Systematic Transcriptome Profiling of hPSC-derived Osteoblasts Unveils CORIN's Mastery in Governing Osteogenesis Through CEBPD Modulation.

The commitment of stem cells to differentiate into osteoblasts is a highly regulated and complex process that involves the coordination of extrinsic signals and intrinsic transcriptional machinery. While rodent osteoblastic differentiation has been extensively studied, research on human osteogenesis has been limited by cell sources and existing models. Here, we systematically dissect hPSC-derived osteoblasts to identify functional membrane proteins and their downstream transcriptional networks involved in human osteogenesis. Our results reveal an enrichment of type II transmembrane serine protease CORIN in humans but not rodent osteoblasts. Functional analyses demonstrated that CORIN depletion significantly impairs osteogenesis. Genome-wide ChIP enrichment and mechanistic studies show that p38 MAPK-mediated CEBPD upregulation is required for CORIN-modulated osteogenesis. Contrastingly, the type I transmembrane heparan sulfate proteoglycan SDC1 enriched in MSCs exerts a negative regulatory effect on osteogenesis through a similar mechanism. ChIP-seq, bulk and single-cell transcriptomes, and functional validations indicated that CEBPD plays a critical role in controlling osteogenesis. In summary, our findings uncover previously unrecognized CORIN-mediated CEBPD transcriptomic networks in driving human osteoblast lineage commitment.

A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis.

Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have pinpointed differentially methylated CpG sites in MS patients. However, the interplay between genetic risk factors and epigenetic regulation remains elusive. Here, we employed a network model to integrate GWAS summary statistics of 14 802 MS cases and 26 703 controls with DNA methylation profiles from 140 MS cases and 139 controls and the human interactome. We identified differentially methylated genes by aggregating additive effects of differentially methylated CpG sites within promoter regions. We reconstructed a gene regulatory network (GRN) using literature-curated transcription factor knowledge. Colocalization of the MS GWAS and methylation quantitative trait loci (mQTL) was performed to assess the GRN. The resultant MS-associated GRN highlighted several single nucleotide polymorphisms with GWAS-mQTL colocalization: rs6032663, rs6065926 and rs2024568 of CD40 locus, rs9913597 of STAT3 locus, and rs887864 and rs741175 of CIITA locus. Moreover, synergistic mQTL and expression QTL signals were identified in CD40, suggesting gene expression alteration was likely induced by epigenetic changes. Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA) indicated that the GRN was enriched in T follicular helper cells (P-value = 0.0016). Drug target enrichment analysis of annotations from the Therapeutic Target Database revealed the GRN was also enriched with drug target genes (P-value = 3.89 × 10-4), revealing repurposable candidates for MS treatment. These candidates included vorinostat (HDAC1 inhibitor) and sivelestat (ELANE inhibitor), which warrant further investigation.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence.

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Four-Order Power Reduction in Nanoscale Electron-Nuclear Double Resonance with a Nitrogen-Vacancy Center in Diamonds.

Detecting nuclear spins using single nitrogen-vacancy (NV) centers is of particular importance in nanoscale science and engineering but often suffers from the heating effect of microwave fields for spin manipulation, especially under high magnetic fields. Here, we realize an energy-efficient nanoscale nuclear-spin detection using a phase-modulation electron-nuclear double resonance scheme. The microwave field can be reduced to 1/250 of the previous requirements, and the corresponding power is over four orders lower. Meanwhile, the microwave-induced broadening to the line-width of the spectroscopy is significantly canceled, and we achieve a nuclear-spin spectrum with a resolution down to 2.1 kHz under a magnetic field at 1840 Gs. The spectral resolution can be further improved by upgrading the experimental control precision. This scheme can also be used in sensing microwave fields and can be extended to a wide range of applications in the future.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data.

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait-cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

Identifying the active compounds and mechanism of action of TongFu XieXia Decoction for treating intestinal obstruction using network pharmacology combined with ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry.

