Multiple courses of steroid pulse therapy are required in treating acquired idiopathic generalized anhidrosis patients with a large anhidrotic area: A retrospective study of 28 cases.

Patients with acquired idiopathic generalized anhidrosis (AIGA) demonstrate a sudden loss of sweating function without neurological or endocrine abnormalities. The main treatment is steroid pulse therapy. However, the number of courses required for improvement has been unclear. This study aims to clarify the factors associated with AIGA disease severity and with AIGA patients' responses to steroid pulse therapy. We retrospectively analysed the clinical information of 28 patients with AIGA in our department from the last 10 years. Univariate analysis revealed that patients with a large anhidrotic area need multiple courses of steroid pulse therapy.

Hair follicle stem cell progeny heal blisters while pausing skin development.

Injury in adult tissue generally reactivates developmental programs to foster regeneration, but it is not known whether this paradigm applies to growing tissue. Here, by employing blisters, we show that epidermal wounds heal at the expense of skin development. The regenerated epidermis suppresses the expression of tissue morphogenesis genes accompanied by delayed hair follicle (HF) growth. Lineage tracing experiments, cell proliferation dynamics, and mathematical modeling reveal that the progeny of HF junctional zone stem cells, which undergo a morphological transformation, repair the blisters while not promoting HF development. In contrast, the contribution of interfollicular stem cell progeny to blister healing is small. These findings demonstrate that HF development can be sacrificed for the sake of epidermal wound regeneration. Our study elucidates the key cellular mechanism of wound healing in skin blistering diseases.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.

Therapeutic base editing and prime editing of COL7A1 mutations in recessive dystrophic epidermolysis bullosa.

Recessive dystrophic epidermolysis bullosa (RDEB) is a severe skin fragility disorder caused by loss-of-function mutations in the COL7A1 gene, which encodes type VII collagen (C7), a protein that functions in skin adherence. From 36 Korean RDEB patients, we identified a total of 69 pathogenic mutations (40 variants without recurrence), including point mutations (72.5%) and insertion/deletion mutations (27.5%). For fibroblasts from two patients (Pat1 and Pat2), we applied adenine base editors (ABEs) to correct the pathogenic mutation of COL7A1 or to bypass a premature stop codon in Pat1-derived primary fibroblasts. To expand the targeting scope, we also utilized prime editors (PEs) to correct the COL7A1 mutations in Pat1- and Pat2-derived fibroblasts. Ultimately, we found that transfer of edited patient-derived skin equivalents (i.e., RDEB keratinocytes and PE-corrected RDEB fibroblasts from the RDEB patient) into the skin of immunodeficient mice led to C7 deposition and anchoring fibril formation within the dermal-epidermal junction, suggesting that base editing and prime editing could be feasible strategies for ex vivo gene editing to treat RDEB.

Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis.

Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by improving the probability of success and increasing process efficiency. One promising approach to this challenge is so-called "in silico drug discovery," which is drug discovery utilizing information and communications technologies (ICT) such as artificial intelligence (AI) and molecular simulation. In recent years, ICT-based science and technology, such as bioinformatics, systems biology, cheminformatics, and molecular simulation, which have been developed mainly in the life science and chemistry fields, have changed the face of drug development. AI-based methods have been developed in the drug discovery process, mainly in relation to drug target discovery and pharmacokinetic analysis. In drug target discovery, an in silico method has been developed that uses a probabilistic framework that eliminates the problems of conventional experimental approaches and provides a key to understanding the pathways and mechanisms from compounds to phenotypes. In the field of pharmacokinetic analysis, we have seen the development of a method using nonclinical data to predict human pharmacokinetic parameters, which are important for predicting drug efficacy and toxicity in clinical trials. In this article, we provide an overview of these methods.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Bioactive substances in the stratum corneum of the epidermis found as indicators of skin damage due to sun exposure.

