Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights: The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Gut microbiota analyses of Saudi populations for type 2 diabetes-related phenotypes reveals significant association.

BACKGROUND: Large-scale gut microbiome sequencing has revealed key links between microbiome dysfunction and metabolic diseases such as type 2 diabetes (T2D). To date, these efforts have largely focused on Western populations, with few studies assessing T2D microbiota associations in Middle Eastern communities where T2D prevalence is now over 20%. We analyzed the composition of stool 16S rRNA from 461 T2D and 119 non-T2D participants from the Eastern Province of Saudi Arabia. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples based on diabetes status and glucose level. RESULTS: In this study we performed the largest microbiome study ever conducted in Saudi Arabia, as well as the first-ever characterization of gut microbiota T2D versus non-T2D in this population. We observed overall positive enrichment within diabetics compared to healthy individuals and amongst diabetic participants; those with high glucose levels exhibited slightly more positive enrichment compared to those at lower risk of fasting hyperglycemia. In particular, the genus Firmicutes was upregulated in diabetic individuals compared to non-diabetic individuals, and T2D was associated with an elevated Firmicutes/Bacteroidetes ratio, consistent with previous findings. CONCLUSION: Based on diabetes status and glucose levels of Saudi participants, relatively stable differences in stool composition were perceived by differential abundance and alpha diversity measures. However, community level differences are evident in the Saudi population between T2D and non-T2D individuals, and diversity patterns appear to vary from well-characterized microbiota from Western cohorts. Comparing overlapping and varying patterns in gut microbiota with other studies is critical to assessing novel treatment options in light of a rapidly growing T2D health epidemic in the region. As a rapidly emerging chronic condition in Saudi Arabia and the Middle East, T2D burdens have grown more quickly and affect larger proportions of the population than any other global region, making a regional reference T2D-microbiome dataset critical to understanding the nuances of disease development on a global scale.

Individuality and ethnicity eclipse a short-term dietary intervention in shaping microbiomes and viromes.

Many diseases linked with ethnic health disparities associate with changes in microbial communities in the United States, but the causes and persistence of ethnicity-associated microbiome variation are not understood. For instance, microbiome studies that strictly control for diet across ethnically diverse populations are lacking. Here, we performed multiomic profiling over a 9-day period that included a 4-day controlled vegetarian diet intervention in a defined geographic location across 36 healthy Black and White females of similar age, weight, habitual diets, and health status. We demonstrate that individuality and ethnicity account for roughly 70% to 88% and 2% to 10% of taxonomic variation, respectively, eclipsing the effects a short-term diet intervention in shaping gut and oral microbiomes and gut viromes. Persistent variation between ethnicities occurs for microbial and viral taxa and various metagenomic functions, including several gut KEGG orthologs, oral carbohydrate active enzyme categories, cluster of orthologous groups of proteins, and antibiotic-resistant gene categories. In contrast to the gut and oral microbiome data, the urine and plasma metabolites tend to decouple from ethnicity and more strongly associate with diet. These longitudinal, multiomic profiles paired with a dietary intervention illuminate previously unrecognized associations of ethnicity with metagenomic and viromic features across body sites and cohorts within a single geographic location, highlighting the importance of accounting for human microbiome variation in research, health determinants, and eventual therapies. Trial Registration: ClinicalTrials.gov ClinicalTrials.gov Identifier: NCT03314194.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Real-time Alerting System for COVID-19 Using Wearable Data.

Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.

Early detection of SARS-CoV-2 and other infections in solid organ transplant recipients and household members using wearable devices.

