A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.

Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripley's K or Besag's L, to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific. We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus p-value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to an ovarian cancer study. An R package and tutorial is provided at https://github.com/sarahsamorodnitsky/SPOT.

Limitation of permutation-based differential correlation analysis.

The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test statistics to capture variations in correlation is challenging, several DCA methods utilize permutation which can loosen parametric (e.g., normality) assumptions. However, permutation is often problematic for DCA due to violating the assumption that samples are exchangeable under the null. Here, we examine the limitations of permutation-based DCA and investigate instances where the permutation-based DCA exhibits poor performance. Experimental results show that the permutation-based DCA often fails to control the type I error under the null hypothesis of equal correlation structures.

Ipilimumab with or without nivolumab in PD-1 or PD-L1 blockade refractory metastatic melanoma: a randomized phase 2 trial.

In this randomized phase 2 trial, blockade of cytotoxic T-lymphocyte protein 4 (CTLA-4) with continuation of programmed death protein 1 (PD-1) blockade in patients with metastatic melanoma who had received front-line anti-PD-1 or therapy against programmed cell death 1 ligand 1 and whose tumors progressed was tested in comparison with CTLA-4 blockade alone. Ninety-two eligible patients were randomly assigned in a 3:1 ratio to receive the combination of ipilimumab and nivolumab, or ipilimumab alone. The primary endpoint was progression-free survival. Secondary endpoints included the difference in CD8 T cell infiltrate among responding and nonresponding tumors, objective response rate, overall survival and toxicity. The combination of nivolumab and ipilimumab resulted in a statistically significant improvement in progression-free survival over ipilimumab (hazard ratio = 0.63, 90% confidence interval (CI) = 0.41-0.97, one-sided P = 0.04). Objective response rates were 28% (90% CI = 19-38%) and 9% (90% CI = 2-25%), respectively (one-sided P = 0.05). Grade 3 or higher treatment-related adverse events occurred in 57% and 35% of patients, respectively, which is consistent with the known toxicity profile of these regimens. The change in intratumoral CD8 T cell density observed in the present analysis did not reach statistical significance to support the formal hypothesis tested as a secondary endpoint. In conclusion, primary resistance to PD-1 blockade therapy can be reversed in some patients with the combination of CTLA-4 and PD-1 blockade. Clinicaltrials.gov identifier: NCT03033576 .

The intratumor microbiome is associated with microsatellite instability.

Intratumoral microbes may have multifunctional roles in carcinogenesis. Microsatellite instability (MSI) is associated with higher tumor immunity and mutational burden. Using whole transcriptome and whole genome sequencing microbial abundance data, we investigated associations of intratumoral microbes with MSI, survival, and MSI-relevant tumor molecular characteristics across multiple cancer types including colorectal cancer (CRC), stomach adenocarcinoma, and endometrial carcinoma. Among 451 CRC patients, our key finding was strong associations of multiple CRC-associated genera, including Dialister and Casatella, with MSI. Dialister and Casatella abundance was associated with improved overall survival (hazard ratiomortality = 0.56, 95% confidence interval = 0.34 to 0.92, and hazard ratiomortality = 0.44, 95% confidence interval = 0.27 to 0.72), respectively, comparing higher relative to lower quantiles. Multiple intratumor microbes were associated with immune genes and tumor mutational burden. Diversity of oral cavity-originating microbes was also associated with MSI among CRC and stomach adenocarcinoma patients. Overall, our findings suggest the intratumor microbiota may differ by MSI status and play a role in influencing the tumor microenvironment.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.

BACKGROUND: Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS. RESULTS: We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios. CONCLUSIONS: Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. Video Abstract.

Vaginal Microbiome Metagenome Inference Accuracy: Differential Measurement Error according to Community Composition.