RATIONALE: TongFu XieXia Decoction (TFXXD), a formulation rooted in traditional Chinese medicine and optimized through clinical practice, serves as an advanced version of the classic Da Cheng Qi decoction used for treating intestinal obstruction (IO), demonstrating significant therapeutic efficacy. However, due to the intricate nature of herbal compositions, the principal constituents and potential mechanisms of TFXXD have yet to be clarified. Accordingly, this study seeks to identify the active compounds and molecular targets of TFXXD, as well as to elucidate its anti-IO mechanisms. METHODS: Qualitative identification of the principal constituents of TFXXD was accomplished using ultra-high preformance liquid chromatography-quadrupole-orbitrap mass spectrometry (UPLC-Q-Orbitrap-MS/MS) analysis. PharmMapper facilitated the prediction of potential molecular targets, whereas protein-protein interaction analysis was conducted using STRING 11.0. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Metascape database. A "compounds-target-pathway" network was meticulously constructed within Cytoscape 3.8.2. Finally, molecular docking studies were performed to investigate the interactions between the core target and the crucial compound. RESULTS: UPLC-Q-Orbitrap-MS/MS analysis identified 65 components with high precision and sensitivity. Furthermore, 64 potential targets were identified as integral to TFXXD bioactivity in IO treatment. Gene Ontology enrichment analysis revealed 995 distinct biological functions, while the Kyoto Encyclopedia of Genes and Genomes enrichment analysis identified 143 intricate signaling pathways. CONCLUSION: Molecular docking studies substantiated the substantial affinity between the TFXXD bioactive constituents and their corresponding targets in the context of IO. TFXXD exerts its therapeutic efficacy in IO through a multifaceted interplay between multiple compounds, targets, and pathways. The integration of network pharmacology with UPLC-Q-Orbitrap-MS/MS has emerged as a promising strategy to unravel the intricate web of molecular interactions underlying herbal medicine. However, it is imperative to emphasize the necessity for further in vivo and in vitro experiments.

Dynamic hydrogel-metal-organic framework system promotes bone regeneration in periodontitis through controlled drug delivery.

Periodontitis is a prevalent chronic inflammatory disease, which leads to gradual degradation of alveolar bone. The challenges persist in achieving effective alveolar bone repair due to the unique bacterial microenvironment's impact on immune responses. This study explores a novel approach utilizing Metal-Organic Frameworks (MOFs) (comprising magnesium and gallic acid) for promoting bone regeneration in periodontitis, which focuses on the physiological roles of magnesium ions in bone repair and gallic acid's antioxidant and immunomodulatory properties. However, the dynamic oral environment and irregular periodontal pockets pose challenges for sustained drug delivery. A smart responsive hydrogel system, integrating Carboxymethyl Chitosan (CMCS), Dextran (DEX) and 4-formylphenylboronic acid (4-FPBA) was designed to address this problem. The injectable self-healing hydrogel forms a dual-crosslinked network, incorporating the MOF and rendering its on-demand release sensitive to reactive oxygen species (ROS) levels and pH levels of periodontitis. We seek to analyze the hydrogel's synergistic effects with MOFs in antibacterial functions, immunomodulation and promotion of bone regeneration in periodontitis. In vivo and in vitro experiment validated the system's efficacy in inhibiting inflammation-related genes and proteins expression to foster periodontal bone regeneration. This dynamic hydrogel system with MOFs, shows promise as a potential therapeutic avenue for addressing the challenges in bone regeneration in periodontitis.

WebCSEA: web-based cell-type-specific enrichment analysis of genes.

Human complex traits and common diseases show tissue- and cell-type- specificity. Recently, single-cell RNA sequencing (scRNA-seq) technology has successfully depicted cellular heterogeneity in human tissue, providing an unprecedented opportunity to understand the context-specific expression of complex trait-associated genes in human tissue-cell types (TCs). Here, we present the first web-based application to quickly assess the cell-type-specificity of genes, named Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.uth.edu/webcsea/). Specifically, we curated a total of 111 scRNA-seq panels of human tissues and 1,355 TCs from 61 different general tissues across 11 human organ systems. We adapted our previous decoding tissue-specificity (deTS) algorithm to measure the enrichment for each tissue-cell type (TC). To overcome the potential bias from the number of signature genes between different TCs, we further developed a permutation-based method that accurately estimates the TC-specificity of a given inquiry gene list. WebCSEA also provides an interactive heatmap that displays the cell-type specificity across 1355 human TCs, and other interactive and static visualizations of cell-type specificity by human organ system, developmental stage, and top-ranked tissues and cell types. In short, WebCSEA is a one-click application that provides a comprehensive exploration of the TC-specificity of genes among human major TC map.

