Developing the Common Marmoset as a Translational Geroscience Model to Study the Microbiome and Healthy Aging.

Emerging data support associations between the depletion of the healthy gut microbiome and aging-related physiological decline and disease. In humans, fecal microbiota transplantation (FMT) has been used successfully to restore gut microbiome structure and function and to treat C. difficile infections, but its application to healthy aging has been scarcely investigated. The marmoset is an excellent model for evaluating microbiome-mediated changes with age and interventional treatments due to their relatively shorter lifespan and many social, behavioral, and physiological functions that mimic human aging. Prior work indicates that FMT is safe in marmosets and may successfully mediate gut microbiome function and host health. This narrative review (1) provides an overview of the rationale for FMT to support healthy aging using the marmoset as a translational geroscience model, (2) summarizes the prior use of FMT in marmosets, (3) outlines a protocol synthesized from prior literature for studying FMT in aging marmosets, and (4) describes limitations, knowledge gaps, and future research needs in this field.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities.

Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We tested if detecting SARS-CoV-2 in wastewater can predict outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored several rural communities in Idaho (USA). While high daily variations in wastewater viral load made real-time interpretation difficult, a SEIR model could factor out the data noise and forecast the start of the Omicron outbreak in five of the six cities that were sampled soon after SARS-CoV-2 quantities increased in wastewater. For one city, the model could predict an outbreak 11 days before reported clinical cases began to increase. An epidemiological modeling approach can transform how epidemiologists use wastewater data to provide public health guidance on infectious diseases in rural communities.

Application of elastic net regression for modeling COVID-19 sociodemographic risk factors.

OBJECTIVES: COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19. METHODS: We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period). RESULTS: Statistically, elastic net improved prediction when compared to multiple regression, as almost every HHS model consistently had a lower root mean square error (RMSE) and satisfactory R2 coefficients. These analyses show that the percentage of minorities, disabled individuals, individuals living in group quarters, and individuals who voted Democratic correlated significantly with COVID-19 attack rate as determined by Variable Importance Plots (VIPs). CONCLUSIONS: The percentage of minorities per county correlated positively with cases in the earlier time period and negatively in the later time period, which complements previous research. In contrast, higher percentages of disabled individuals per county correlated negatively in the earlier time period. Counties with an above average percentage of group quarters experienced a high attack rate early which then diminished in significance after the primary vaccine rollout. Higher Democratic voting consistently correlated negatively with cases, coinciding with previous findings regarding a partisan divide in COVID-19 cases at the county level. Our findings can assist regional policymakers in distributing resources to more vulnerable counties in future pandemics based on SVI.

Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality.

In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.

Correction: Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations.

[This corrects the article DOI: 10.1371/journal.pgen.1008458.].

Application of Elastic Net Regression for Modeling COVID-19 Sociodemographic Risk Factors.

Objectives: COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19. Methods: We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period). Results: Statistically, elastic net improved prediction when compared to multiple regression, as almost every HHS model consistently had a lower root mean square error (RMSE) and satisfactory R2 coefficients. These analyses show that the percentage of minorities, disabled individuals, individuals living in group quarters, and individuals who voted Democratic correlated significantly with COVID-19 attack rate as determined by Variable Importance Plots (VIPs). Conclusions: The percentage of minorities per county correlated positively with cases in the earlier time period and negatively in the later time period, which complements previous research. In contrast, higher percentages of disabled individuals per county correlated negatively in the earlier time period. Counties with an above average percentage of group quarters experienced a high attack rate early which then diminished in significance after the primary vaccine rollout. Higher Democratic voting consistently correlated negatively with cases, coinciding with previous findings regarding a partisan divide in COVID-19 cases at the county level. Our findings can assist policymakers in distributing resources to more vulnerable counties in future pandemics based on SVI.

Biofilms preserve the transmissibility of a multi-drug resistance plasmid.