Several studies have compared metagenome inference performance in different human body sites; however, none specifically reported on the vaginal microbiome. Findings from other body sites cannot easily be generalized to the vaginal microbiome due to unique features of vaginal microbial ecology, and investigators seeking to use metagenome inference in vaginal microbiome research are "flying blind" with respect to potential bias these methods may introduce into analyses. We compared the performance of PICRUSt2 and Tax4Fun2 using paired 16S rRNA gene amplicon sequencing and whole-metagenome sequencing data from vaginal samples from 72 pregnant individuals enrolled in the Pregnancy, Infection, and Nutrition (PIN) cohort. Participants were selected from those with known birth outcomes and adequate 16S rRNA gene amplicon sequencing data in a case-control design. Cases experienced early preterm birth (<32 weeks of gestation), and controls experienced term birth (37 to 41 weeks of gestation). PICRUSt2 and Tax4Fun2 performed modestly overall (median Spearman correlation coefficients between observed and predicted KEGG ortholog [KO] relative abundances of 0.20 and 0.22, respectively). Both methods performed best among Lactobacillus crispatus-dominated vaginal microbiotas (median Spearman correlation coefficients of 0.24 and 0.25, respectively) and worst among Lactobacillus iners-dominated microbiotas (median Spearman correlation coefficients of 0.06 and 0.11, respectively). The same pattern was observed when evaluating correlations between univariable hypothesis test P values generated with observed and predicted metagenome data. Differential metagenome inference performance across vaginal microbiota community types can be considered differential measurement error, which often causes differential misclassification. As such, metagenome inference will introduce hard-to-predict bias (toward or away from the null) in vaginal microbiome research. IMPORTANCE Compared to taxonomic composition, the functional potential within a bacterial community is more relevant to establishing mechanistic understandings and causal relationships between the microbiome and health outcomes. Metagenome inference attempts to bridge the gap between 16S rRNA gene amplicon sequencing and whole-metagenome sequencing by predicting a microbiome's gene content based on its taxonomic composition and annotated genome sequences of its members. Metagenome inference methods have been evaluated primarily among gut samples, where they appear to perform fairly well. Here, we show that metagenome inference performance is markedly worse for the vaginal microbiome and that performance varies across common vaginal microbiome community types. Because these community types are associated with sexual and reproductive outcomes, differential metagenome inference performance will bias vaginal microbiome studies, obscuring relationships of interest. Results from such studies should be interpreted with substantial caution and the understanding that they may over- or underestimate associations with metagenome content.

Accommodating multiple potential normalizations in microbiome associations studies.

BACKGROUND: Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes.  RESULTS: To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well.  CONCLUSIONS: The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.

A fast kernel independence test for cluster-correlated data.

Cluster-correlated data receives a lot of attention in biomedical and longitudinal studies and it is of interest to assess the generalized dependence between two multivariate variables under the cluster-correlated structure. The Hilbert-Schmidt independence criterion (HSIC) is a powerful kernel-based test statistic that captures various dependence between two random vectors and can be applied to an arbitrary non-Euclidean domain. However, the existing HSIC is not directly applicable to cluster-correlated data. Therefore, we propose a HSIC-based test of independence for cluster-correlated data. The new test statistic combines kernel information so that the dependence structure in each cluster is fully considered and exhibits good performance under high dimensions. Moreover, a rapid p value approximation makes the new test fast applicable to large datasets. Numerical studies show that the new approach performs well in both synthetic and real world data.

Batch effects removal for microbiome data via conditional quantile regression.

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

Phylogeny-guided microbiome OTU-specific association test (POST).

BACKGROUND: The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. RESULTS: In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods. CONCLUSIONS: Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN ( https://CRAN.R-project.org/package=POSTm ). Video Abstract.

Race, the Vaginal Microbiome, and Spontaneous Preterm Birth.

