Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data.

Multiplexed spatial proteomics reveals the spatial organization of cells in tumors, which is associated with important clinical outcomes such as survival and treatment response. This spatial organization is often summarized using spatial summary statistics, including Ripley's K and Besag's L. However, if multiple regions of the same tumor are imaged, it is unclear how to synthesize the relationship with a single patient-level endpoint. We evaluate extant approaches for accommodating multiple images within the context of associating summary statistics with outcomes. First, we consider averaging-based approaches wherein multiple summaries for a single sample are combined in a weighted mean. We then propose a novel class of ensemble testing approaches in which we simulate random weights used to aggregate summaries, test for an association with outcomes, and combine the $P$-values. We systematically evaluate the performance of these approaches via simulation and application to data from non-small cell lung cancer, colorectal cancer, and triple negative breast cancer. We find that the optimal strategy varies, but a simple weighted average of the summary statistics based on the number of cells in each image often offers the highest power and controls type I error effectively. When the size of the imaged regions varies, incorporating this variation into the weighted aggregation may yield additional power in cases where the varying size is informative. Ensemble testing (but not resampling) offered high power and type I error control across conditions in our simulated data sets.

Neuronal splicing of the unmethylated histone H3K4 reader, PHF21A, prevents excessive synaptogenesis.

PHF21A is a histone-binding protein that recognizes unmethylated histone H3K4, the reaction product of LSD1 histone demethylase. PHF21A and LSD1 form a complex, and both undergo neuron-specific microexon splicing. The PHF21A neuronal microexon interferes with nucleosome binding, whereas the LSD1 neuronal microexon weakens H3K4 demethylation activity and can alter the substrate specificity to H3K9 or H4K20. However, the temporal expression patterns of PHF21A and LSD1 splicing isoforms during brain development and their biological roles remain unknown. In this work, we report that neuronal PHF21A isoform expression precedes neuronal LSD1 expression during human neuron differentiation and mouse brain development. The asynchronous splicing events resulted in stepwise deactivation of the LSD1-PHF21A complex in reversing H3K4 methylation. An unbiased proteomics survey revealed that the enzymatically inactive LSD1-PHF21A complex interacts with neuron-specific binding partners, including MYT1-family transcription factors and post-transcriptional mRNA processing proteins such as VIRMA. The interaction with the neuron-specific components, however, did not require the PHF21A microexon, indicating that the neuronal proteomic milieu, rather than the microexon-encoded PHF21A segment, is responsible for neuron-specific complex formation. Finally, by using two Phf21a mutant mouse models, we found that Phf21a neuronal splicing prevents excess synapse formation that otherwise would occur when canonical PHF21A is expressed in neurons. These results suggest that the role of the PHF21A microexon is to dampen LSD1-mediated H3K4 demethylation, thereby containing aberrant synaptogenesis.

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

MOTIVATION: 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. RESULTS: 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 ovarian and lung cancer studies. AVAILABILITY AND IMPLEMENTATION: An R package and tutorial are provided at https://github.com/sarahsamorodnitsky/SPOT.

A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data.

MOTIVATION: T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed. RESULTS: In this paper, we develop an innovative "multi-bin" rarefying approach that partitions samples into multiple bins according to their library sizes, conducts rarefying within each bin for alpha diversity calculations, and performs meta-analysis across bins. Extensive simulations using real-world data highlight the inadequacy of the overall rarefying approach in controlling the confounding effect of library size. Our method proves robust in addressing library size confounding, outperforming competing normalization strategies by achieving better-controlled type-I error rates and enhanced statistical power in association tests. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/mli171/MultibinAlpha. The datasets are freely available at https://doi.org/10.21417/B7001Z and https://doi.org/10.21417/AR2019NC.

Preclinical studies of gene replacement therapy for CDKL5 deficiency disorder.