Self-transmissible multidrug resistance (MDR) plasmids are a major health concern because they can spread antibiotic resistance to pathogens. Even though most pathogens form biofilms, little is known about how MDR plasmids persist and evolve in biofilms. We hypothesize that (i) biofilms act as refugia of MDR plasmids by retaining them in the absence of antibiotics longer than well-mixed planktonic populations and that (ii) the evolutionary trajectories that account for the improvement of plasmid persistence over time differ between biofilms and planktonic populations. In this study, we evolved Acinetobacter baumannii with an MDR plasmid in biofilm and planktonic populations with and without antibiotic selection. In the absence of selection, biofilm populations were better able to maintain the MDR plasmid than planktonic populations. In planktonic populations, plasmid persistence improved rapidly but was accompanied by a loss of genes required for the horizontal transfer of plasmids. In contrast, in biofilms, most plasmids retained their transfer genes, but on average, plasmid, persistence improved less over time. Our results showed that biofilms can act as refugia of MDR plasmids and favor the horizontal mode of plasmid transfer, which has important implications for the spread of MDR.

Effects of a farm-specific fecal microbial transplant (FMT) product on clinical outcomes and fecal microbiome composition in preweaned dairy calves.

Gastrointestinal disease (GI) is the most common illness in pre-weaned dairy calves. Therefore, effective strategies to manipulate the microbiome of dairy calves under commercial dairy operations are of great importance to improve animal health and reduce antimicrobial usage. The objective of this study was to develop a farm-specific FMT product and to investigate its effects on clinical outcomes and fecal microbial composition of dairy calves. The FMT product was derived from feces from healthy donors (5-24 days of age) raised in the same calf ranch facility as the FMT recipients. Healthy and diarrheic calves were randomly enrolled to a control (n = 115) or FMT (n = 112) treatment group (~36 g of processed fecal matter once daily for 3 days). Fecal samples were collected at enrollment and again 9 days later after the first FMT dose. Although the FMT product was rich in organisms typically known for their beneficial probiotic properties, the FMT therapy did not prevent or ameliorate GI disease in dairy calves. In fact, calves that received FMT were less likely to recover from GI disease, and more likely to die due to GI disease complications. Fecal microbial community analysis revealed an increase in the alpha-diversity in FMT calves; however, no major differences across treatment groups were observed in the beta-diversity analysis. Calves that received FMT had higher relative abundance of an uncultured organism of the genus Lactobacillus and Lactobacillus reuteri on day 10. Moreover, FMT calves had lower relative abundance of Clostridium nexile and Bacteroides vulgatus on day 10. Our results indicate the need to have an established protocol when developing FMT products, based on rigorous inclusion and exclusion criteria for the selection of FMT donors free of potential pathogens, no history of disease or antibiotic treatment.

Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria.

Vibrio cholerae biotype El Tor is perpetuating the longest cholera pandemic in recorded history. The genomic islands VSP-1 and VSP-2 distinguish El Tor from previous pandemic V. cholerae strains. Using a co-occurrence analysis of VSP genes in >200,000 bacterial genomes we built gene networks to infer biological functions encoded in these islands. This revealed that dncV, a component of the cyclic-oligonucleotide-based anti-phage signalling system (CBASS) anti-phage defence system, co-occurs with an uncharacterized gene vc0175 that we rename avcD for anti-viral cytodine deaminase. We show that AvcD is a deoxycytidylate deaminase and that its activity is post-translationally inhibited by a non-coding RNA named AvcI. AvcID and bacterial homologues protect bacterial populations against phage invasion by depleting free deoxycytidine nucleotides during infection, thereby decreasing phage replication. Homologues of avcD exist in all three domains of life, and bacterial AvcID defends against phage infection by combining traits of two eukaryotic innate viral immunity proteins, APOBEC and SAMHD1.

Effects of trust, risk perception, and health behavior on COVID-19 disease burden: Evidence from a multi-state US survey.

Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.

Fecal microbiome profiles of neonatal dairy calves with varying severities of gastrointestinal disease.