A Genome-wide Association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos.

Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value < 1.2 × 10-10, minor allele frequency ≥ 1%, proportion of variance explained [PEV] mean = 3.4%, PEVrange = 1%-22%) with generalized effects in two population-based studies and confirmed 301 known locus-metabolite associations. Half of the identified variants with generalized effect were located in genes, including five nonsynonymous variants. We identified co-localization with the expression quantitative trait loci at 105 discovered and 151 known loci-metabolites sets. rs5855544, upstream of SLC51A, was associated with higher levels of three steroid sulfates and co-localized with expression levels of SLC51A in several tissues. Mendelian randomization (MR) analysis identified several metabolites associated with coronary heart disease (CHD) and type 2 diabetes. For example, two variants located in or near CYP4F2 (rs2108622 and rs79400241, respectively), involved in vitamin E metabolism, were associated with the levels of octadecanedioate and vitamin E metabolites (gamma-CEHC and gamma-CEHC glucuronide); MR analysis showed that genetically high levels of these metabolites were associated with lower odds of CHD. Our findings document the genetic architecture of circulating metabolites in an underrepresented Hispanic/Latino community, shedding light on disease etiology.

Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity.

Different spatiotemporal abnormalities have been implicated in different neuropsychiatric disorders and anthropometric social traits, yet an investigation in the temporal network modularity with brain tissue transcriptomics has been lacking. We developed a supervised network approach to investigate the genome-wide association study (GWAS) results in the spatial and temporal contexts and demonstrated it in 20 brain disorders and anthropometric social traits. BrainSpan transcriptome profiles were used to discover significant modules enriched with trait susceptibility genes in a developmental stage-stratified manner. We investigated whether, and in which developmental stages, GWAS-implicated genes are coordinately expressed in brain transcriptome. We identified significant network modules for each disorder and trait at different developmental stages, providing a systematic view of network modularity at specific developmental stages for a myriad of brain disorders and traits. Specifically, we observed a strong pattern of the fetal origin for most psychiatric disorders and traits [such as schizophrenia (SCZ), bipolar disorder, obsessive-compulsive disorder and neuroticism], whereas increased co-expression activities of genes were more strongly associated with neurological diseases [such as Alzheimer's disease (AD) and amyotrophic lateral sclerosis] and anthropometric traits (such as college completion, education and subjective well-being) in postnatal brains. Further analyses revealed enriched cell types and functional features that were supported and corroborated prior knowledge in specific brain disorders, such as clathrin-mediated endocytosis in AD, myelin sheath in multiple sclerosis and regulation of synaptic plasticity in both college completion and education. Our study provides a landscape view of the spatiotemporal features in a myriad of brain-related disorders and traits.

Hepatic circadian and differentiation factors control liver susceptibility for fatty liver disease and tumorigenesis.