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is a rare neurodevelopmental disorder caused by a mutation in the X-linked CDKL5 gene. CDKL5 is a serine/threonine kinase that is critical for axon outgrowth and dendritic morphogenesis as well as synapse formation, maturation, and maintenance. This disorder is characterized by early-onset epilepsy, hypotonia, and failure to reach cognitive and motor developmental milestones. Because the disease is monogenic, delivery of the CDKL5 gene to the brain of patients should provide clinical benefit. To this end, we designed a gene therapy vector, adeno-associated virus (AAV)9.Syn.hCDKL5, in which human CDKL5 gene expression is driven by the synapsin promoter. In biodistribution studies conducted in mice, intracerebroventricular (i.c.v.) injection resulted in broader, more optimal biodistribution than did intra-cisterna magna (i.c.m.) delivery. AAV9.Syn.hCDKL5 treatment increased phosphorylation of EB2, a bona fide CDKL5 substrate, demonstrating biological activity in vivo. Our data provide proof of concept that i.c.v. delivery of AAV9.Syn.hCDKL5 to neonatal male Cdkl5 knockout mice reduces pathology and reduces aberrant behavior. Functional improvements were seen at doses of 3e11 to 5e11 vector genomes/g brain, which resulted in transfection of ≥50% of the neurons. Functional improvements were not seen at lower doses, suggesting a requirement for broad distribution for efficacy.

Viral genomic variation and the severity of genital HSV-2 infection as quantified by shedding rate: a viral genome-wide association study.

BACKGROUND: The clinical severity of genital HSV-2 infection varies widely among infected persons with some experiencing frequent genital lesions while others are asymptomatic. The viral genital shedding rate is closely associated with and has been established as a surrogate marker of clinical severity. METHODS: To assess the relationship between viral genetics and shedding, we assembled a set of 145 persons who had the severity of their genital herpes quantified through determination of their HSV genital shedding rate. An HSV-2 sample from each person was sequenced and biallelic variants among these genomes were identified. RESULTS: We found no association between metrics of genome-wide variation in HSV-2 and shedding rate. A viral genome-wide association study (vGWAS) identified the minor alleles of three individual unlinked variants as significantly associated with higher shedding rate (p<8.4x10-5): C44973T (A512T), a non-synonymous variant in UL22 (glycoprotein H); A74534G, a synonymous variant in UL36 (large tegument protein); and T119283C, an intergenic variant. We also found an association between the total number of minor alleles for the significant variants and shedding rate (p=6.6x10-7). CONCLUSIONS: These results add to a growing body of literature for HSV suggesting a connection between viral genetic variation and clinically important phenotypes of infection.

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.

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.

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.

Thinner biological tissues induce leaflet flutter in aortic heart valve replacements.

Valvular heart disease has recently become an increasing public health concern due to the high prevalence of valve degeneration in aging populations. For patients with severely impacted aortic valves that require replacement, catheter-based bioprosthetic valve deployment offers a minimally invasive treatment option that eliminates many of the risks associated with surgical valve replacement. Although recent percutaneous device advancements have incorporated thinner, more flexible biological tissues to streamline safer deployment through catheters, the impact of such tissues in the complex, mechanically demanding, and highly dynamic valvular system remains poorly understood. The present work utilized a validated computational fluid-structure interaction approach to isolate the behavior of thinner, more compliant aortic valve tissues in a physiologically realistic system. This computational study identified and quantified significant leaflet flutter induced by the use of thinner tissues that initiated blood flow disturbances and oscillatory leaflet strains. The aortic flow and valvular dynamics associated with these thinner valvular tissues have not been previously identified and provide essential information that can significantly advance fundamental knowledge about the cardiac system and support future medical device innovation. Considering the risks associated with such observed flutter phenomena, including blood damage and accelerated leaflet deterioration, this study demonstrates the potentially serious impact of introducing thinner, more flexible tissues into the cardiac system.

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.

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.

A method for subtype analysis with somatic mutations.

MOTIVATION: Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancer subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes. However, the low prevalence of individual mutations poses a tremendous challenge to the related statistical analysis. RESULTS: In this article, we propose an approach, subtype analysis with somatic mutations (SASOM), for the association analysis of cancer subtypes with somatic mutations. Our approach tests the association between a set of somatic mutations (from a genetic pathway) and subtypes, while incorporating functional information of the mutations into the analysis. We further propose a robust p-value combination procedure, DAPC, to synthesize statistical significance from different sources. Simulation studies show that the proposed approach has correct type I error and tends to be more powerful than possible alternative methods. In a real data application, we examine the somatic mutations from a cutaneous melanoma dataset, and identify a genetic pathway that is associated with immune-related subtypes. AVAILABILITY AND IMPLEMENTATION: The SASOM R package is available at https://github.com/rksyouyou/SASOM-pkg. R scripts and data are available at https://github.com/rksyouyou/SASOM-analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiRKAT: kernel machine regression-based global association tests for the microbiome.