BACKGROUND/PURPOSE: Although an inflammatory response upon acute injury caused by ultraviolet radiation (UV) can be observed immediately, the influence of long-term, repetitive low-dose UV exposure on the skin cannot be precisely perceived, making early detection of chronic damage difficult. This study investigated bioactive substances in the stratum corneum as a potential early and sensitive indicator of the influence of sun exposure on the skin using receiver operating characteristic (ROC) analysis. METHODS: Receiver operating characteristic analysis was performed to assess the responsiveness of cytokines [interleukin (IL)-1α, IL-1 receptor antagonist (IL-1ra), IL-10, tumor necrosis factor (TNF)-α], BCL2-associated protein X (Bax), Toll-like receptor (TLR)3, and TLR4 in the stratum corneum of healthy people exposed (dorsum of the hand) and unexposed (inner arm) to UV. Sunscreen was applied to patients with photodermatosis for 4 weeks to evaluate changes in IL-1ra/IL-1α, TNF-α, Bax, and TLR3 levels after sunscreen application, as these molecules exhibited high responsiveness to sun exposure according to ROC analysis. In addition, IL-1ra, IL-1α, and IL-10 levels were quantified by enzyme-linked immunosorbent assay, and TNF-α, Bax, TLR3, and TLR4 levels were semi-quantitatively assessed by immunocytochemistry. RESULTS: Receiver operating characteristic analysis identified IL-1ra/IL-1α, TNF-α, Bax, and TLR3 in the stratum corneum as highly responsive to sun exposure. Moreover, in participants, including patients with photodermatosis, IL-1ra/IL-1α, TNF-α, and Bax levels decreased significantly after sunscreen application. CONCLUSION: The results revealed that IL-1ra/IL-1α, TNF-α, and Bax in the stratum corneum represent sensitive indicators of the influence of sun exposure on the skin.

Reliability and validity of the Japanese version of the overall dry skin score in older patients.

BACKGROUND: Dry skin is the most common skin problem, especially in the elderly. However, there is no effective instrument to assess dry skin in Japan. This study aimed to evaluate the reliability and validity of the Japanese version of the overall dry skin score (ODS-J), the gold standard for dry skin assessment. MATERIALS AND METHODS: A cross-sectional study was conducted on 47 patients aged > 65 years. Images of skin on their limbs were captured using a digital camera; both upper and lower limbs were assessed (n = 4/patient). One dermatologist; two wound, ostomy, and continence nurses; and three nursing researchers independently evaluated the images using the ODS-J to assess the intraclass correlation coefficient (ICC) for inter-rater reliability. Stratum corneum hydration (SCH) and transepidermal water loss (TEWL) were the external criteria used to verify concurrent and known-groups validity. RESULTS: In total, 182 sites at which the SCH and TEWL could be measured were evaluated for the ODS-J. The ICC for inter-rater reliability of the six raters was 0.939 (p < 0.001). A higher ODS-J was associated with lower SCH (ρ = -0.374; p < 0.001) and lower TEWL (ρ = -0.287; p < 0.001) values. The ODS-J for the lower legs was significantly higher than that of the forearms (p < 0.001). CONCLUSIONS: The ODS-J showed good inter-rater reliability, concurrent validity, and known-groups validity. It can be used by clinical nurses in Japan to observe patients' skin and is an effective indicator for the evaluation of skin care.

Zonula occludens-1 demonstrates a unique appearance in buccal mucosa over several layers.