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths, and the most common primary liver malignancy to present in the clinic. With the exception of liver transplant, treatment options for advanced HCC are limited, but improved tumor stratification could open the door to new treatment options. Previously, we demonstrated that the circadian regulator Aryl Hydrocarbon-Like Receptor Like 1 (ARNTL, or Bmal1) and the liver-enriched nuclear factor 4 alpha (HNF4α) are robustly co-expressed in healthy liver but incompatible in the context of HCC. Faulty circadian expression of HNF4α- either by isoform switching, or loss of expression- results in an increased risk for HCC, while BMAL1 gain-of-function in HNF4α-positive HCC results in apoptosis and tumor regression. We hypothesize that the transcriptional programs of HNF4α and BMAL1 are antagonistic in liver disease and HCC. Here, we study this antagonism by generating a mouse model with inducible loss of hepatic HNF4α and BMAL1 expression. The results reveal that simultaneous loss of HNF4α and BMAL1 is protective against fatty liver and HCC in carcinogen-induced liver injury and in the "STAM" model of liver disease. Furthermore, our results suggest that targeting Bmal1 expression in the absence of HNF4α inhibits HCC growth and progression. Specifically, pharmacological suppression of Bmal1 in HNF4α-deficient, BMAL1-positive HCC with REV-ERB agonist SR9009 impairs tumor cell proliferation and migration in a REV-ERB-dependent manner, while having no effect on healthy hepatocytes. Collectively, our results suggest that stratification of HCC based on HNF4α and BMAL1 expression may provide a new perspective on HCC properties and potential targeted therapeutics.

Effects of ginseng on short-chain fatty acids and intestinal microbiota in rats with spleen-qi deficiency based on gas chromatography-mass spectrometry and 16s rRNA technology.

RATIONALE: Spleen-qi deficiency syndrome, a common weakness syndrome in traditional Chinese medicine, results from insufficient spleen-qi levels. For centuries, ginseng has been relied upon as a traditional Chinese medicine to treat spleen-qi deficiency syndrome. Until now, the mechanism feature of ginseng in treating temper deficiency through intestinal bacteria and short-chain fatty acid (SCFA) metabolites has not been fully elucidated. METHODS: This study established a rat model of spleen-qi deficiency via multi-factor compound modeling that involved fatigue injury and a controlled diet. The content of SCFAs between different treatment groups was determined by gas chromatography-mass spectrometry. And the 16s rRNA sequencing technology was applied to reveal the effects of ginseng on the intestinal microecological environment of the rats. RESULTS: It was found that the ginseng treatment group exhibited the most remarkable regulatory effect on propionic acid, surpassing all other administration groups. Ginseng increased the relative abundance of beneficial bacteria and decreased that of harmful bacteria at the genus level in rats with spleen-qi deficiency syndrome. And propionic acid is significantly positively correlated with Lactobacillus level and significantly negatively correlated with uncultured_bacterium_f_Muribaculaceae (p < 0.05). n-Butyric acid is negatively correlated with the Faecalibaculum level (p < 0.01). n-Valeric acid is significantly negatively correlated with the Romboutsia level (p < 0.01). CONCLUSION: The mechanism of ginseng treatment for spleen-qi deficiency is elucidated from the perspective of gut microbiota and its metabolite SCFAs. It provides a new way for further development and utilization of ginseng and a theoretical basis.

Urine and serum metabolomics study of wild ginseng in rats with spleen-qi deficiency using rapid resolution liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

Patients with a spleen-qi deficiency often exhibit dysfunction in the metabolic system. Metabolites are considered the most direct reflection of individual physiological and pathological conditions and represent attractive candidates to provide deep insights into disease phenotypes. This study examines the potential therapeutic mechanism of wild ginseng on spleen-qi deficiency through the analysis of serum and urine metabolomics using rapid-resolution liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry. The reasons for the superiority of wild ginseng treatment over cultivated ginseng were also analyzed in depth. After wild ginseng intervention, anandamide, urobilinogen, aldosterone, and testosterone glucuronide were significantly reduced in serum. Meanwhile, argininosuccinic acid, L-cysteine, and seven other metabolites were significantly elevated in serum. Nine metabolites, including L-acetylcarnitine, and citrulline were elevated in the urine, and trimethylamine N-oxide, adrenic acid, and 10 other metabolites were reduced. Arginine biosynthesis, pantothenate and CoA biosynthesis, thiamin metabolism, taurine, and tryptophan metabolism pathways were mainly improved. Further analysis was conducted on the relationship between Lactobacillus and Akkermansia bacteria with metabolites, and it was found that they are mainly related to amino acid metabolites. This study provides strong theoretical support and direction for further explanation of the immune mechanism of wild ginseng and lays the foundation for future studies.