Previous studies have investigated the associations between the vaginal microbiome and preterm birth, with the aim of determining whether differences in community patterns meaningfully alter risk and could therefore be the target of intervention. We report on vaginal microbial analysis of a nested case-control subset of the Pregnancy, Infection, and Nutrition (PIN) Study, including 464 White women (375 term birth and 89 spontaneous preterm birth, sPTB) and 360 Black women (276 term birth and 84 sPTB). We found that the microbiome of Black women has higher alpha-diversity, higher abundance of Lactobacillus iners, and lower abundance of Lactobacillus crispatus. However, among women who douche, there were no significant differences in microbiome by race. The sPTB-associated microbiome exhibited a lower abundance of L. crispatus, while alpha diversity and L. iners were not significantly associated with sPTB. For each order of magnitude increase in the normalized relative abundance of L. crispatus, multivariable adjusted odds of sPTB decreased by approximately 20% (odds ratio, 0.81; 95% confidence interval, 0.70, 0.94). When we considered the impact of douching, associations between the microbiome and sPTB were limited to women who do not douche. We also observed strong intercorrelations between a range of maternal factors, including poverty, education, marital status, age, douching, and race, with microbiome effect sizes in the range of 1.8 to 5.2% in univariate models. Therefore, race may simply be a proxy for other socially driven factors that differentiate microbiome community structures. Future work will continue to refine reliable microbial biomarkers for preterm birth across diverse cohorts. IMPORTANCE Approximately 10% of all pregnancies in the United States end in preterm birth, and over 14% of pregnancies end in preterm birth among Black women. Knowledge on the associations between vaginal microbiome and preterm birth is important for understanding the potential cause and assessing risk of preterm birth. Our study is one of the largest studies performed to date to investigate the associations between vaginal microbiome and spontaneous preterm birth (sPTB), with stratified design for Black and White women. We found that the vaginal microbiome was different between Black and White women. The vaginal microbiome was associated with sPTB, and a lower abundance of L. crispatus increased the risk of sPTB independent of racial differences in microbial community structures. Furthermore, we also found that vaginal douching obscured the associations between vaginal microbiome, race, and preterm birth, suggesting that vaginal douching is an important factor to consider in future studies.

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.

BACKGROUND: T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. METHODS: We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. RESULTS: Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. CONCLUSION: The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

Impact of Topical Interventions on the Vaginal Microbiota and Metabolome in Postmenopausal Women: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Postmenopausal women with genitourinary symptoms of menopause are often prescribed vaginal estradiol or moisturizer for symptom improvement, but the impact of these treatments on the local microenvironment is poorly understood. Objective: To compare changes in the vaginal microbiota, metabolome, and pH among women using low-dose vaginal estradiol tablet or low pH moisturizer gel for 12-weeks vs low pH placebo. Design, Setting, and Participants: This is a post hoc prespecified secondary analysis of a 12-week multicenter randomized clinical trial among postmenopausal women with moderate to severe genitourinary symptoms. Women were enrolled between April 2016 and February 2017; final follow-up visits occurred in April 2017. Data were analyzed from November 2018 to July 2021. Interventions: Ten-µg vaginal estradiol plus placebo gel vs placebo tablet plus vaginal moisturizer vs dual placebo. Main Outcomes and Measures: The main outcome measures were changes in the diversity and composition of the vaginal microbiota, changes in the metabolome, and pH. Results: Of 302 postmenopausal women from the parent trial, 144 women (mean [SD] age, 61 [4] years) were included in this analysis. After 12 weeks, the microbiota was dominated with Lactobacillus and Bifidobacterium communities among 36 women (80%) in the estradiol group, compared with 16 women (36%) using moisturizer and 13 women (26%) using placebo (P < .001). The composition of vaginal fluid metabolites also varied after 12-weeks among women in the estradiol group with significant changes in 90 of 171 metabolites measured (53%) (P < .001), including an increase in lactate. The 12-week pH among women in the estradiol group was lower vs placebo (median [IQR] pH, 5 [4.5-6.0] vs 6 [5.5-7.0]; P = .005) but not the moisturizer group vs placebo (median [IQR] pH, 6 [5.5-6.5]; P = .28). There was a decrease in pH from baseline to 12-weeks within the moisturizer (median [IQR] pH, 7 [6.0-7.5] vs 6 [5.5-6.5]; P < .001) and placebo (median [IQR] pH, 7 [7.0-7.5] vs 6 [5.5-7.0]; P < .001) groups. Women with high-diversity bacterial communities at baseline exhibited greater median change in pH compared with women with low-diversity communities (median [IQR] change, -1 [-2 to -0.5] vs -0.3 [-1.1 to 0], P = .007). Conclusions and Relevance: This secondary analysis of a randomized clinical trial found that use of vaginal estradiol tablets resulted in substantial changes in the vaginal microbiota and metabolome with a lowering in pH, particularly in women with high-diversity bacterial communities at baseline. Low pH moisturizer or placebo did not significantly impact the vaginal microbiota or metabolome despite lowering the vaginal pH. Estradiol use may offer additional genitourinary health benefits to postmenopausal women. Trial Registration: ClinicalTrials.gov Identifier: NCT02516202.

