Journal of Advanced Research

In perinatal medicine, intrauterine growth restriction (IUGR) is one of the greatest challenges. The etiology of IUGR is multifactorial, but most cases are thought to arise from placental insufficiency. However, identifying the placental cause of IUGR can be difficult due to numerous confounding factors. Selective IUGR (sIUGR) would be a good model to investigate how impaired placentation affects fetal development, as the growth discordance between monochorionic twins cannot be explained by confounding genetic or maternal factors. Herein we constructed and analyzed the placental proteomic profiles of IUGR twins and the normal cotwins. Specifically, we identified a total of 5481 proteins and 233 differentially expressed proteins (DEPs), including 57 upregulated and 176 downregulated DEPs in IUGR twins. Bioinformatic analysis indicates that these DEPs are mainly associated with cardiovascular system development and function, organismal survival, and organismal development. Notably, 34 DEPs are significantly enriched in angiogenesis, and diminished placental angiogenesis in IUGR twins has been further elaborately confirmed. Moreover, we found decreased expression of metadherin (MTDH) in placentas for IUGR twins and demonstrated that MTDH contributes to placental angiogenesis and fetal growth in vitro. Collectively, our findings reveal the comprehensive proteomic signature of placentas for sIUGR twins, and the DEPs identified may provide in-depth insights into pathogenesis of placental dysfunction and subsequent impaired fetal growth.

The clinical diagnosis of major depressive disorder (MDD), a heterogeneous disorder, still depends on subjective information in terms of various symptoms regarding mood. Detecting extracellular vesicles (EVs) in blood may result in finding a diagnostic biomarker that reflects the depressive stage of patients with MDD. Here, we report the results on the glycosylation pattern of enriched plasma EVs from patients with MDD and age-matched healthy subjects. In this cohort, the levels of Triticum vulgaris (wheat germ) agglutinin (WGA), N-acetyl glucosamine (GlcNAc) and N-acetylneuraminic acid (Neu5Ac, sialic acid) - binding lectin, were significantly decreased in patients with MDD in depressive state compared to healthy subjects (area under the curve (AUC): 0.87 (95% confidence interval (CI) 0.76 - 0.97)) and in remission state (AUC: 0.88 (95% CI 0.72 - 1.00)). Furthermore, proteome analysis revealed that the von Willebrand factor (vWF) was a significant factor recognized by WGA. WGA-binding vWF antigen differentiated patients with MDD versus healthy subjects (AUC: 0.92 (95% CI 0.82 - 1.00)) and the same patients with MDD in depressive versus remission state (AUC: 0.98 (95% CI 0.93 - 1.00)). In this study, the change patterns in the glycoproteins contained in plasma EVs support the usability of testing to identify patients who are at increased risk of depression during antidepressant treatment.

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases. But the great heterogeneity of AD makes it difficult to design an accurate diagnostic pipeline based on traditional diagnostic methods. In other words, the AD diagnosis has suffered from an inaccurate bottleneck. Thus, it is necessary to develop a novel and accurate diagnostic model to supplement existing methods. The recent development of advanced gene sequencing technologies enables potential in accurate AD diagnosis. Inspired by this, we developed an accurate AD diagnosis based on transcriptomic data in skin tissue. Using these data of 149 subjects, including AD patients and healthy controls, from Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of AD and identified six critical genes (PPP4R1, SERPINB4, S100A7, S100A9, BTC, and GALNT6) by random forest classifier. In a follow-up study of these genes, we constructed a neural network model (average AUC=0.943) to automatically distinguish subjects with AD from healthy controls. Among these critical genes, we found that PPP4R1 and GALNT6 had never been reported to be associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes may be developed into useful biomarkers of AD diagnosis and may provide invaluable clues or perspectives for further researches on the pathogenesis of AD.