The increasing global prevalence of SARS-CoV-2 and the resulting COVID-19 disease pandemic pose significant concerns for clinical management of solid organ transplant recipients (SOTR). Wearable devices that can measure physiologic changes in biometrics including heart rate, heart rate variability, body temperature, respiratory, activity (such as steps taken per day) and sleep patterns, and blood oxygen saturation show utility for the early detection of infection before clinical presentation of symptoms. Recent algorithms developed using preliminary wearable datasets show that SARS-CoV-2 is detectable before clinical symptoms in >80% of adults. Early detection of SARS-CoV-2, influenza, and other pathogens in SOTR, and their household members, could facilitate early interventions such as self-isolation and early clinical management of relevant infection(s). Ongoing studies testing the utility of wearable devices such as smartwatches for early detection of SARS-CoV-2 and other infections in the general population are reviewed here, along with the practical challenges to implementing these processes at scale in pediatric and adult SOTR, and their household members. The resources and logistics, including transplant-specific analyses pipelines to account for confounders such as polypharmacy and comorbidities, required in studies of pediatric and adult SOTR for the robust early detection of SARS-CoV-2, and other infections are also reviewed.

Pre-symptomatic detection of COVID-19 from smartwatch data.

Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

Finer-Scale Phylosymbiosis: Insights from Insect Viromes.

Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern whereby the ecological relatedness (e.g., beta diversity relationships) of host-associated microbial communities parallels the phylogeny of the host species. Representing the most abundant biological entities on the planet and common members of the animal-associated microbiome, viruses can be influential members of host-associated microbial communities that may recapitulate, reinforce, or ablate phylosymbiosis. Here we sequence the metagenomes of purified viral communities from three different parasitic wasp Nasonia species, one cytonuclear introgression line of Nasonia, and the flour moth outgroup Ephestia kuehniella. Results demonstrate complete phylosymbiosis between the viral metagenome and insect phylogeny. Across all Nasonia contigs, 69% of the genes in the viral metagenomes are either new to the databases or uncharacterized, yet over 99% of the contigs have at least one gene with similarity to a known sequence. The core Nasonia virome spans 21% of the total contigs, and the majority of that core is likely derived from induced prophages residing in the genomes of common Nasonia-associated bacterial genera: Proteus, Providencia, and Morganella. We also assemble the first complete viral particle genomes from Nasonia-associated gut bacteria. Taken together, results reveal the first complete evidence for phylosymbiosis in viral metagenomes, new genome sequences of viral particles from Nasonia-associated gut bacteria, and a large set of novel or uncharacterized genes in the Nasonia virome. This work suggests that phylosymbiosis at the host-microbiome level will likely extend to the host-virome level in other systems as well. IMPORTANCE Viruses are the most abundant biological entity on the planet and interact with microbial communities with which they associate. The virome of animals is often dominated by bacterial viruses, known as bacteriophages or phages, which can (re)structure bacterial communities potentially vital to the animal host. Beta diversity relationships of animal-associated bacterial communities in laboratory and wild populations frequently parallel animal phylogenetic relationships, a pattern termed phylosymbiosis. However, little is known about whether viral communities also exhibit this eco-evolutionary pattern. Metagenomics of purified viruses from recently diverged species of Nasonia parasitoid wasps reared in the lab indicates for the first time that the community relationships of the virome can also exhibit complete phylosymbiosis. Therefore, viruses, particularly bacteriophages here, may also be influenced by animal evolutionary changes either directly or indirectly through the tripartite interactions among hosts, bacteria, and phage communities. Moreover, we report several new bacteriophage genomes from the common gut bacteria in Nasonia.

Gut microbiota diversity across ethnicities in the United States.

Composed of hundreds of microbial species, the composition of the human gut microbiota can vary with chronic diseases underlying health disparities that disproportionally affect ethnic minorities. However, the influence of ethnicity on the gut microbiota remains largely unexplored and lacks reproducible generalizations across studies. By distilling associations between ethnicity and differences in two US-based 16S gut microbiota data sets including 1,673 individuals, we report 12 microbial genera and families that reproducibly vary by ethnicity. Interestingly, a majority of these microbial taxa, including the most heritable bacterial family, Christensenellaceae, overlap with genetically associated taxa and form co-occurring clusters linked by similar fermentative and methanogenic metabolic processes. These results demonstrate recurrent associations between specific taxa in the gut microbiota and ethnicity, providing hypotheses for examining specific members of the gut microbiota as mediators of health disparities.

Evolutionary Genetics of Cytoplasmic Incompatibility Genes cifA and cifB in Prophage WO of Wolbachia.

