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The IL-17A inhibitor secukinumab is efficacious for the treatment of psoriasis. To better understand its mechanism of action, we investigated its impact on psoriatic lesions from 15 patients with moderate-to-severe plaque psoriasis undergoing secukinumab treatment. We characterized the longitudinal transcriptomic changes of whole lesional skin tissue as well as cutaneous CD4+ and CD8+ T effector cells and CD4+ T regulatory cells across 12 weeks of treatment. Secukinumab was clinically effective and reduced disease-associated overexpression of IL17A , IL17F, IL23A, IL23R, and IFNG in whole tissue as soon as 2 weeks after initiation of treatment. IL17A overexpression in T-cell subsets, primarily CD8+ T cells, was also reduced. Although secukinumab treatment resolved 89‒97% of psoriasis-associated expression differences in bulk tissue and T-cell subsets by week 12 of treatment, we observed expression differences involved in IFN signaling and metallothionein synthesis that remained unresolved at this time point as well as potential treatment-associated expression differences involved in IL-15 signaling. These changes were accompanied by shifts in broader immune cell composition on the basis of deconvolution of RNA-sequencing data. In conclusion, our study reveals several phenotypic and cellular changes within the lesion that underlie clinical improvement from secukinumab.

Psoriasis is a chronic, inflammatory skin disease that affects 2‒3% of the global population. Besides skin manifestations, patients with psoriasis have increased susceptibility to a number of comorbidities, including psoriatic arthritis, cardiovascular disease, and inflammatory bowel disease. To understand the systemic component of psoriasis pathogenesis, we performed a pilot study to examine the fecal metagenome, host colonic transcriptome, and host peripheral blood immune profiles of patients with psoriasis and healthy controls. Our study showed increased functional diversity in the gut microbiome of patients with psoriasis. In addition, we identified microbial species that preferentially associate with patients with psoriasis and which have been previously found to associate with other autoimmune diseases. Intriguingly, our data revealed three psoriasis subgroups that have distinct microbial and host features. Integrating these features revealed host‒microbe associations that are specific to psoriasis or particular psoriasis subgroups. Our findings provide insight into the factors that may affect the development of comorbidities in patients with psoriasis and may hold diagnostic potential for early identification of patients with psoriasis at risk for these comorbidities.

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

Ankylosing spondylitis (AS) is an immune-mediated inflammatory disorder that primarily affects the axial skeleton, especially the sacroiliac joints and spine. This results in chronic back pain and, in extreme cases, ankylosis of the spine. Despite its debilitating effects, the pathogenesis of AS remains to be further elucidated. This study used single cell CITE-seq technology to analyze peripheral blood mononuclear cells (PBMCs) in AS and in healthy controls. We identified a number of molecular features associated with AS. CD52 was found to be overexpressed in both RNA and surface protein expression across several cell types in patients with AS. CD16+ monocytes overexpressed TNFSF10 and IL-18Rα in AS, while CD8+ TEM cells and natural killer cells overexpressed genes linked with cytotoxicity, including GZMH, GZMB, and NKG7. Tregs underexpressed CD39 in AS, suggesting reduced functionality. We identified an overrepresented NK cell subset in AS that overexpressed CD16, CD161, and CD38, as well as cytotoxic genes and pathways. Finally, we developed machine learning models derived from CITE-seq data for the classification of AS and achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of > 0.95. In summary, CITE-seq identification of AS-associated genes and surface proteins in specific cell subsets informs our understanding of pathogenesis and potential new therapeutic targets, while providing new approaches for diagnosis via machine learning.