Testing microbiome association using integrated quantile regression models.

MOTIVATION: Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes, and within this vein, vast computational tools and methods have been devised for standard binary or continuous outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular (e.g. zero-inflated or mixtures) such that conditional outcome mean is less meaningful. We address this gap by investigating association analysis between microbiome compositions and conditional outcome quantiles. RESULTS: We introduce a new association analysis tool named MiRKAT-IQ within the Microbiome Regression-based Kernel Association Test framework using Integrated Quantile regression models to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features (e.g. microbiome community compositions). Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles over a process, and thus our new MiRKAT-IQ test is robust to both the location of association signals (e.g. mean, variance, median) and the heterogeneous distribution of the outcome. Extensive numerical simulation studies have been conducted to show the validity of the new MiRKAT-IQ test. We demonstrate the potential usefulness of MiRKAT-IQ with applications to actual biological data collected from a previous microbiome study. AVAILABILITY AND IMPLEMENTATION: R codes to implement the proposed methodology is provided in the MiRKAT package, which is available on CRAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vaginal and Extra-Vaginal Bacterial Colonization and Risk for Incident Bacterial Vaginosis in a Population of Women Who Have Sex With Men.

BACKGROUND: Bacterial vaginosis (BV) is a common cause of vaginal discharge and associated with vaginal acquisition of BV-associated bacteria (BVAB). METHODS: We used quantitative polymerase chain reaction assays to determine whether presence or concentrations of BVAB in the mouth, anus, vagina, or labia before BV predict risk of incident BV in 72 women who have sex with men. RESULTS: Baseline vaginal and extra-vaginal colonization with Gardnerella spp, Megasphaera spp, Sneathia spp, BVAB-2, Dialister sp type 2, and other BVAB was more common among subjects with incident BV. CONCLUSIONS: Prior colonization with BVAB is a consistent risk for BV.

Do Genetic Variants Modify the Effect of Smoking on Risk of Preeclampsia in Pregnancy?

OBJECTIVE: Maternal smoking is associated with as much as a 50% reduced risk of preeclampsia, despite increasing risk of other poor pregnancy outcomes that often co-occur with preeclampsia, such as preterm birth and fetal growth restriction. Researchers have long sought to understand whether this perplexing association is biologically based, or a result of noncausal mechanisms. We examined whether smoking-response genes modify the smoking-preeclampsia association to investigate potential biological explanations. STUDY DESIGN: We conducted a nested case-control study within the Norwegian Mother, Father and Child Birth Cohort (1999-2008) of 2,596 mother-child dyads. We used family-based log-linear Poisson regression to examine modification of the maternal smoking-preeclampsia relationship by maternal and fetal single nucleotide polymorphisms involved in cellular processes related to components of cigarette smoke (n = 1,915 with minor allele frequency ≥10%). We further investigated the influence of smoking cessation during pregnancy. RESULTS: Three polymorphisms showed overall (p < 0.001) multiplicative interaction between smoking and maternal genotype. For rs3765692 (TP73) and rs10770343 (PIK3C2G), protection associated with smoking was reduced with two maternal copies of the risk allele and was stronger in continuers than quitters (interaction p = 0.02 for both loci, based on testing 3-level smoking by 3-level genotype). For rs2278361 (APAF1) the inverse smoking-preeclampsia association was eliminated by the presence of a single risk allele, and again the trend was stronger in continuers than in quitters (interaction p = 0.01). CONCLUSION: Evidence for gene-smoking interaction was limited, but differences by smoking cessation warrant further investigation. We demonstrate the potential utility of expanded dyad methods and gene-environment interaction analyses for outcomes with complex relationships between maternal and fetal genotypes and exposures. KEY POINTS: · Maternal and fetal genotype may differentially influence preeclampsia.. · Smoking-related genes did not strongly modify smoking-preeclampsia association.. · Smoking cessation reduced strength of gene by smoking interactions..

Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ).

