scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.

scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning.

The identification of differentially expressed genes between different cell groups is a crucial step in analyzing single-cell RNA-sequencing (scRNA-seq) data. Even though various differential expression analysis methods for scRNA-seq data have been proposed based on different model assumptions and strategies recently, the differentially expressed genes identified by them are quite different from each other, and the performances of them depend on the underlying data structures. In this paper, we propose a new ensemble learning-based differential expression analysis method, scDEA, to produce a more stable and accurate result. scDEA integrates the P-values obtained from 12 individual differential expression analysis methods for each gene using a P-value combination method. Comprehensive experiments show that scDEA outperforms the state-of-the-art individual methods with different experimental settings and evaluation metrics. We expect that scDEA will serve a wide range of users, including biologists, bioinformaticians and data scientists, who need to detect differentially expressed genes in scRNA-seq data.

EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning.

MOTIVATION: Spatially resolved gene expression profiles are the key to exploring the cell type spatial distributions and understanding the architecture of tissues. Many spatially resolved transcriptomics (SRT) techniques do not provide single-cell resolutions, but they measure gene expression profiles on captured locations (spots) instead, which are mixtures of potentially heterogeneous cell types. Currently, several cell-type deconvolution methods have been proposed to deconvolute SRT data. Due to the different model strategies of these methods, their deconvolution results also vary. RESULTS: Leveraging the strengths of multiple deconvolution methods, we introduce a new weighted ensemble learning deconvolution method, EnDecon, to predict cell-type compositions on SRT data in this work. EnDecon integrates multiple base deconvolution results using a weighted optimization model to generate a more accurate result. Simulation studies demonstrate that EnDecon outperforms the competing methods and the learned weights assigned to base deconvolution methods have high positive correlations with the performances of these base methods. Applied to real datasets from different spatial techniques, EnDecon identifies multiple cell types on spots, localizes these cell types to specific spatial regions and distinguishes distinct spatial colocalization and enrichment patterns, providing valuable insights into spatial heterogeneity and regionalization of tissues. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/Zhangxf-ccnu/EnDecon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell-type annotation with accurate unseen cell-type identification using multiple references.

The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.

WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values.

The mechanisms controlling biological process, such as the development of disease or cell differentiation, can be investigated by examining changes in the networks of gene dependencies between states in the process. High-throughput experimental methods, like microarray and RNA sequencing, have been widely used to gather gene expression data, which paves the way to infer gene dependencies based on computational methods. However, most differential network analysis methods are designed to deal with fully observed data, but missing values, such as the dropout events in single-cell RNA-sequencing data, are frequent. New methods are needed to take account of these missing values. Moreover, since the changes of gene dependencies may be driven by certain perturbed genes, considering the changes in gene expression levels may promote the identification of gene network rewiring. In this study, a novel weighted differential network estimation (WDNE) model is proposed to handle multi-platform gene expression data with missing values and take account of changes in gene expression levels. Simulation studies demonstrate that WDNE outperforms state-of-the-art differential network estimation methods. When applied WDNE to infer differential gene networks associated with drug resistance in ovarian tumors, cell differentiation and breast tumor heterogeneity, the hub genes in the estimated differential gene networks can provide important insights into the underlying mechanisms. Furthermore, a Matlab toolbox, differential network analysis toolbox, was developed to implement the WDNE model and visualize the estimated differential networks.

Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technologies have been testified revolutionary for their promotion on the profiling of single-cell transcriptomes at single-cell resolution. Excess zeros due to various technical noises, called dropouts, will mislead downstream analyses. Therefore, it is crucial to have accurate imputation methods to address the dropout problem. RESULTS: In this article, we develop a new dropout imputation method for scRNA-seq data based on multi-objective optimization. Our method is different from existing ones, which assume that the underlying data has a preconceived structure and impute the dropouts according to the information learned from such structure. We assume that the data combines three types of latent structures, including the horizontal structure (genes are similar to each other), the vertical structure (cells are similar to each other) and the low-rank structure. The combination weights and latent structures are learned using multi-objective optimization. And, the weighted average of the observed data and the imputation results learned from the three types of structures are considered as the final result. Comprehensive downstream experiments show the superiority of our method in terms of recovery of true gene expression profiles, differential expression analysis, cell clustering and cell trajectory inference. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/Zhangxf-ccnu/scMOO and https://zenodo.org/record/5785195. The codes to reproduce the downstream analyses in this article can be found at https://github.com/Zhangxf-ccnu/scMOO_experiments_codes and https://zenodo.org/record/5786211. The detailed list of data sets used in the present study is represented in Supplementary Table S1 in the Supplementary materials. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Novel Tu translation elongation factor, mitochondrial (TUFM) homozygous variant in a consanguineous family with premature ovarian insufficiency.