BackgroundFibromyalgia (FM) is a chronic disorder characterized by widespread pain and immune dysregulation. Emerging evidence suggests that gut microbiota and inflammatory proteins may contribute to the development of FM. ObjectiveThe aim of this study was to investigate the causal relationships between gut microbiota, inflammatory proteins (cytokines/chemokines), and FM using bidirectional Mendelian randomization (MR) and meta-analysis approaches. MethodsMR analyses were conducted using genetic data from European populations, employing methods such as MR-IVW, MR-Egger, and MR-weighted median. Reverse MR was also performed, with FM treated as the exposure. A meta-analysis was conducted to consolidate the findings. ResultsRuminococcus gauvreauii was identified as a risk factor for FM, while Enterorhabdus, Parabacteroides, Butyricicoccus, and Prevotella 9 were found to be protective. Five inflammatory proteins--C-X-C motif chemokine 5 (CXCL5), S100-A12, Leukemia inhibitory factor receptor (LIFR), Monocyte chemoattractant protein 2 (MCP-2/CCL8), and Tumor necrosis factor (TNF-)--exhibited protective associations, while Natural killer cell receptor 2B4 (NKCR-2B4/CD244) and Interleukin-12 subunit beta (IL-12{beta}) were associated with an increased risk of FM. ConclusionThis study highlights the role of gut microbiota and inflammatory proteins (cytokines/chemokines) in the pathogenesis of FM. Through Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, the findings suggest their involvement in immune regulation, inflammatory responses, and viral pathways. These findings provide new insights into potential therapeutic targets for modulating gut health and immune responses, opening new avenues for future research and clinical interventions.

BackgroundHow skin microbiota in psoriasis patients responded to systematic therapeutics remained unknown. ObjectivesTo profile temporal shifts in transcriptionally active skin microbiota in psoriasis patients receiving systemic therapies. MethodsWe prospectively enrolled 61 psoriasis patients and 29 skin-healthy controls in 2015-2019. Using RNA-based 16S rRNA gene sequencing, we analyzed 969 samples from skin lesions and compared microbial abundance and diversity by therapeutic classes and disease severity. ResultsLesional microbiota in patients on conventional systemics and TNF- inhibitor was different in relative abundances in Firmicutes (7.83% higher, adjusted P < 0.001) and Proteobacteria (6.98% lower, adjusted P < 0.01) from that in patients on anti-interleukin monoclonal antibodies (anti-ILAb) at baseline. The only difference during treatment was a 1.47% lower abundance in Bacteroides associated with nonbiologics use (adjusted P < 0.01). We identified no indicator taxa by disease severity at baseline yet noticed that a minor relative reduction in Corynebacterium sp. was associated with clinical responses to treatment. Compared to anti-ILAb, TNF- inhibitor and nonbiologics were associated with -0.21 lower Shannon Diversity (adjusted P < 0.01) and 0.03 higher Shannon Evenness (adjusted P < 0.01). Results of ordinated principal coordinates analysis revealed that, lesional microbiota from patients of these 3 therapeutic groups was compositionally distinct. Our work also demonstrated concurrent changes in clonal shifts in systemic T cell receptor clonotypes that were associated with systemic use of biologics. ConclusionsCommunity abundances and diversities of skin microbiota may be useful in distinguishing skin microbiota from patients receiving different systemic therapeutics. Specifically, use of anti-ILAb and TNF- inhibitor was associated with sample-wise microbial abundances and diversities, but not richness, over time. These findings highlighted the potential utility of skin microbiota as biomarkers for personalized treatment plans in patients with moderate-to-severe psoriasis.

Chronic spontaneous urticaria (CSU) is one of the most intractable human-specific skin diseases. However, as no experimental animal model exists, the mechanism underlying disease pathogenesis in vivo remains unclear, making the establishment of a curative treatment challenging. Here, using a novel approach combining mathematical modeling, in vitro experiments and clinical data analysis, we show that the pathological state of CSU patients can be inferred by geometric features of the skin eruptions. Based on our hierarchical mathematical modelling and the analysis of 105 CSU patient eruption pattern geometries, analyzed by six dermatologists, we demonstrate that the eruption patterns can be classified into five categories, each with distinct histamine, basophils, mast cells and coagulation factors network signatures. Furthermore, our network analysis revealed that tissue factor degradation/activation likely determines boundary/area pattern, and that the state of spontaneous histamine release from mast cells may contribute to divergence of the boundary pattern. Thus, our study not only demonstrates that pathological states of diseases can be defined by geometric features but will also facilitate more accurate decision-making to manage CSU in the clinical setting.