Regulatory T cells (Tregs) reside in nonlymphoid tissues where they carry out unique functions. The molecular mechanisms responsible for Treg accumulation and maintenance in these tissues are relatively unknown. Using an unbiased discovery approach, we identified LAYN (layilin), a C-type lectin-like receptor, to be preferentially and highly expressed on a subset of activated Tregs in healthy and diseased human skin. Expression of layilin on Tregs was induced by TCR-mediated activation in the presence of IL-2 or TGF-β. Mice with a conditional deletion of layilin in Tregs had reduced accumulation of these cells in tumors. However, these animals somewhat paradoxically had enhanced immune regulation in the tumor microenvironment, resulting in increased tumor growth. Mechanistically, layilin expression on Tregs had a minimal effect on their activation and suppressive capacity in vitro. However, expression of this molecule resulted in a cumulative anchoring effect on Treg dynamic motility in vivo. Taken together, our results suggest a model whereby layilin facilitates Treg adhesion in skin and, in doing so, limits their suppressive capacity. These findings uncover a unique mechanism whereby reduced Treg motility acts to limit immune regulation in nonlymphoid organs and may help guide strategies to exploit this phenomenon for therapeutic benefit.

BackgroundPsoriasis is an inflammatory, IL-17-driven skin disease in which autoantigen-induced CD8+ T cells have been identified as pathogenic drivers.ObjectiveOur study focused on comprehensively characterizing the phenotypic variation of CD8+ T cells in psoriatic lesions.MethodsWe used single-cell RNA sequencing to compare CD8+ T-cell transcriptomic heterogeneity between psoriatic and healthy skin.ResultsWe identified 11 transcriptionally diverse CD8+ T-cell subsets in psoriatic and healthy skin. Among several inflammatory subsets enriched in psoriatic skin, we observed 2 Tc17 cell subsets that were metabolically divergent, were developmentally related, and expressed CXCL13, which we found to be a biomarker of psoriasis severity and which achieved comparable or greater accuracy than IL17A in a support vector machine classifier of psoriasis and healthy transcriptomes. Despite high coinhibitory receptor expression in the Tc17 cell clusters, a comparison of these cells with melanoma-infiltrating CD8+ T cells revealed upregulated cytokine, cytolytic, and metabolic transcriptional activity in the psoriatic cells that differed from an exhaustion program.ConclusionUsing high-resolution single-cell profiling in tissue, we have uncovered the diverse landscape of CD8+ T cells in psoriatic and healthy skin, including 2 nonexhausted Tc17 cell subsets associated with disease severity.

BackgroundSleep dysfunction and sleep disorders are important comorbidities of psoriasis. Not only do these sleep comorbidities contribute to reduced quality of life, but they may also lead to worsening psoriasis and increased susceptibility to cardiometabolic diseases. While psoriasis and sleep dysfunction are thought to be linked by itch, depression, and immune system dysregulation, the relationship between psoriasis and sleep dysfunction is not yet fully understood.ObjectiveWe sought to compare previous studies characterizing the gut microbiome in psoriasis and sleep dysfunction and examine the potential relevance of shared findings on cardiometabolic and overall health.MethodsWe performed literature searches of PubMed and Embase databases to find studies evaluating the gut microbiome in psoriasis, sleep dysfunction, and cardiometabolic diseases.ResultsStudies characterizing the gut microbiome in psoriasis and sleep dysfunction reveal shared findings, specifically an increased Firmicutes to Bacteroidetes ratio and reduced abundance of short chain fatty acid-producing bacteria. These dysbiotic features have also been shown to promote systemic inflammation and cardiometabolic disease.ConclusionIn favoring an increased Firmicutes to Bacteroidetes ratio and reduced abundance of short chain fatty acid-producing bacteria, sleep dysfunction could be contributing to worsening psoriasis and cardiometabolic comorbidities through intestinal dysbiosis. Future studies are needed to determine whether gut- and sleep-targeting interventions could be therapeutic in psoriasis patients with poor sleep.