Tight junctions (TJs) firmly seal epithelial cells and are key players in the epithelial barrier. TJs consist of several proteins, including those of the transmembrane claudin family and the scaffold zonula occludens (ZO) family. Epithelial tissues are exposed to different conditions: to air in the stratified epithelium of the skin and to liquids in the monolayer of the intestine. The TJs in stratified oral mucosal epithelium have remained insufficiently elucidated in terms of distributions, appearances and barrier functions of TJ proteins in normal buccal mucosa. We investigated these and ZO-1 and claudin-1 were found to be expressed in the top third and in the bottom three quarters of the mucosal epithelium. ZO-1 in the buccal mucosa was found to have an irregular linear appearance. ZO-1 in the buccal mucosa continuously existed in several layers. Electron microscopy revealed the buccal mucosa to have kissing points. In a biotin permeation assay that sought to investigate inside-outside barrier function, the biotin tracer penetrated several ZO-1 layers but did not pass through all the ZO-1 layers. We found that the oral mucosal cell knockdown of TJP1 or CLDN1 resulted in decreases of TER but no significant change in FITC-dextran leakage. Our results suggest that the distribution and appearance of ZO-1 in the buccal mucosa differ from those in the skin. We were unable to prove barrier function in this study but we did show barrier function against small molecules in vivo and against ions in vitro.

Discovery of natural TRPA1 activators through pharmacophore-based virtual screening and a biological assay.

Transient receptor potential cation channel subfamily A member 1 (TRPA1), a member of the transient receptor potential family, detects a wide range of environmental stimuli, such as low temperature, abnormal pH, and reactive irritants. TRPA1 is of great interest as a target protein in fields related to pharmaceuticals and foods. In this study, a library of natural products was explored to identify TRPA1 activators by pharmacophore screening of known TRPA1 agonists and biological assays for agonist activity. The study identified six natural compounds as novel TRPA1 agonists. The discovery of these compounds may prove useful in elucidating the TRPA1 activation mechanism.

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations.

Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.

Effectiveness of weak wiping pressure during bed baths in hospitalized older adults: A single-blind randomized crossover trial.

This study evaluated the effectiveness of weak wiping pressure on skin barrier function and patient satisfaction in comparison to ordinary pressure in hospitalized older adults. Forty-seven participants in a general hospital were blindly and randomly assigned a sequence of two bed baths: wiping three times with weak pressure (12-14 mmHg) and ordinary pressure (23-25 mmHg). Transepidermal water loss and stratum corneum hydration were measured before and after the intervention, and patient satisfaction was assessed using a Likert scale. Ordinary pressure significantly decreased skin barrier function compared to weak pressure; however, neither of the pressures caused discomfort. Weak pressure was more effective than ordinary pressure in preventing skin disorders and providing satisfaction. Subgroup cluster analysis showed that ordinary pressure was likely to impair the skin barrier function in older adults with diabetes/dyslipidemia and renal dysfunction. The application of weak pressure during bed baths, especially for these patients, is recommended.

Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach.

Recent work has gradually been clarifying the binding site of non-electrophilic agonists on the transient receptor potential A1 (TRPA1). This study searched for non-electrophilic TRPA1 agonists by means of in silico drug discovery techniques based on three-dimensional (3-D) protein structure. First, agonist-bound pocket structures were explored using an advanced molecular dynamics simulation starting from the cryo-electron microscopic structure of TRPA1, and several pocket structures suitable for virtual screening were extracted by structure evaluation using known non-electrophilic TRPA1 agonists. Next, 49 compounds were selected as new non-electrophilic agonist candidates from a library of natural products comprising 10,555 compounds by molecular docking toward these pocket structures. Measurement of the TRPA1 agonist activity of these compounds showed notable TRPA1 activation with three compounds (decanol, 2-ethyl-1-hexanol, phenethyl butanoate). Decanol and 2-ethyl-1-hexanol, which are categorized as fatty alcohols, in particular have a novel chemical scaffold for TRPA1 activation. The results of this study are expected to be of considerable use in understanding the molecular mechanism of TRPA1 recognition by non-electrophilic agonists.

Fc-binding proteins enhance autoantibody-induced BP180 depletion in pemphigoid.

