Treatment response to isotretinoin correlates with specific shifts in Cutibacterium acnes strain composition within the follicular microbiome.

There are no drugs as effective as isotretinoin for acne. Deciphering the changes in the microbiome induced by isotretinoin in the pilosebaceous follicle of successfully treated patients can pave the way to identify novel therapeutic alternatives. We determined how the follicular microbiome changes with isotretinoin and identified which alterations correlate with a successful treatment response. Whole genome sequencing was done on casts from facial follicles of acne patients sampled before, during and after isotretinoin treatment. Alterations in the microbiome were assessed and correlated with treatment response at 20 weeks as defined as a 2-grade improvement in global assessment score. We investigated the α-diversity, ß-diversity, relative abundance of individual taxa, Cutibacterium acnes strain composition and bacterial metabolic profiles with a computational approach. We found that increased ß-diversity of the microbiome coincides with a successful treatment response to isotretinoin at 20 weeks. Isotretinoin selectively altered C. acnes strain diversity in SLST A and D clusters, with increased diversity in D1 strains correlating with a successful clinical response. Isotretinoin significantly decreased the prevalence of KEGG Ontology (KO) terms associated with four distinct metabolic pathways inferring that follicular microbes may have limited capacity for growth or survival following treatment. Importantly, these alterations in microbial composition or metabolic profiles were not observed in patients that failed to achieve a successful response at 20 weeks. Alternative approaches to recapitulate this shift in the balance of C. acnes strains and microbiome metabolic function within the follicle may be beneficial in the future treatment of acne.

Evolution of the facial skin microbiome during puberty in normal and acne skin.

BACKGROUND: The composition of the skin microbiome varies from infancy to adulthood and becomes most stable in adulthood. Adult acne patients harbour an 'acne microbiome' dominated by specific strains of Cutibacterium acnes. However, the precise timing of skin microbiome evolution, the development of the acne microbiome, and the shift to virulent C. acnes strain composition during puberty is unknown. OBJECTIVES: We performed a cross-sectional pilot study in a paediatric population to understand how and when the skin microbiome composition transitions during puberty and whether a distinct 'acne microbiome' emerges in paediatric subjects. METHODS: Forty-eight volunteers including males and females, ages 7-17 years, with and without acne were enrolled and evaluated for pubertal development using the Tanner staging criteria. Sebum levels were measured, and skin microbiota were collected by sterile swab on the subject's forehead. DNA was sequenced by whole genome shotgun sequencing. RESULTS: A significant shift in microbial diversity emerged between early (T1-T2) and late (T3-T5) stages of puberty, coinciding with increased sebum production on the face. The overall relative abundance of C. acnes in both normal and acne skin increased during puberty and individual C. acnes strains were uniquely affected by pubertal stage and the presence of acne. Further, an acne microbiome signature associated with unique C. acnes strain composition and metabolic activity emerges in late puberty in those with acne. This unique C. acnes strain composition is predicted to have increased porphyrin production, which may contribute to skin inflammation. CONCLUSIONS: Our data suggest that the stage of pubertal development influences skin microbiome composition. As children mature, a distinct acne microbiome composition emerges in those with acne. Understanding how both puberty and acne influence the microbiome may support novel therapeutic strategies to combat acne in the paediatric population.

Variant-set association test for generalized linear mixed model.

Advances in high-throughput biotechnologies have culminated in a wide range of omics (such as genomics, epigenomics, transcriptomics, metabolomics, and metagenomics) studies, and increasing evidence in these studies indicates that the biological architecture of complex traits involves a large number of omics variants each with minor effects but collectively accounting for the full phenotypic variability. Thus, a major challenge in many "ome-wide" association analyses is to achieve adequate statistical power to identify multiple variants of small effect sizes, which is notoriously difficult for studies with relatively small-sample sizes. A small-sample adjustment incorporated in the kernel machine regression framework was proposed to solve this for association studies under various settings. However, such an adjustment in the generalized linear mixed model (GLMM) framework, which accounts for both sample relatedness and non-Gaussian outcomes, has not yet been attempted. In this study, we fill this gap by extending small-sample adjustment in kernel machine association test to GLMM. We propose a new Variant-Set Association Test (VSAT), a powerful and efficient analysis tool in GLMM, to examine the association between a set of omics variants and correlated phenotypes. The usefulness of VSAT is demonstrated using both numerical simulation studies and applications to data collected from multiple association studies. The software for implementing the proposed method in R is available at https://www.github.com/jchen1981/SSKAT.

A unified DNA sequence and non-DNA sequence mapping model of complex traits.

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non-DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed of randomly sampling maternal plants from a natural population and their half-sib seeds. This design forms a two-level hierarchical platform for a joint linkage-linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence-based effects and non-DNA sequence-based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non-DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA-based effects and non-DNA sequence-based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.

Adaptive and powerful microbiome multivariate association analysis via feature selection.

The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods' susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

Anterior, posterior, and nonkeratometric contributions to refractive astigmatism in pseudophakes.