SUMMARY: Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. AVAILABILITY AND IMPLEMENTATION: MiRKAT is available on CRAN as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Loss of Bardet-Biedl syndrome proteins causes synaptic aberrations in principal neurons.

Bardet-Biedl syndrome (BBS), a ciliopathy, is a rare genetic condition characterised by retinal degeneration, obesity, kidney failure, and cognitive impairment. In spite of progress made in our general understanding of BBS aetiology, the molecular and cellular mechanisms underlying cognitive impairment in BBS remain elusive. Here, we report that the loss of BBS proteins causes synaptic dysfunction in principal neurons, providing a possible explanation for the cognitive impairment phenotype observed in BBS patients. Using synaptosomal proteomics and immunocytochemistry, we demonstrate the presence of Bbs proteins in the postsynaptic density (PSD) of hippocampal neurons. Loss of Bbs results in a significant reduction of dendritic spines in principal neurons of Bbs mouse models. Furthermore, we show that spine deficiency correlates with events that destabilise spine architecture, such as impaired spine membrane receptor signalling, known to be involved in the maintenance of dendritic spines. Our findings suggest a role for BBS proteins in dendritic spine homeostasis that may be linked to the cognitive phenotype observed in BBS.

Correction: Loss of Bardet-Biedl syndrome proteins causes synaptic aberrations in principal neurons.

[This corrects the article DOI: 10.1371/journal.pbio.3000414.].

Bacterial Communities Associated With Abnormal Nugent Score in Postmenopausal Versus Premenopausal Women.

The Nugent score is the reference standard for bacterial vaginosis (BV) diagnosis but has not been validated in postmenopausal women. We compared relative abundances from 16S ribosomal RNA gene sequencing of vaginal microbiota with Nugent score in cohorts of premenopausal (n = 220) and postmenopausal (n = 144) women. In premenopausal women, 33 taxa were significantly correlated with Nugent score, including the classic BV-associated taxa Gardnerella, Atopobium, Sneathia, Megasphaera, and Prevotella. In postmenopausal women, 11 taxa were significantly associated with Nugent score, including Prevotella but no other BV-associated genera. High Nugent scores should not be used to infer BV in postmenopausal women.

Comparison of the Vaginal Microbiota in Postmenopausal Black and White Women.

BACKGROUND: We compared vaginal microbial communities in postmenopausal black and white women. METHODS: Shotgun sequencing of vaginal swabs from postmenopausal women self-identified as black or white was compared using MiRKAT. RESULTS: Vaginal community dominance by Lactobacillus crispatus or Lactobacillusgasseri was more common in 44 postmenopausal black women (n = 12, 27%) than among 44 matched white women (n = 2, 5%; P = .01). No individual taxa were significantly more abundant in either group. CONCLUSIONS: We identified small overall differences in vaginal microbial communities of black and white postmenopausal women. L. crispatus dominance was more common in black women. CLINICAL TRIALS REGISTRATION: NCT02516202 (MsFLASH05) and NCT01418209 (MsFLASH03).

Increased mucosal neutrophil survival is associated with altered microbiota in HIV infection.

Gastrointestinal (GI) mucosal dysfunction predicts and likely contributes to non-infectious comorbidities and mortality in HIV infection and persists despite antiretroviral therapy. However, the mechanisms underlying this dysfunction remain incompletely understood. Neutrophils are important for containment of pathogens but can also contribute to tissue damage due to their release of reactive oxygen species and other potentially harmful effector molecules. Here we used a flow cytometry approach to investigate increased neutrophil lifespan as a mechanism for GI neutrophil accumulation in chronic, treated HIV infection and a potential role for gastrointestinal dysbiosis. We report that increased neutrophil survival contributes to neutrophil accumulation in colorectal biopsy tissue, thus implicating neutrophil lifespan as a new therapeutic target for mucosal inflammation in HIV infection. Additionally, we characterized the intestinal microbiome of colorectal biopsies using 16S rRNA sequencing. We found that a reduced Lactobacillus: Prevotella ratio associated with neutrophil survival, suggesting that intestinal bacteria may contribute to GI neutrophil accumulation in treated HIV infection. Finally, we provide evidence that Lactobacillus species uniquely decrease neutrophil survival and neutrophil frequency in vitro, which could have important therapeutic implications for reducing neutrophil-driven inflammation in HIV and other chronic inflammatory conditions.

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.