Clinical misclassification between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) affects treatment plans and carries risks of potential for recurrence, metastases morbidity and mortality. We report the development of a novel tissue sampling approach with molecular biopsy using electroporation. The methods, coined e-biopsy, enables non-thermal permeabilization of cells in the skin for efficient vacuum-assistant extraction of informative biomolecules for rapid diagnosis. We used e-biopsy for ex vivo proteome extraction from 3 locations per patient in 21 cSCC and 21 BCC pathologically validated human tissue samples. The total 126 extracted proteomes were profiled using LC/MS/MS. The obtained mass spectra presented significantly different proteome profiles for cSCC and BCC with several hundreds of proteins significantly differentially expressed in each tumor in comparison to the other. Notably, 17 proteins were uniquely expressed in BCC and 7 were uniquely expressed in cSCC patients. Statistical analysis of differentially expressed proteins found 31 cellular processes, 23 cellular functions and 10 cellular components significantly different between cSCC and BCC. Machine Learning classification models constructed on the sampled proteomes enabled the separation of cSCC patients from BCC with average cross-validation accuracy of 81%, cSCC prediction positive predictive value (PPV) of 78.7% and sensitivity of 92.3%, which is comparable to initial diagnostics in a clinical setup. Finally, the protein-protein interaction analysis of the 11 most informative proteins, derived from Machine Learning framework, enabled detection of a novel protein-protein interaction network valuable for further understanding of skin tumors. Our results provide evidence that the e-biopsy approach could potentially be used as a tool to support cutaneous tumors classification with rapid molecular profiling.

Pemphigus vulgaris (PV) is a life-threatening autoimmune blistering disease caused by pathogenic autoantibodies targeting the desmosomal cadherins desmoglein 3 (DSG3) and desmoglein 1 (DSG1), essential for cell-cell adhesion. Although the role of these autoantibodies in disease pathogenesis is well established, the mechanisms initiating the autoimmune response remain unclear. A strong genetic association has been identified between PV and specific HLA class II alleles, particularly HLA-DRB1*04:02 and *14:01, which may facilitate the presentation of desmoglein-derived peptides to autoreactive CD4+ T cells. Environmental factors are also thought to contribute, with molecular mimicry being a leading hypothesis--whereby foreign antigens resembling host proteins trigger cross-reactive immune responses. In this study, we analyzed HLA-DRB1 allele frequencies in Turkish PV patients and controls, confirming strong associations with *04:02 and 14:01. Notably, we found that the heterozygous HLA-DRB104:02/14:01 genotype confers an approximately 100-fold increased risk for PV. Building on this, we propose a novel model: cadherin-like proteins from the salivary glands of mosquitoes or similar blood-feeding insects may structurally mimic DSG3/DSG1. In our model--termed vector-derived cadherin mimicry (VCM)--pattern recognition, rather than primary sequence identity (i.e., shared surface charge/topology patterns) is key to the mimicry mechanism. Repeated exposure to such antigens in genetically susceptible individuals may contribute to tolerance breakdown and disease initiation. Using HLA genotyping, peptide binding prediction, structural modeling, and molecular dynamics simulations, we provide preliminary in silico evidence supporting the VCM hypothesis as a potential trigger for PV-specific autoimmunity.

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexplored, despite its potential to efficiently capture stable biological signals accumulated over weeks to months. This study leveraged machine learning to investigate the potential of the hair proteome--all detectable peptides and proteins--as a biomarker source for stress-associated psychopathology. We analyzed protein profiles from hair segments of women with non-suicidal self-injury disorder (n = 36) and healthy controls (n = 32). Of 1114 identified proteins, 611 were sufficiently abundant for analyses. Partial Least Squares Discriminant Analysis achieved stable 84.4% cross-validated accuracy for classification of clinical groups (p < .001), outperforming models based on data-derived clusters (60%), stress-related proteins (73%), and simulated hair cortisol from meta-analytic effect sizes (53-59%). Predicted class probabilities strongly correlated with clinical symptoms and well-being (r > .60). Key predictive proteins were linked to pain perception, oxidative stress, and cholesterol homeostasis. Approximately 15% of proteins differed significantly between groups, with the strongest candidates related to ribosomal function--an emerging target in depression. These findings establish hair proteomics as a promising, non-invasive biomarker source for psychiatric research, warranting validation in larger cohorts and exploration of clinical applications in risk assessment and personalized interventions.