BackgroundPsoriasis impacts 1-3% of the world's population and is characterized by hyper-proliferation of keratinocytes and increased inflammation. At the molecular level, psoriasis is commonly driven by a Th17 response, which serves as a major therapeutic target. Microbiome perturbations have been associated with several immune-mediated diseases such as atopic dermatitis, asthma, and multiple sclerosis. Although a few studies have investigated the association between the skin microbiome and psoriasis, conflicting results have been reported plausibly due to the lack of standardized sampling and profiling protocols, or to inherent microbial variability across human subjects and underpowered studies. To better understand the link between the cutaneous microbiota and psoriasis, we conducted an analysis of skin bacterial communities of 28 psoriasis patients and 26 healthy subjects, sampled at six body sites using a standardized protocol and higher sequencing depth compared to previous studies. Mouse studies were employed to examine dermal microbial-immune interactions of bacterial species identified from our study.ResultsSkin microbiome profiling based on sequencing the 16S rRNA V1-V3 variable region revealed significant differences between the psoriasis-associated and healthy skin microbiota. Comparing the overall community structures, psoriasis-associated microbiota displayed higher diversity and more heterogeneity compared to healthy skin bacterial communities. Specific microbial signatures were associated with psoriatic lesional, psoriatic non-lesional, and healthy skin. Specifically, relative enrichment of Staphylococcus aureus was strongly associated with both lesional and non-lesional psoriatic skin. In contrast, Staphylococcus epidermidis and Propionibacterium acnes were underrepresented in psoriatic lesions compared to healthy skin, especially on the arm, gluteal fold, and trunk. Employing a mouse model to further study the impact of cutaneous Staphylcoccus species on the skin T cell differentiation, we found that newborn mice colonized with Staphylococcus aureus demonstrated strong Th17 polarization, whereas mice colonized with Staphylococcus epidermidis or un-colonized controls showed no such response.ConclusionOur results suggest that microbial communities on psoriatic skin is substantially different from those on healthy skin. The psoriatic skin microbiome has increased diversity and reduced stability compared to the healthy skin microbiome. The loss of community stability and decrease in immunoregulatory bacteria such as Staphylococcus epidermidis and Propionibacterium acnes may lead to higher colonization with pathogens such as Staphylococcus aureus, which could exacerbate cutaneous inflammation along the Th17 axis.

It has long been recognized that anatomic location is an important feature for defining distinct subtypes of plaque psoriasis. However, little is known about the molecular differences between scalp, palmoplantar, and conventional plaque psoriasis. To investigate the molecular heterogeneity of these psoriasis subtypes, we performed RNA-seq and flow cytometry on skin samples from individuals with scalp, palmoplantar, and conventional plaque psoriasis, along with samples from healthy control patients. We performed differential expression analysis and network analysis using weighted gene coexpression network analysis (WGCNA). Our analysis revealed a core set of 763 differentially expressed genes common to all sub-types of psoriasis. In contrast, we identified 605, 632, and 262 genes uniquely differentially expressed in conventional, scalp, and palmoplantar psoriasis, respectively. WGCNA and pathway analysis revealed biological processes for the core genes as well as subtype-specific genes. Flow cytometry analysis revealed a shared increase in the percentage of CD4+ T regulatory cells in all psoriasis subtypes relative to controls, whereas distinct psoriasis subtypes displayed differences in IL-17A, IFN-gamma, and IL-22 production. This work reveals the molecular heterogeneity of plaque psoriasis and identifies subtype-specific signaling pathways that will aid in the development of therapy that is appropriate for each subtype of plaque psoriasis.

Recent studies have suggested that the intestinal microbiome plays an important role in modulating risk of several chronic diseases, including inflammatory bowel disease, obesity, type 2 diabetes, cardiovascular disease, and cancer. At the same time, it is now understood that diet plays a significant role in shaping the microbiome, with experiments showing that dietary alterations can induce large, temporary microbial shifts within 24 h. Given this association, there may be significant therapeutic utility in altering microbial composition through diet. This review systematically evaluates current data regarding the effects of several common dietary components on intestinal microbiota. We show that consumption of particular types of food produces predictable shifts in existing host bacterial genera. Furthermore, the identity of these bacteria affects host immune and metabolic parameters, with broad implications for human health. Familiarity with these associations will be of tremendous use to the practitioner as well as the patient.

PurposeTo understand the changes in the microbiome in psoriatic disease, we conducted a systematic review of studies comparing the skin and gut microbiota in psoriatic individuals and healthy controls.FindingsOur review of studies pertaining to the cutaneous microbiome showed a trend towards an increased relative abundance of Streptococcus and a decreased level of Propionibacterium in psoriasis patients compared to controls. In the gut microbiome, the ratio of Firmicutes and Bacteroidetes was perturbed in psoriatic individuals compared to healthy controls. Actinobacteria was also relatively underrepresented in psoriasis patients relative to healthy individuals.SummaryAlthough the field of the psoriatic microbiome is relatively new, these first studies reveal interesting differences in microbiome composition that may be associated with the development of psoriatic comorbidities and serve as novel therapeutic targets.

