pH was correlated with α-diversity (P = 0.005) and predictive of the rumen microbiome signature at week 10 (R² = 0.48; P = 0.04). Rumen microbiome stability did not occur until approximately 9 weeks following adaptation to the diet and was associated with changes in specific bacterial populations and rumen environment. The results of this study suggest that adaptation and wash-out periods must be re-evaluated in order to accommodate necessary rumen microbiome acclimation.

Engineering the microbiome for animal health and conservation

IMPACT STATEMENT

Considering the clear effects of microbiota on important aspects of animal biology and development (including in humans), this topic is timely and broadly appealing, as it compels us to consider the possibilities of altering the microbiome (without antibiotics) to positively affect animal health. In this review, we highlight three general approaches to manipulating the microbiome that have demonstrated success and promise for use in animal health. We also point out knowledge gaps where further inquiry would most benefit the field. Our paper not only provides a short and digestible overview of the current state of application, but also calls for further exploration of the microbial diversity at hand to expand our toolkit, while also leveraging the diversity and flexibility of animal systems to better understand mechanisms of efficacy.

A Novel Sparse Compositional Technique Reveals Microbial Perturbations

The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.