Here we describe the first double-blinded, randomized, placebo-controlled trial (RCT) on vaginal microbiota transplantation (VMT) without antibiotics in women with both symptomatic and asymptomatic vaginal dysbiosis. Forty-nine women were randomly assigned to VMT or placebo. The trial did not show a significant conversion to our predefined Lactobacillus-dominated microbiome. However, in participants not initially converting, antiseptic pretreatment before a subsequent VMT led to a 50% conversion rate, associated with an anti-inflammatory shift in gene expression. Metagenomic sequencing and strain-level genetic analysis confirmed donor engraftment in five of 10 women who showed microbiome conversion. Extensive exploration of the microbiome, immune response and metadata revealed differences in baseline energy metabolism in participants who later experienced donor engraftment. Treatments for vaginal dysbiosis are urgently needed and given that VMT can lead to donor engraftment and change the vaginal immune profile, future studies should focus on optimizing this treatment for various womens health diseases.

Toxic epidermal necrolysis (TEN) is a fatal drug-induced skin reaction and an emerging public health issue. Triggered by common medications, TEN patients undergo severe and sudden epidermal detachment caused by keratinocyte cell death. Although molecular mechanisms driving keratinocyte cell death have been proposed, the main drivers remain unknown and no effective therapy exists. To systematically map molecular changes that are associated with TEN and identify potential druggable targets, we employed the single- cell spatial proteomics technique Deep Visual Proteomics. We analyzed formalin-fixed paraffin-embedded archived skin-tissue biopsies of three types of cutaneous drug reactions with varying severity and quantified over 5,000 proteins in keratinocytes and skin-infiltrating immune cells. Most strikingly, this revealed a robust enrichment of Type-I and -II interferon signature in the immune cell and keratinocyte compartment of TEN patients, along with a drastic activation of pSTAT1. Targeted inhibition with pan- JAK inhibitor (JAKi) tofacitinib reduced keratinocyte-directed cytotoxicity in a novel live-cell imaging assay, using patient-derived keratinocytes and peripheral blood mononuclear cells (PBMCs). Furthermore, oral administration of pan-JAKi tofacitinib or baricitinib ameliorated clinical and histological disease severity in two distinct mouse models of TEN. Lastly, JAKi treatment was safe and associated with rapid cutaneous re- epithelialization and recovery in four patients with TEN. This study uncovers the JAK- STAT and interferon signaling pathways as key pathogenic drivers of TEN and demonstrates the potential of targeted JAK inhibition as a curative therapy.

A growing number of clinical and preclinical studies suggest that alterations in peripheral and brain immunal system and followed inflammation are associated with the pathophysiology of depression, also leading to the changes in local glucose metabolism in the brain. Here, we identified Yin-yang 1 (YY1), a transcription factor that has been reported to be closely associated with central and peripheral inflammation. The levels of YY1 and IL-1{beta} were significantly increased in blood samples from depressed individuals, and significantly decreased after treatment with Vortioxetine. Meanwhile, it was found that the level of YY1 in plasma was negatively correlated with visual learning reasoning and problem solving in MDD patients, and positively correlated with the level of IL-1{beta} in plasma. CUMS animals showed depressive-like behavior. Compared with the control group, MicroPET analysis showed that the decrease of glucose metabolism in the hippocampus, entorhinal cortex, amygdala, striatum and mPFC was reversed after treatment. After treatment, these changes were reversed. In conclusion, Our study suggested that YY1-NF-{kappa}B - IL-1{beta} inflammatory pathway may play an essential part on both mood changes and cognitive impairment in depression, and may be associated with changes in glucose metabolism in the emotion regulation and cognition related brain regions. These findings provide new evidence for the inflammatory mechanisms of depression.