Premature ovarian insufficiency (POI) is a clinical syndrome of ovarian dysfunction characterized by cessation of menstruation occurring before the age of 40 years. The genetic causes of idiopathic POI remain unclear. Here we recruited a POI patient from a consanguineous family to screen for potential pathogenic variants associated with POI. Genetic variants of the pedigree were screened using whole-exome sequencing analysis and validated through direct Sanger sequencing. A homozygous variant in TUFM (c.524G>C: p.Gly175Ala) was identified in this family. TUFM (Tu translation elongation factor, mitochondrial) is a nuclear-encoded mitochondrial protein translation elongation factor that plays a critical role in maintaining normal mitochondrial function. The variant position was highly conserved among species and predicted to be disease causing. Our in vitro functional studies demonstrated that this variant causes decreased TUFM protein expression, leading to mitochondrial dysfunction and impaired autophagy activation. Moreover, we found that mice with targeted Tufm variant recapitulated the phenotypes of human POI. Thus, this is the first report of a homozygous pathogenic TUFM variant in POI. Our findings highlighted the essential role of mitochondrial genes in folliculogenesis and ovarian function maintenance.

Elevated cell-free mitochondria DNA level of patients with premature ovarian insufficiency.

BACKGROUND: Premature ovarian insufficiency (POI) patients present with a chronic inflammatory state. Cell-free mitochondria DNA (cf-mtDNA) has been explored as a reliable biomarker for estimating the inflammation-related disorders, however, the cf-mtDNA levels in POI patients have never been measured. Therefore, in the presenting study, we aimed to evaluate the levels of cf-mtDNA in plasma and follicular fluid (FF) of POI patients and to determine a potential role of cf-mtDNA in predicting the disease progress and pregnancy outcomes. METHODS: We collected plasma and FF samples from POI patients, biochemical POI (bPOI) patients and control women. Quantitative real-time PCR was used to measure the ratio of mitochondrial genome to nuclear genome of cf-DNAs extracted from the plasma and FF samples. RESULTS: The plasma cf-mtDNA levels, including COX3, CYB, ND1 and mtDNA79, were significantly higher in overt POI patients than those in bPOI patients or control women. The plasma cf-mtDNA levels were weakly correlated with ovarian reserve, and could not be improved by regular hormone replacement therapy. The levels of cf-mtDNA in FF, rather than those in plasma, exhibited the potential to predict the pregnancy outcomes, although they were comparable among overt POI, bPOI and control groups. CONCLUSIONS: The increased plasma cf-mtDNA levels in overt POI patients indicated its role in the progress of POI and the FF cf-mtDNA content may hold the value in predicting pregnancy outcomes of POI patients.

[Optimization of ethanol reflux extraction process of Ziziphi Spinosae Semen- Schisandrae Sphenantherae Fructus based on network pharmacology combined with response surface methodology].

The present study optimized the ethanol extraction process of Ziziphi Spinosae Semen-Schisandrae Sphenantherae Fructus drug pair by network pharmacology and Box-Behnken method. Network pharmacology and molecular docking were used to screen out and verify the potential active components of Ziziphi Spinosae Semen-Schisandrae Sphenantherae Fructus, and the process evaluation indexes were determined in light of the components of the content determination under Ziziphi Spinosae Semen and Schisandrae Sphenantherae Fructus in the Chinese Pharmacopoeia(2020 edition). The analytic hierarchy process(AHP) was used to determine the weight coefficient of each component, and the comprehensive score was calculated as the process evaluation index. The ethanol extraction process of Ziziphi Spinosae Semen-Schisandrae Sphenantherae Fructus was optimized by the Box-Behnken method. The core components of the Ziziphi Spinosae Semen-Schisandrae Sphenantherae Fructus drug pair were screened out as spinosin, jujuboside A, jujuboside B, schisandrin, schisandrol, schisandrin A, and schisandrin B. The optimal extraction conditions obtained by using the Box-Behnken method were listed below: extraction time of 90 min, ethanol volume fraction of 85%, and two times of extraction. Through network pharmacology and molecular docking, the process evaluation indexes were determined, and the optimized process was stable, which could provide an experimental basis for the production of preparations containing Ziziphi Spinosae Semen-Schisandrae Sphenantherae Fructus.

Differential network analysis by simultaneously considering changes in gene interactions and gene expression.

MOTIVATION: Differential network analysis is an important tool to investigate the rewiring of gene interactions under different conditions. Several computational methods have been developed to estimate differential networks from gene expression data, but most of them do not consider that gene network rewiring may be driven by the differential expression of individual genes. New differential network analysis methods that simultaneously take account of the changes in gene interactions and changes in expression levels are needed. RESULTS: : In this article, we propose a differential network analysis method that considers the differential expression of individual genes when identifying differential edges. First, two hypothesis test statistics are used to quantify changes in partial correlations between gene pairs and changes in expression levels for individual genes. Then, an optimization framework is proposed to combine the two test statistics so that the resulting differential network has a hierarchical property, where a differential edge can be considered only if at least one of the two involved genes is differentially expressed. Simulation results indicate that our method outperforms current state-of-the-art methods. We apply our method to identify the differential networks between the luminal A and basal-like subtypes of breast cancer and those between acute myeloid leukemia and normal samples. Hub nodes in the differential networks estimated by our method, including both differentially and nondifferentially expressed genes, have important biological functions. AVAILABILITY AND IMPLEMENTATION: All the datasets underlying this article are publicly available. Processed data and source code can be accessed through the Github repository at https://github.com/Zhangxf-ccnu/chNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Urokinase-type plasminogen activator receptor (uPAR) as a therapeutic target in cancer.