Gastrointestinal disease (GI) is the most common illness in pre-weaned dairy calves. Studies have associated the fecal microbiome composition with health status, but it remains unclear how the microbiome changes across different levels of GI disease and breeds. Our objective was to associate the clinical symptoms of GI disease with the fecal microbiome. Fecal samples were collected from calves (n = 167) of different breeds (Holstein, Jersey, Jersey-cross and beef-cross) from 4-21 d of age. Daily clinical evaluations assessed health status. Calves with loose or watery feces were diagnosed with diarrhea and classified as bright-sick (BS) or depressed-sick (DS) according to behavior. Calves with normal or semiformed feces and no clinical illness were classified as healthy (H). One hundred and three fecal samples were obtained from consistently healthy calves and 64 samples were from calves with diarrhea (n = 39 BS; n = 25 DS). The V3-V4 region of 16S rRNA gene was sequenced and analyzed. Differences were identified by a linear-mixed effects model with a negative binomial error. DS and Jersey calves had a higher relative abundance of Streptococcus gallolyticus relative to H Holstein calves. In addition, DS calves had a lower relative abundance of Bifidobacterium longum and an enrichment of Escherichia coli. Species of the genus Lactobacillus, such as an unclassified Lactobacillus, Lactobacillus reuteri, and Lactobacillus salivarius were enriched in calves with GI disease. Moreover, we created a model to predict GI disease based on the fecal microbiome composition. The presence of Eggerthella lenta, Bifidobacterium longum, and Collinsella aerofaciens were associated with a healthy clinical outcome. Although lactobacilli are often associated with beneficial probiotic properties, the presence of E. coli and Lactobacillus species had the highest coefficients positively associated with GI disease prediction. Our results indicate that there are differences in the fecal microbiome of calves associated with GI disease severity and breed specificities.

Structural identifiability of the generalized Lotka-Volterra model for microbiome studies.

Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.

Rhinovirus Reduces the Severity of Subsequent Respiratory Viral Infections by Interferon-Dependent and -Independent Mechanisms.

Coinfection by heterologous viruses in the respiratory tract is common and can alter disease severity compared to infection by individual virus strains. We previously found that inoculation of mice with rhinovirus (RV) 2 days before inoculation with a lethal dose of influenza A virus [A/Puerto Rico/8/34 (H1N1) (PR8)] provides complete protection against mortality. Here, we extended that finding to a second lethal respiratory virus, pneumonia virus of mice (PVM), and analyzed potential mechanisms of RV-induced protection. RV completely prevented mortality and weight loss associated with PVM infection. Major changes in host gene expression upon PVM infection were delayed compared to PR8. RV induced earlier recruitment of inflammatory cells, which were reduced at later times in RV-inoculated mice. Findings common to both virus pairs included the upregulated expression of mucin-associated genes and dampening of inflammation-related genes in mice that were inoculated with RV before lethal virus infection. However, type I interferon (IFN) signaling was required for RV-mediated protection against PR8 but not PVM. IFN signaling had minor effects on PR8 replication and contributed to controlling neutrophilic inflammation and hemorrhagic lung pathology in RV/PR8-infected mice. These findings, combined with differences in virus replication levels and disease severity, suggest that the suppression of inflammation in RV/PVM-infected mice may be due to early, IFN-independent suppression of viral replication, while that in RV/PR8-infected mice may be due to IFN-dependent modulation of immune responses. Thus, a mild upper respiratory viral infection can reduce the severity of a subsequent severe viral infection in the lungs through virus-dependent mechanisms. IMPORTANCE Respiratory viruses from diverse families cocirculate in human populations and are frequently detected within the same host. Although clinical studies suggest that infection by multiple different respiratory viruses may alter disease severity, animal models in which we can control the doses, timing, and strains of coinfecting viruses are critical to understanding how coinfection affects disease severity. Here, we compared gene expression and immune cell recruitment between two pairs of viruses (RV/PR8 and RV/PVM) inoculated sequentially in mice, both of which result in reduced severity compared to lethal infection by PR8 or PVM alone. Reduced disease severity was associated with suppression of inflammatory responses in the lungs. However, differences in disease kinetics and host and viral gene expression suggest that protection by coinfection with RV may be due to distinct molecular mechanisms. Indeed, we found that antiviral cytokine signaling was required for RV-mediated protection against lethal infection by PR8 but not PVM.

Risk of disease and willingness to vaccinate in the United States: A population-based survey.