The bacterial endosymbiont Wolbachia manipulates arthropod reproduction to facilitate its maternal spread through host populations. The most common manipulation is cytoplasmic incompatibility (CI): Wolbachia-infected males produce modified sperm that cause embryonic mortality, unless rescued by embryos harboring the same Wolbachia. The genes underlying CI, cifA and cifB, were recently identified in the eukaryotic association module of Wolbachia's prophage WO. Here, we use transcriptomic and genomic approaches to address three important evolutionary facets of the cif genes. First, we assess whether or not cifA and cifB comprise a classic toxin-antitoxin operon in wMel and show that the two genes exhibit striking, transcriptional differences across host development. They can produce a bicistronic message despite a predicted hairpin termination element in their intergenic region. Second, cifA and cifB strongly coevolve across the diversity of phage WO. Third, we provide new domain and functional predictions across homologs within Wolbachia, and show that amino acid sequences vary substantially across the genus. Finally, we investigate conservation of cifA and cifB and find frequent degradation and loss of the genes in strains that no longer induce CI. Taken together, we demonstrate that cifA and cifB exhibit complex transcriptional regulation in wMel, provide functional annotations that broaden the potential mechanisms of CI induction, and report recurrent erosion of cifA and cifB in non-CI strains, thus expanding our understanding of the most widespread form of reproductive parasitism.

Genetic signatures for Helicobacter pylori strains of West African origin.

Helicobacter pylori is a genetically diverse bacterial species that colonizes the stomach in about half of the human population. Most persons colonized by H. pylori remain asymptomatic, but the presence of this organism is a risk factor for gastric cancer. Multiple populations and subpopulations of H. pylori with distinct geographic distributions are recognized. Genetic differences among these populations might be a factor underlying geographic variation in gastric cancer incidence. Relatively little is known about the genomic features of African H. pylori strains compared to other populations of strains. In this study, we first analyzed the genomes of H. pylori strains from seven globally distributed populations or subpopulations and identified encoded proteins that exhibited the highest levels of sequence divergence. These included secreted proteins, an LPS glycosyltransferase, fucosyltransferases, proteins involved in molybdopterin biosynthesis, and Clp protease adaptor (ClpS). Among proteins encoded by the cag pathogenicity island, CagA and CagQ exhibited the highest levels of sequence diversity. We then identified proteins in strains of Western African origin (classified as hspWAfrica by MLST analysis) with sequences that were highly divergent compared to those in other populations of strains. These included ATP-dependent Clp protease, ClpS, and proteins of unknown function. Three of the divergent proteins sequences identified in West African strains were characterized by distinct insertions or deletions up to 8 amino acids in length. These polymorphisms in rapidly evolving proteins represent robust genetic signatures for H. pylori strains of West African origin.

Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History.

[This corrects the article DOI: 10.1371/journal.pbio.2000225.].

Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History.

Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern, whereby the ecological relatedness of host-associated microbial communities parallels the phylogeny of related host species. Here, we test the prevalence of phylosymbiosis and its functional significance under highly controlled conditions by characterizing the microbiota of 24 animal species from four different groups (Peromyscus deer mice, Drosophila flies, mosquitoes, and Nasonia wasps), and we reevaluate the phylosymbiotic relationships of seven species of wild hominids. We demonstrate three key findings. First, intraspecific microbiota variation is consistently less than interspecific microbiota variation, and microbiota-based models predict host species origin with high accuracy across the dataset. Interestingly, the age of host clade divergence positively associates with the degree of microbial community distinguishability between species within the host clades, spanning recent host speciation events (~1 million y ago) to more distantly related host genera (~108 million y ago). Second, topological congruence analyses of each group's complete phylogeny and microbiota dendrogram reveal significant degrees of phylosymbiosis, irrespective of host clade age or taxonomy. Third, consistent with selection on host-microbiota interactions driving phylosymbiosis, there are survival and performance reductions when interspecific microbiota transplants are conducted between closely related and divergent host species pairs. Overall, these findings indicate that the composition and functional effects of an animal's microbial community can be closely allied with host evolution, even across wide-ranging timescales and diverse animal systems reared under controlled conditions.

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.