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease. We performed a comprehensive analysis to construct the correlation of m6A and immune in HCM. Two HCM datasets (GSE141910 and GSE160997) and m6A-related regulators were obtained from GEO and published articles, respectively. Differentially expressed m6A-related regulators were obtained. Random forest model and nomogram were conducted to assess the risk of HCM, and finally, the m6A subtype was constructed. Functional enrichment analysis was conducted. Protein-protein interaction network of differentially expressed genes between m6A subtypes was performed. Furthermore, we constructed the Hubgene-chemical network, Hubgene-microRNA network, and Hubgene-transcription factor network of the top 10 hubgenes. Additionally, the immune subtype and hubgene subtype were constructed. PCR was performed to validate the m6A-related regulators. We obtained 20 m6A-related regulators in HCM. Among them, 8 m6A-related regulators differentially expressed (YTHDC1, HNRNPC, and FMR1 were up-regulated while YTHDC2, FTO, WTAP, IGF2BP2, and IGF2BP3 were down-regulated). FTO, FMR1, IGF2BP3, YTHDC1, and IGF2BP2 were the top 5 important m6A-related regulators and were used to conduct the nomogram. We obtained 329 differentially expressed genes in m6A subtype and these genes enriched HCM-related processes and pathways. Furthermore, we constructed the Hubgene-chemical network, Hubgene-microRNA network, and Hubgene-transcription factor network of the top 10 hubgenes (NFKBIA, NFKB1, PSMA3, PSMC4, PSMA2, PSMA4, PSMD7, PSMD10, PSMD8, and PSMA6). And then we constructed an immune subtype based on the immune cell infiltration levels and hubgene subtype based on the expression of the top 10 hubgenes. Finally, we verified the main results through experiments. In conclusion, we built a nomogram and identified 8 m6A-related regulators and 10 hubgenes, which were prominently associated with HCM. We found that m6A and the immune system may play a crucial role in the HCM. Accordingly, those genes and pathways might become therapeutic targets with clinical usefulness in the future.

Sequencing-based studies have recognized hundreds of genetic variants that increase the risk of schizophrenia (SCZ), but only a few percents of heritability can be attributed to these loci. It is challenging to discover the full spectrum of schizophrenia genes and reveal the dysregulated functions underlying the disease. Here, we proposed a holistic model for predicting disease genes (HMPDG), a novel machine learning prediction strategy integrated by Protein-Protein Interaction Network (PPIN), pathogenicity score, and RNA expression data. Applying HMPDG, 1946 potential risk genes (PRGs) as a complement of the genetic basis of SCZ were predicted. Among these, the first decile genes were highlighted as high confidence genes (HCGs). PRGs were validated by multiple independent studies of schizophrenia, including genome-wide association studies (GWASs), gene expression studies, and epigenetic studies. Remarkably, the strategy revealed causal genes of schizophrenia in GWAS loci and regions of copy number variant (CNV), providing a new insight to identify key genes in disease-related loci with multi genes. Leveraging our predictions, we depict the spatiotemporal expression pattern and functional groups of schizophrenia risk genes, which can help us figure out the pathophysiology of schizophrenia and facilitate the discovery of biomarkers. Taken together, our strategy will advance the understanding of schizophrenia genetic basis and the development of diagnosis and therapeutics.