BACKGROUND: Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy. We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived risk of infection-holding fixed other considerations such as vaccine confidence and convenience. METHODS AND FINDINGS: We used an original survey instrument that presents 7 vaccine-preventable "new" diseases to gather demographically diverse sample data from the United States in 2018 (N = 2,411). Our survey was conducted online between January 25, 2018, and February 2, 2018, and was structured in 3 parts. First, we collected information concerning the places participants live and visit in a typical week. Second, participants were presented with one of 7 hypothetical disease outbreaks and asked how they would respond. Third, we collected sociodemographic information. The survey was designed to match population parameters in the US on 5 major dimensions: age, sex, income, race, and census region. We also were able to closely match education. The aggregate demographic details for study participants were a mean age of 43.80 years, 47% male and 53% female, 38.5% with a college degree, and 24% nonwhite. We found an overall change of at least 30% in proportion willing to vaccinate as risk of infection increases. When considering morbidity information, the proportion willing to vaccinate went from 0.476 (0.449-0.503) at 0 local cases of disease to 0.871 (0.852-0.888) at 100 local cases (upper and lower 95% confidence intervals). When considering mortality information, the proportion went from 0.526 (0.494-0.557) at 0 local cases of disease to 0.916 (0.897-0.931) at 100 local cases. In addition, we ffound that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity (P = 0.0002), that older populations are more willing than younger (P<0.0001), that the highest income bracket (>$90,000) is more willing than all others (P = 0.0001), that men are more willing than women (P = 0.0011), and that the proportion willing to vaccinate is related to both ideology and the level of risk (P = 0.004). Limitations of this study include that it does not consider how other factors (such as social influence) interact with local case counts in people's vaccine decision-making, it cannot determine whether different degrees of severity in morbidity or mortality failed to be statistically significant because of survey design or because participants use heuristically driven decision-making that glosses over degrees, and the study does not capture the part of the US that is not online. CONCLUSIONS: In this study, we found that different degrees of risk (in terms of local cases of disease) correspond with different proportions of populations willing to vaccinate. We also identified several sociodemographic aspects of vaccine propensity. Understanding how vaccine propensity is affected by sociodemographic factors is invaluable for predicting where outbreaks are more likely to occur and their expected size, even with the resulting cascade of changing vaccination rates and the respective feedback on potential outbreaks.

Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations.

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Vaginal Biomarkers That Predict Cervical Length and Dominant Bacteria in the Vaginal Microbiomes of Pregnant Women.

In many impoverished regions of the world, it may not be possible to assess two major risk factors for preterm birth: a short cervical length and the depletion of vaginal lactobacilli. We determined whether measuring specific compounds in vaginal fluid might be a simple, noninvasive, and cost-effective way to predict the bacteria that dominate the vaginal microbiome and indicate the presence of a shortened cervix (<25 mm). Vaginal fluid samples were prospectively collected from mid-trimester pregnant women, and the concentrations of d- and l-lactic acid, tissue inhibitor of matrix metalloproteinases TIMP-1 and TIMP-2, matrix metalloproteinases MMP-2 and MMP-8, the 70-kDa heat shock protein, a2 isoform of vacuolar ATPase, and sequestrome-1 were quantified by an enzyme-linked immunosorbent assay (ELISA). The compositions of vaginal microbiomes were assessed by analysis of the V1-V3 regions of 16S rRNA genes, while cervical length was determined by transvaginal ultrasonography. The vaginal microbiomes could be clustered into five community state types (CSTs), four of which were dominated by a single Lactobacillus species. The dominance of Lactobacillus crispatus or Lactobacillus jensenii in the vaginal microbiome predicted the level of d-lactic acid present. Several of the biomarkers, especially TIMP-1, in combination with the subject's age and race, were significantly associated with cervical length. Using piecewise structural equation modeling, we established a causal network that links CST to cervical length via biomarkers. We concluded that measuring levels of TIMP-1 and d-lactic acid in vaginal secretions might be a straightforward way to assess the risk for preterm birth due to a short cervix and microbiome composition.IMPORTANCE Premature birth and its complications are the largest contributors to infant death in the United States and globally. A short cervical length and the depletion of Lactobacillus species are known risk factors for preterm birth. However, in many resource-poor areas of the world, the technology to test for their occurrence is unavailable, and pregnant women with these risk factors are neither identified nor treated. In this study, we used path analysis to gain an unprecedented understanding of interactions between vaginal microbiome composition, the concentrations of various compounds in vaginal secretions, and cervical length. We identified low-cost point-of-care measures that might be used to identify pregnant women at risk for preterm birth. The use of these measures coupled with appropriate preventative or treatment strategies could reduce the incidence of preterm births in poor areas of the world that lack access to more sophisticated diagnostic methods.