Metabolomics is an emerging new "omics" field involving the systematic analysis of the metabolites in a biologic system. These metabolites provide a molecular snapshot of cellular activity and are thus important for understanding the functional changes in metabolic pathways that drive disease. Recently, metabolomics has been used to study the local and systemic metabolic changes in psoriasis and its cardiometabolic comorbidities. Such studies have revealed novel insights into disease pathogenesis and suggest new biochemical signatures that may be used as a marker of psoriatic disease. This review will discuss common strategies in metabolomics analysis, current findings in the metabolomics of psoriasis, and emerging trends in psoriatic metabolomics.

This article compares distribution functions among pairs of locations in their domains, in contrast to the typical approach of univariate comparison across individual locations. This bivariate approach is studied in the presence of sampling bias, which has been gaining attention in COVID-19 studies that over-represent more symptomatic people. In cases with either known or unknown sampling bias, we introduce Anderson–Darling-type tests based on both the univariate and bivariate formulation. A simulation study shows the superior performance of the bivariate approach over the univariate one. We illustrate the proposed methods using real data on the distribution of the number of symptoms suggestive of COVID-19. [ABSTRACT FROM AUTHOR]

In data analysis using dimension reduction methods, the main goal is to summarize how the response is related to the covariates through a few linear combinations. One key issue is to determine the number of independent, relevant covariate combinations, which is the dimension of the sufficient dimension reduction (SDR) subspace. In this work, we propose an easily‐applied approach to conduct inference for the dimension of the SDR subspace, based on augmentation of the covariate set with simulated pseudo‐covariates. Applying the partitioning principal to the possible dimensions, we use rigorous sequential testing to select the dimensionality, by comparing the strength of the signal arising from the actual covariates to that appearing to arise from the pseudo‐covariates. We show that under a "uniform direction" condition, our approach can be used in conjunction with several popular SDR methods, including sliced inverse regression. In these settings, the test statistic asymptotically follows a beta distribution and therefore is easily calibrated. Moreover, the family‐wise type I error rate of our sequential testing is rigorously controlled. Simulation studies and an analysis of newborn anthropometric data demonstrate the robustness of the proposed approach, and indicate that the power is comparable to or greater than the alternatives. [ABSTRACT FROM AUTHOR]

Representative interpretation criteria are examined to evaluate the capacity of drilled shaft foundations under axial uplift loading. A wide variety of uplift load test data are used, and these data are divided into drained and undrained databases. The interpretation criteria are applied to these load test data to establish a consistent uplift interpretation criterion. The results are comparable for both drained and undrained loading. In general, the undrained load test results show somewhat less variability than the drained results. Based on these analyses, the [Q.sub.L2], [Q.sub.0.5in], and slope tangent methods are the more reliable and consistent, and specific design recommendations for the interpretation of uplift drilled shaft load test are given, in terms of both capacity and displacement. CE Database subject headings: Drilled shafts; Foundations; Uplift; Load tests; Displacement.

In this work, we describe a novel operation of charge-injection-induced error-free charge-based capacitance measurement (CIEF CBCM) method. This method has the simplest test structure among various CBCM methods by using only one N/PMOS pair. CIEF CBCM has the advantage of being free from charge-injection-induced errors and of efficient layout area usage. It is very suitable for industrial applications for large amounts of accurate capacitance characterizations with a limited layout area. Besides, ClEF CBCM is also implemented for investigating the impact of floating dummy metal fills on interconnect capacitance directly from silicon data. Index Terms--Capacitance measurement, charge-based capacitance measurement (CBCM), charge injection, interconnect capacitance.

The effectiveness of a block scheduling program for Algebra students at a Central Texas high school is studied. Results show that failure rates in block classes were higher than those in traditional classes for the school years 1994-95 and 1995-96. However, it was found that most of the students in the block and traditional classes who failed in Algebra also failed in their other classes.