Immunoglobulins (Igs) consist of two antigen-binding regions (Fab) and one constant region (Fc). Protein A and protein G are bacterial proteins used for the purification of IgG by virtue of their high affinities for the Fc fragment. Rheumatoid factors are autoantibodies against IgG Fc fragments, which are present in the body under physiological conditions. Little is known about the influence of Fc-binding proteins on the pathogenicity of antibody-induced autoimmune diseases. Pemphigoid diseases are a group of autoimmune subepidermal blistering disorders that includes bullous pemphigoid and mucous membrane pemphigoid. IgGs targeting the non-collagenous NC16A domain of the 180-kDa bullous pemphigoid antigen (BP180) are known to induce skin fragility in mice and the depletion of BP180 in keratinocytes. In this study, mAb against NC16A in combination with Fc-binding proteins was found to enhance BP180 depletion. Although mAb against the C-terminus of BP180 does not show pathogenicity in vivo or in vitro, mAb treatment with Fc-binding proteins clearly induced skin fragility in mice and BP180 depletion in keratinocytes. Anti-BP180 mAbs and Fc-binding proteins were colocalized in the cytoplasm and at the basement membrane zone. Cell adhesion strengths were decreased in parallel with BP180 amounts. Clinically, bullous pemphigoid patients had higher rheumatoid factor titers than controls. Anti-BP180 mAb in combination with high-titer rheumatoid factor serum was found to enhance BP180 depletion. Furthermore, saliva from mucous membrane pemphigoid patients contained larger quantities of bacteria and Fc-binding proteins than controls. Our results suggest that Fc-binding proteins (rheumatoid factor or protein G) may enhance the pathogenicity of autoantibodies in pemphigoid diseases. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

The direct binding of collagen XVII and collagen IV is disrupted by pemphigoid autoantibodies.

The basement membrane zone (BMZ) is framed by hemidesmosomes and extracellular matrix (ECM) including collagen IV (COL4). Hemidesmosomes are multiprotein complexes that include collagen XVII (COL17). BMZ proteins can be targeted in autoimmune subepidermal blistering diseases, e.g., pemphigoid targeting COL17. The blistering mechanisms in pemphigoid have not been fully elucidated, especially in mucous membrane pemphigoid (MMP), which mainly affects the mucosa. In this study, we showed that oral lesions in pemphigoid may be attributed to the inhibition of protein-protein interactions by autoantibodies. Using immunoprecipitation, we revealed that COL17 directly binds to COL4 in normal human keratinocytes and normal human oral keratinocytes. In particular, the C-terminus of COL17 is binding site to COL4 in oral keratinocytes. The precise COL4-binding region on COL17 was determined by protein-protein binding assay to be from amino acid Gly1175 to Asp1340 on the C-terminus. MMP-IgG or mAb recognizing the C-terminus hindered the interaction of COL17 with COL4 in oral keratinocytes. Furthermore, keratinocyte adhesion strength to COL4-coated plates was significantly reduced by the treatment of mAb against the C-terminus. In addition, the inflammatory infiltrates around perilesions were significantly less in MMP compared to BP. These results indicate that pemphigoid IgG targeting the C-terminus plays a pathogenic role in blister formation in the oral mucosa to inhibit protein interactions with less inflammation.

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

Motivation: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity. Results: This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources. Availability and implementation: Code and data are available on GitHub at https://github.com/tsudalab/bpbi. Contact: terayama@cbms.k.u-tokyo.ac.jp or tsuda@k.u-tokyo.ac.jp. Supplementary information: Supplementary data are available at Bioinformatics online.

The identification of autoantigens in mucous membrane pemphigoid using immortalized oral mucosal keratinocytes.