PURPOSE: To investigate the relationship between measured anterior (ACA) and posterior (PCA) keratometric astigmatism and postoperative refractive astigmatism (RA) and to quantify noncorneal astigmatism (NCA) contributions to RA. SETTING: Penn State College of Medicine, Hershey, Pennsylvania, USA. DESIGN: Retrospective consecutive case series. METHODS: Consecutive eyes underwent preoperative biometry (IOLMaster 700) and tomography/topography using a dual Scheimpflug-placido disk-based device (Galilei G4), cataract surgery with implantation of a monofocal intraocular lens (IOL), and postoperative manifest refractions. RA was compared with keratometric astigmatism using the following methods: IOLMaster, SimK, CorT, SimK + measured PCA, total corneal power at the corneal plane (TCP2), and CorT(Total). An ocular residual astigmatism (ORA) vector was calculated between RA and each measured astigmatism. RESULTS: Analysis was based on 296 eyes. ORA centroids were 0.28 @ 179, 0.45 @ 001, 0.37 @ 001, 0.19 @ 003, 0.19 @ 001, and 0.23 @ 178 diopter (D) for the 6 aforementioned methods, respectively (P < .000001 [ORAx, ORAy]). Based on TCP2 measurements, eyes with against-the-rule ACA and with-the-rule (WTR) ACA had ORA centroids of 0.09 @ 082 and 0.58 @ 001 D (P < .000001 [ORAx, ORAy]), respectively. ORA was nonzero and not entirely explained by the cornea, especially in those with WTR ACA. CONCLUSIONS: Total keratometric astigmatism did not explain all ocular astigmatism. Noncorneal contributions were significant, especially in eyes with WTR ACA.

Surgical Management of Vestibular Schwannoma: Practice Pattern Analysis via NSQIP.

OBJECTIVE: Characterize current perspectives in the surgical management of vestibular schwannoma (VS) to guide otolaryngologists in understanding United States practice patterns. METHODS: A retrospective analysis of ACS-NSQIP database was performed to abstract all patients from 2008 to 2016 who underwent VS resection using ICD-9/10 codes 225.1 and D33.3, respectively. The specific surgical approach employed was identified via CPT codes 61520, 61526/61596, and 61591, which represent retrosigmoid (RS), translabyrinthine (TL) and middle cranial fossa (MCF) approaches, respectively. Analyzed outcomes include general surgical complications, total length of stay, and reoperation. RESULTS: A total of 1671 VS cases were identified, 1266 (75.7%) were RS, 292 (17.5%) were TL, and 114 (6.8%) were MCF. The annual number of cases increased over the study period from 15 to 375, which is chiefly attributed to increased institutional participation in ACS-NSQIP. Perioperative variables including BMI (P < .001), ASA class (P = .004), ethnicity (P = .008), operative time (P < .001), and reoperation (P < .001) were found to be statistically significant between cohorts. Increased utilization of RS approach was consistent over the entire study period, with significantly more RS performed than either TL or MCF. Finally, a statistically significant difference with respect to general surgical complication rates was not noted between surgical approaches. CONCLUSIONS: There is increased employment of RS approach for the operative management of VS, which likely is the result of increased reliance on both stereotactic radiosurgery and observation as alternative treatment strategies.

Safety of bariatric surgery in patients with coronary artery disease.

BACKGROUND: Patients with obesity and established coronary artery disease (CAD) may benefit from surgical weight loss; however, its safety is unknown in this population. OBJECTIVE: To assess the association between CAD and the incidence of 30-day postoperative mortality and major adverse cardiac events (MACEs) in patients undergoing bariatric surgery. SETTINGS: Multicenter cohort study. METHODS: We used the 2017 MBSAQIP database to study patients undergoing bariatric surgery from accredited centers in the United States and Canada between Jan 1, 2017, and Dec 31, 2017. Multivariate logistic regression was used to determine whether established CAD was independently associated with 30-day mortality and MACE, a composite endpoint that included myocardial infarction and/or cardiac arrest. RESULTS: We reviewed data from 167,819 patients from 832 centers. There were 4172 patients with diagnosed CAD, and 163,647 without it. At 30-day follow-up, the endpoints of mortality, cardiac arrest, myocardial infarction, and MACE occurred in 172 (.10%), 82 (.05%), 57 (.03%), and 135 (.08%) patients, respectively. The endpoints occurred more significantly in patients with CAD compared with patients without CAD; 22 (.53%) versus 150 (.09%) for mortality, 13 (.31%) versus 69 (.04%) for cardiac arrest, 17 (.41%) versus 40 (.02%) for myocardial infarction, and 28 (.67%) versus 107 (.07%) for MACE (P < .001 for all comparisons). CONCLUSIONS: Postoperative mortality and MACE following bariatric surgery are higher in patients with CAD than those without; however, the absolute incidence is low (<1%). The decision to pursue bariatric surgery and/or preoperative cardiac testing in patients with CAD should include consideration of the overall incidence of adverse cardiac outcomes and the stability of CAD.

An Adaptive Multivariate Two-Sample Test With Application to Microbiome Differential Abundance Analysis.

Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential abundance analysis: the individual-based univariate differential abundance analysis and the group-based multivariate differential abundance analysis. The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. The multivariate analysis evaluates the overall shift in the abundance of microbiome composition between two conditions, which provides useful preliminary differential information for the necessity of follow-up validation studies. In this paper, we present a novel Adaptive multivariate two-sample test for Microbiome Differential Analysis (AMDA) to examine whether the composition of a taxa-set are different between two conditions. Our simulation studies and real data applications demonstrated that the AMDA test was often more powerful than several competing methods while preserving the correct type I error rate. A free implementation of our AMDA method in R software is available at https://github.com/xyz5074/AMDA.