Keshan disease (KD), an endemic heart disease with multifocal necrosis and replacement fibrosis of the myocardium,is still a nightmare situation for human health. However, molecular mechanism in the pathogenesis of KD remains unclear. Herein, blood samples were collected from 68 KD patients and 100 controls, and we systematically analyzed mutation profiles using whole-exome sequencing (WES). Causative genes of dilated cardiomyopathy (DCM), gene-based burden analysis, disease and pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed. Of the 98 DCM-causative genes, 106 rare variants in 28 genes were detected in KD patients with minor allele frequency (MAF) < 0.001. Gene-based burden analysis, PPI network analysis, and automated Phenolyzer analysis were performed to prioritize 199 candidate genes, which combined with 98 DCM-causative genes, and reference genes from gene microarray or proteomics in KD. Then, 19 candidate pathogenic genes were selected, and 9 candidate genes were identified using PPI analysis, including HIF1A, GART, ALAD, VCL, DTNA, NEXN, INPPL1, NOS3, and JAK2. The 199 candidate genes were further analyzed using disease enrichment with CTD database and PPI analysis, and 21 candidate genes were identified. By combining with disease enrichment and PPI analysis, 7 Selenium (Se)-related genes were further identified, including ALAD, RBM10, GSN, GGT1, ADD1, PARP1, and NOS3. Based on the gene function and data validation, NEXN, TAF1C, FUT4, ALAD, ZNF608, and STX2 were the most likely pathogenic genes in KD. Notably, ALAD is the only candidate pathogenic gene identified by four different analyses, and its homozygous mutant mice could affect heart development and cause death.

ObjectiveAs an idiopathic hypertensive disorder of pregnancy, pre-eclampsia (PE) remains a major cause of maternal and neonatal morbidity and mortality, with no effective strategy for causal treatment. MethodsThis study was performed by downloading the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) based on the GSE173193 dataset, including single-cell sequencing data from placental samples of two PE patients and two normal controls. Placental cell subpopulations and their transcriptional heterogeneity were compared between PE and healthy pregnancies, and the mechanisms of PE cell dynamics in the hypoxic microenvironment were confirmed by in vitro experiments. ResultsIn this study, we constructed a large-scale single-cell transcriptome ecological landscape of 26,416 cells from healthy pregnant and PE patients placenta and further identified a PE-specific CSNK2B-positive subpopulation of chorionic villous trophoblast (EVT) cells. Specifically, this study revealed that the EVT subpopulation PTBP1 was inactivated by lactonization in the hypoxic microenvironment, resulting in low expression of the N6-methyladenosine (m6A) reading protein IGF2BP2. On the basis of this, low expression of IGF2BP2 inhibits mitochondrial autophagy, causes the accumulation of damaged mitochondria, exacerbates lactic acid accumulation while inducing EVT apoptosis on the one hand. In particular, hypoxia may initially promote oxidative stress through the production of mitochondrial reactive oxygen species. on the other hand, it inhibits EVT adherent spot signaling, decreases EVT invasive ability, leads to impaired placental spiral vessel recast, and promotes PE disease process. In addition, there are interactions between abnormal metabolic signaling of PE-specific EVT subpopulations and microenvironmental immune cells, which activate metabolic inflammation. ConclusionThe present study not only provides a new cell biological and genetic basis for elucidating the pathogenesis of PE, but also contributes to the design of an allopathic treatment strategy for PE.

Erythroderma is an acute and potentially life-threatening inflammatory condition characterized by redness and scaling of > 90% of the skin. Its treatment is challenging because various underlying skin diseases can cause erythroderma and are difficult to distinguish. Here, we performed in-depth proteomics and transcriptomics analyses of skin from 96 patients with erythroderma caused by five different diseases, including pityriasis rubra pilaris, psoriasis, atopic dermatitis, cutaneous T-cell lymphoma, and drug-induced maculopapular rash. High-throughput workflows enabled in-depth molecular profiling, identifying over 9,300 proteins and 17,200 protein coding transcripts, revealing distinct molecular signatures for each disease. The proteome showed elevated expression of type 2 immunity associated Charcot-Leyden crystal in skin of atopic dermatitis, potentially contributing to NLRP3-driven chronic inflammation in this disease. Complementary transcriptomic analysis demonstrated selective upregulation of IL17C in pityriasis rubra pilaris, strongly correlating with increased IL1 family cytokine expression. Interestingly, only a subset of these patients expressed this IL17C-IL1 signature, suggesting treatment-relevant disease endotypes. Through multi-omics integration, we uncovered disease-specific molecular signatures consistently altered at both protein and transcript levels. In particular, we identified elevated expression of T-cell regulator RASAL3 in cutaneous T-cell lymphoma, which has not been explored in its pathogenesis so far. To translate these molecular profiles into clinical utility, we expanded our adaptive machine-learning algorithm (ADAPT-Mx) for tissue based-disease classification. This achieved 76.6% diagnostic accuracy, substantially outperforming combined conventional clinical and histopathological methods (59.5%). This study provides a template for precision diagnostics in erythroderma and demonstrates the clinical potential of multi-omic profiling in severe inflammatory skin diseases.