BACKGROUND: Mucous membrane pemphigoid (MMP) is a rare chronic autoimmune subepithelial blistering disorder, targeting multiple basement membrane zone (BMZ) proteins including collagen XVII (COL17). Circulating autoantibodies of MMP are often undetected due to their lower titers. The oral mucosa is a valuable substrate for the detection of autoantibodies in MMP patients. However, obtaining normal human oral mucosa is more difficult than obtaining normal human skin. We established immortalized normal human oral mucosal keratinocytes (OMKs) and performed immunoblotting using immortalized OMK lysate for detecting autoantigens in MMP. METHODS: Immortalized OMKs were generated from primary OMKs using E6/E7 proteins of HPV. We compared the protein expression levels of major BMZ proteins between primary OMKs and immortalized OMKs. We performed immunoblotting to detect autoantigens using cell lysates from immortalized OMKs in 30 MMP patients. RESULTS: There were no significant differences between primary OMKs and immortalized OMKs in terms of protein expression levels of the BMZ proteins, including COL17, laminin 332, integrin α6/ß4, collagen VII, and collagen IV. Cell lysates of immortalized OMKs effectively identified MMP autoantigens in 60% (18/30) of MMP sera. We found an interesting case of MMP whose autoantibodies preferentially reacted to the 120-kD protein that is an ectodomain of COL17. CONCLUSION: We demonstrated that a cell lysate of immortalized OMKs is a reliable substrate for the detection of MMP autoantigens. This newly developed immunoblotting analysis method promises to contribute to the diagnosis of MMP.

Regulatory T-cell dysfunction induces autoantibodies to bullous pemphigoid antigens in mice and human subjects.

BACKGROUND: Regulatory T (Treg) cells play a crucial role in peripheral immune tolerance in multiple organs, including the skin. Thus far, the effect of peripheral immune tolerance failure on autoantibody-related autoimmune reactions to the skin is unclear. OBJECTIVE: We sought to elucidate the target autoantigens in the skin under the condition of Treg cell dysfunction caused by forkhead box P3 (Foxp3) gene mutations in scurfy mice and patients with immunodysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome. METHODS: Sera and skin from scurfy mice and sera from patients with IPEX syndrome were analyzed to detect target autoantigens by using immunofluorescence studies, ELISAs, and immunoblotting. The pathogenicity of scurfy IgG was examined by using a passive transfer experiment. CD4+ T cells from scurfy mice were transferred to immunodeficient mice to examine their pathogenicity. Signal transducer and activator of transcription 6 (Stat6)-/- scurfy mice were analyzed to further clarify the molecular pathway of autoantibody production. Follicular helper T-cell counts are measured in Stat6-/- scurfy mice and scurfy mice. RESULTS: Scurfy mice spontaneously generated IgG autoantibodies to the dermal-epidermal junction, which had been class-switched from IgM within 12 days after birth. The target autoantigens were murine BP230 and type XVII collagen (COL17). The scurfy polyclonal autoantibodies did not induce skin fragility in neonatal mice. Autoantibody production was induced by CD4+ T cells from scurfy mice and was ameliorated by Stat6 gene knockout in association with a decrease of follicular helper T cells. We also identified autoantibodies to COL17 and BP230 in patients with IPEX syndrome and found an association between production of autoantibodies to COL17 and an eczematous skin phenotype. CONCLUSIONS: Dysregulation of Treg cells generates autoantibodies to COL17 and BP230 in vivo.

Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach.

Protein-drug binding mode prediction from the apo-protein structure is challenging because drug binding often induces significant protein conformational changes. Here, the authors report a computational workflow that incorporates a novel pocket generation method. First, the closed protein pocket is expanded by repeatedly filling virtual atoms during molecular dynamics (MD) simulations. Second, after ligand docking toward the prepared pocket structures, binding mode candidates are ranked by MD/Molecular Mechanics Poisson-Boltzmann Surface Area. The authors validated our workflow using CDK2 kinase, which has an especially-closed ATP-binding pocket in the apo-form, and several inhibitors. The crystallographic pose coincided with the top-ranked docking pose for 59% (34/58) of the compounds and was within the top five-ranked ones for 88% (51/58), while those estimated by a conventional prediction protocol were 9% (5/58) and 50% (29/58), respectively. Our study demonstrates that the prediction accuracy is significantly improved by preceding pocket expansion, leading to generation of conformationally-diverse binding mode candidates. © 2018 Wiley Periodicals, Inc.