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

CSEA-DB: an omnibus for human complex trait and cell type associations.

During the past decade, genome-wide association studies (GWAS) have identified many genetic variants with susceptibility to several thousands of complex diseases or traits. The genetic regulation of gene expression is highly tissue-specific and cell type-specific. Recently, single-cell technology has paved the way to dissect cellular heterogeneity in human tissues. Here, we present a reference database for GWAS trait-associated cell type-specificity, named Cell type-Specific Enrichment Analysis DataBase (CSEA-DB, available at https://bioinfo.uth.edu/CSEADB/). Specifically, we curated total of 5120 GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. We further collected >900 000 cells from the leading consortia such as Human Cell Landscape, Human Cell Atlas, and extensive literature mining, including 752 tissue cell types from 71 adult and fetal tissues across 11 human organ systems. The tissues and cell types were annotated with Uberon and Cell Ontology. By applying our deTS algorithm, we conducted 10 250 480 times of trait-cell type associations, reporting a total of 598 (11.68%) GWAS traits with at least one significantly associated cell type. In summary, CSEA-DB could serve as a repository of association map for human complex traits and their underlying cell types, manually curated GWAS, and single-cell transcriptome resources.

De novo mutations disturb early brain development more frequently than common variants in schizophrenia.

Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment.

BACKGROUND: Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness. RESULTS: We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways. CONCLUSIONS: In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

Anti-Adhesive Properties of Calcium Alginate from Sargassum fusiforme against Particulate Matter-Induced Inflammation.

Fine dust generated by particulate matter (PM) pollution is a serious ecological issue in industrialized countries and causes disorders of the respiratory system and skin in humans. In the previous study, Sargassum fusiforme was treated with citric acid to remove heavy metals. In this study, the transfer of PM-mediated inflammatory responses through the skin to macrophages was evaluated. Moreover, the anti-adhesive effects of calcium alginate isolated from S. fusiforme (SFCA) against PM-induced inflammation were investigated. The structures of processing and unprocessing SFCA were then analyzed by Fourier-transform infrared spectroscopy (FT-IR), revealing minimal change after acid-processing. SFCA had protective effects both in PM-stimulated HaCaT keratinocytes and RAW 264.7 macrophages. In cellular environments, it was found that SFCA attenuated signal protein expressions such as inducible nitric oxide synthase (iNOS), cyclooxygenase (COX)-2, prostaglandin E2 (PGE2), and pro-inflammatory cytokines. Furthermore, macrophages were added to the culture medium of PM-stimulated keratinocytes to induce inflammation. SFCA was observed to significantly inhibit inflammatory responses; additionally, SFCA showed an in vivo anti-adhesive effect in zebrafish embryos.

Biomarkers of Mycoplasma pneumoniae pneumonia in children by urine metabolomics based on Q Exactive liquid chromatography/tandem mass spectrometry.

RATIONALE: Mycoplasma pneumoniae has become one of the common pathogens causing pediatric respiratory infections. In clinical diagnosis, throat swabs are very difficult to obtain from children, and there is a possibility of false positive results; hence, there are few clinically available diagnostic methods. METHODS: In this study, Q Exactive liquid chromatography/tandem mass spectrometry was used to analyze the metabolites in the urine of healthy children (HC) and M. pneumoniae pneumonia in children (MPPC) patients. A multivariate statistical analysis was performed to screen the differential metabolites. Based on the HMDB and KEGG, the possible metabolic pathways subject to biological alteration were identified. RESULTS: Compared with HC, 73 different metabolites in MPPC patients disrupted nine metabolic pathways through different change trends; after integrating various parameters, 20 significantly different metabolites were identified as MPPC potential biomarkers. Through the above two analysis modes, acetylphosphate and 2,5-dioxopentanoate were both screened out and used as potential biomarkers for the early diagnosis of MPPC for the first time. CONCLUSIONS: The characterization of 20 potential biomarkers provides a scientific basis for predicting and diagnosing MPPC. This article further indicates that urine metabolic profiling has great potential in diagnosing MPPC and can effectively prevent the disease from causing further deterioration.