Comparison of the Vaginal Microbiomes of Premenopausal and Postmenopausal Women.

For decades hormone therapy (HT) has been prescribed to treat the symptoms of menopause, such as vaginal dryness, itching and burning. Here we sought to compare the vaginal microbiomes of postmenopausal women who received low dose estrogen therapy to those of premenopausal and postmenopausal women, and to do so in conjunction with assessing the alleviation of symptoms associated with vaginal atrophy. In this study vaginal swab samples were obtained from 45 women who were classified as either premenopausal, postmenopausal, or postmenopausal and undergoing HT. The vaginal microbiomes of these women were characterized by 16S rRNA gene sequencing and bacterial abundances were quantified by qPCR. We found that the vaginal communities from our cohort could be divided into six clusters (A-F) based on differences in the composition and relative abundances of bacterial taxa. Communities in cluster A were dominated by Lactobacillus crispatus, and those of cluster B were dominated by Gardnerella vaginalis. Communities in cluster C had high proportions of L. iners, while those in cluster D were more even and included several co-dominant taxa. Communities in clusters E and F were dominated by Bifidobacterium and L. gasseri, respectively. The vaginal communities of most postmenopausal women receiving HT (10/15) were dominated by species of lactobacilli and belonged to clusters A, C, and F (P < 0.001). This sharply contrasts with vaginal communities of postmenopausal women without HT, most of which (10/15) were in cluster D, depleted of lactobacilli, and had about 10-fold fewer total bacteria (P < 0.05). The vaginal communities of women in each study group differed in terms of the dominant bacterial species composition and relative abundance. Those of postmenopausal women receiving HT significantly differed from those of postmenopausal women without HT and were most often dominated by species of Lactobacillus. Noteworthy, HT greatly improved vaginal atrophy scores, decreased vaginal pH, and significantly increased bacterial numbers in comparison to postmenopausal women not receiving HT.

Vaginal Glycogen, Not Estradiol, Is Associated With Vaginal Bacterial Community Composition in Black Adolescent Women.

PURPOSE: The purpose of this study was to characterize the composition of vaginal bacterial communities in a cohort of black adolescent women and to determine how the species composition of these communities correlates with levels of estradiol, glycogen, and stress. METHODS: Twenty-one black adolescent women were sampled longitudinally. The composition of their vaginal communities was determined by analyzing the sequences of the V1-V3 regions of 16S rRNA genes, and they were grouped based on patterns in species abundances. The relationships between estradiol, glycogen, psychosocial stress, and the composition of these communities were assessed. RESULTS: Vaginal communities could be distinguished and classified into three groups that differed in the abundances of Lactobacillus. Eighty-one percent of study participants had communities dominated by species of Lactobacillus. Glycogen levels were higher in communities dominated by one or multiple species of Lactobacillus compared with those having low proportions of Lactobacillus. Estradiol and psychosocial stress measurements did not differ among the three groups, whereas estradiol and glycogen exhibited a weak positive relationship that was not statistically significant. CONCLUSIONS: The findings of this pilot study suggest that glycogen levels are associated with vaginal community composition in young black women; however, estradiol and psychosocial stress are not. In addition, the results suggest there is no simple relationship between levels of estradiol and the production of vaginal glycogen.

Stability of equilibria in quantitative genetic models based on modified-gradient systems.

Motivated by questions in biology, we investigate the stability of equilibria of the dynamical system [Formula: see text] which arise as critical points of f, under the assumption that [Formula: see text] is positive semi-definite. It is shown that the condition [Formula: see text], where [Formula: see text] is the smallest eigenvalue of [Formula: see text], plays a key role in guaranteeing uniform asymptotic stability and in providing information on the basis of attraction of those equilibria.

Modeling time-series data from microbial communities.

As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.