Current approaches to selecting molecularly targeted therapies (biologics and oral small molecules) for immune-mediated skin diseases largely overlook interindividual immunologic heterogeneity, in part due to challenges of sample collection and the lack of broadly accepted biomarkers of therapeutic response. We sought to develop a rapid, minimally invasive method for obtaining and measuring biomarkers from skin and to generate predictive models of therapy response in two common inflammatory skin diseases, psoriasis and eczema. Here we present Detergent-based Immune Profiling System (DIPS), which enables painless and non-scarring collection of full thickness epidermal protein from skin and is suitable for downstream 45-plex immune protein biomarker analysis. We first developed machine learning models with the goal of accurately distinguishing between eczema (n=55) and psoriasis (n=74). Subsequently, models that correlate biomarker patterns with treatment response or nonresponse to commonly used biologic therapies were developed. We subsequently developed DIPS-Derm, a web-based platform that provides automated diagnostic and treatment predictions from data generated with DIPS. These results support the promise of artificial intelligence (AI)-driven precision dermatology and highlight the clinical potential of DIPS for personalized medicine in inflammatory skin disease.

Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive Behavior Therapy (CBT) in 163 treatment-naive outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways. Changes in metabolite concentrations related to each treatment arm were identified and compared to define metabolic signatures of exposure. In addition, association between metabolites and depressive symptom severity (assessed with the 17-item Hamilton Rating Scale for Depression [HRSD17]) and anxiety symptom severity (assessed with the 14-item Hamilton Rating Scale for Anxiety [HRSA14]) were evaluated, both at baseline and after 12 weeks of treatment. Significant reductions in serum serotonin level and increases in tryptophan-derived indoles that are gut bacterially derived were observed with escitalopram and duloxetine arms but not in CBT arm. These include indole-3-propionic acid (I3PA), indole-3-lactic acid (I3LA) and Indoxyl sulfate (IS), a uremic toxin. Purine-related metabolites were decreased across all arms. Different metabolites correlated with improved symptoms in the different treatment arms revealing potentially different mechanisms between response to antidepressant medications and to CBT.

BackgroundInflammation and autoimmune responses contribute to the pathophysiology of Long COVID, and its affective and chronic fatigue syndrome (CFS) symptoms, labeled "the physio-affective phenome." ObjectivesTo investigate whether Long COVID and its physio-affective phenome are linked to autoimmunity to the tight junction proteins, zonulin and occludin (ZOOC), and immune reactivity to lipopolysaccharides (LPS), and whether the latter are associated with signs of human herpes virus-6 reactivation (HHV-6), autoimmunity directed against oligodendrocyte and neuronal proteins, including myelin basic protein (MBP). MethodsIgA/IgM/IgG responses to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), HHV-6, ZOOC, and neuronal proteins, C-reactive protein (CRP) and advanced oxidation protein products (AOPP), were measured in 90 Long COVID patients and 90 healthy controls. The physio-affective phenome was conceptualized as a factor extracted from physical and affective symptom domains. ResultsNeural network identified IgA directed to LPS (IgA-LPS), IgG-ZOOC, IgG-LPS, and IgA-ZOOC as the most important variables associated with Long COVID diagnosis with an area under the ROC curve of 0.755. Partial Least Squares analysis showed that 40.9% of the variance in the physio-affective phenome was explained by CRP, IgA-MPB and IgG-MBP. A large part of the variances in both autoimmune responses to MBP (36.3-39.7%) was explained by autoimmunity (IgA and IgG) directed to ZOOC. The latter was strongly associated with indicants of HHV-6 reactivation, which in turn was associated with increased IgM-SARS-CoV-2. ConclusionsAutoimmunity against components of the tight junctions and increased bacterial translocation may be involved in the pathophysiology of Long COVIDs physio-affective phenome.