BACKGROUND: Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates. METHODS: As an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy. RESULTS: Thorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives. CONCLUSIONS: We present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis. Video Abstract.

Image-based patient-specific flow simulations are consistent with stroke in pediatric cerebrovascular disease.

Moyamoya disease (MMD) is characterized by narrowing of the distal internal carotid artery and the circle of Willis (CoW) and leads to recurring ischemic and hemorrhagic stroke. A retrospective review of data from 50 pediatric MMD patients revealed that among the 24 who had a unilateral stroke and were surgically treated, 11 (45.8%) had a subsequent, contralateral stroke. There is no reliable way to predict these events. After a pilot study in Acta-/- mice that have features of MMD, we hypothesized that local hemodynamics are predictive of contralateral strokes and sought to develop a patient-specific analysis framework to noninvasively assess this stroke risk. A pediatric MMD patient with an occlusion in the right middle cerebral artery and a right-sided stroke, who was surgically treated and then had a contralateral stroke, was selected for analysis. By using an unsteady Navier-Stokes solver within an isogeometric analysis framework, blood flow was simulated in the CoW model reconstructed from the patient's postoperative imaging data, and the results were compared with those from an age- and sex-matched control subject. A wall shear rate (WSR) > 60,000 s-1 (about 12 × higher than the coagulation threshold of 5000 s-1 and 9 × higher than control) was measured in the terminal left supraclinoid artery; its location coincided with that of the subsequent postsurgical left-sided stroke. A parametric study of disease progression revealed a strong correlation between the degree of vascular morphology altered by MMD and local hemodynamic environment. The results suggest that an occlusion in the CoW could lead to excessive contralateral WSRs, resulting in thromboembolic ischemic events, and that WSR could be a predictor of future stroke.

Association between postmenopausal vulvovaginal discomfort, vaginal microbiota, and mucosal inflammation.

BACKGROUND: Half of all postmenopausal women report symptoms of vulvar, vaginal, or urinary discomfort with substantial impact on sexual function and quality of life; underlying mechanisms leading to symptoms are poorly understood. OBJECTIVE: To examine the possibility that the vaginal microbiota and/or mucosal immune response contributes to the severity of bothersome vaginal symptoms, we conducted a substudy of samples from a randomized trial of vaginal treatment for genitourinary syndrome of menopause to compare these features between women whose symptoms improved and women whose symptoms did not improve. STUDY DESIGN: This is a secondary analysis of samples collected in a 12-week randomized trial of treatment with vaginal estradiol or moisturizer vs placebo for moderate-severe postmenopausal symptoms of vaginal discomfort. We randomly selected 20 women in each arm with ≥2-point decrease in most bothersome symptom severity (responders) and 20 matched controls with ≤1-point decrease (nonresponders). At 0, 4, and 12 weeks, we characterized vaginal microbiota (16S ribosomal RNA gene sequencing), vaginal fluid metabolites (broad-based metabolomic profiling), vaginal fluid-soluble immune markers (Meso Scale Discovery), pH, and vaginal maturation index. We compared responders with nonresponders at baseline and across all visits using linear mixed models to evaluate associations with microbiota, metabolites, and immune markers, incorporating visit and participant-specific random effects while controlling for treatment arm. RESULTS: Here, the mean age of women was 61 years (n=120), and most women (92%) were White. At enrollment, no significant differences were observed between responders and nonresponders in age, most bothersome symptom type or severity, microbiota composition or diversity, Lactobacillus dominance, metabolome, or immune markers. There was a significant decrease in diversity of the vaginal microbiota in both responders and nonresponders (P<.001) over 12 weeks. Although this change did not differ by responder status, diversity was associated with treatment arm: more women in the estradiol arm (63%) had Lactobacillus-dominant, lower diversity bacterial communities than women in the moisturizer (35%) or dual placebo (23%) arms (P=.001) at 12 weeks. The metabolome, vaginal maturation index, and measured immune markers were not associated with responder status over the 12 weeks but varied by treatment arm. CONCLUSION: Postmenopausal vaginal symptom severity was not significantly associated with vaginal microbiota or mucosal inflammatory markers in this small study. Women receiving vaginal estradiol experienced greater abundance of lactobacilli and lower vaginal pH at end of treatment.