Urokinase-type plasminogen activator receptor (uPAR) is an attractive target for the treatment of cancer, because it is expressed at low levels in healthy tissues but at high levels in malignant tumours. uPAR is closely related to the invasion and metastasis of malignant tumours, plays important roles in the degradation of extracellular matrix (ECM), tumour angiogenesis, cell proliferation and apoptosis, and is associated with the multidrug resistance (MDR) of tumour cells, which has important guiding significance for the judgement of tumor malignancy and prognosis. Several uPAR-targeted antitumour therapeutic agents have been developed to suppress tumour growth, metastatic processes and drug resistance. Here, we review the recent advances in the development of uPAR-targeted antitumor therapeutic strategies, including nanoplatforms carrying therapeutic agents, photodynamic therapy (PDT)/photothermal therapy (PTT) platforms, oncolytic virotherapy, gene therapy technologies, monoclonal antibody therapy and tumour immunotherapy, to promote the translation of these therapeutic agents to clinical applications.

Sphingobacterium faecale sp. nov., a 1-aminocyclopropane-1-carboxylate deaminase producing bacterium isolated from camel faeces.

An investigation of the diversity of 1-aminocyclopropane-1-carboxylate deaminase producing bacteria associated with camel faeces revealed the presence of a novel bacterial strain designated C459-1T. It was Gram-stain-negative, short-rod-shaped and non-motile. Strain C459-1T was observed to grow optimally at 35 °C, at pH 7.0 and in the presence of 0 % NaCl on Luria-Bertani agar medium. The cells were found to be positive for catalase and oxidase activities. The major fatty acids (>10 %) were identified as iso-C15 : 0, summed feature 3 (C16 : 1 ω6c and/or C16 : 1 ω7c) and iso-C17 : 0 3-OH. The predominant menaquinone was MK-7. The major polar lipids consisted of phosphatidylethanolamine, one sphingophospholipid, two unknown aminophospholipids, three unknown glycolipids and five unknown lipids. The genomic DNA G+C content was 40.3 mol%. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain C459-1T was affiliated with the genus Sphingobacterium and had the highest sequence similarity to Sphingobacterium tabacisoli h337T (97.0 %) and Sphingobacterium paucimobilis HER1398T (95.6 %). The average nucleotide identity and digital DNA-DNA hybridization values between strain C459-1T and S. tabacisoli h337T were 83.8 and 33.8 %, respectively. Phenotypic characteristics including enzyme activities and carbon source utilization differentiated strain C459-1T from other Sphingobacterium species. Based on its phenotypic, chemotaxonomic and phylogenetic properties, strain C459-1T represents a novel species of the genus Sphingobacterium, for which the name Sphingobacterium faecale sp. nov. is proposed, with strain is C459-1T (CGMCC 1.18716T=KCTC 82381T) as the type strain.

Review targeted drug delivery systems for norcantharidin in cancer therapy.

Norcantharidin (NCTD) is a demethylated derivative of cantharidin (CTD), the main anticancer active ingredient isolated from traditional Chinese medicine Mylabris. NCTD has been approved by the State Food and Drug Administration for the treatment of various solid tumors, especially liver cancer. Although NCTD greatly reduces the toxicity of CTD, there is still a certain degree of urinary toxicity and organ toxicity, and the poor solubility, short half-life, fast metabolism, as well as high venous irritation and weak tumor targeting ability limit its widespread application in the clinic. To reduce its toxicity and improve its efficacy, design of targeted drug delivery systems based on biomaterials and nanomaterials is one of the most feasible strategies. Therefore, this review focused on the studies of targeted drug delivery systems combined with NCTD in recent years, including passive and active targeted drug delivery systems, and physicochemical targeted drug delivery systems for improving drug bioavailability and enhancing its efficacy, as well as increasing drug targeting ability and reducing its adverse effects.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. RESULTS: In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity.

MOTIVATION: Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account. RESULTS: In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/Zhangxf-ccnu/NETI2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks.

MOTIVATION: Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information. RESULTS: In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks. AVAILABILITY AND IMPLEMENTATION: The package is available at https://github.com/happyalfred2016/GRGMF. CONTACT: leouyang@szu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

miR-210 Participates in Hepatic Ischemia Reperfusion Injury by Forming a Negative Feedback Loop With SMAD4.

BACKGROUND AND AIMS: Hepatic ischemia-reperfusion (IR) injury is a major complication of liver transplantation, resection, and hemorrhagic shock. Hypoxia is a key pathological event associated with IR injury. MicroRNA-210 (miR-210) has been characterized as a micromanager of hypoxia pathway. However, its function and mechanism in hepatic IR injury is unknown. APPROACH AND RESULTS: In this study, we found miR-210 was induced in liver tissues from patients subjected to IR-related surgeries. In a murine model of hepatic IR, the level of miR-210 was increased in hepatocytes but not in nonparenchymal cells. miR-210 deficiency remarkably alleviated liver injury, cell inflammatory responses, and cell death in a mouse hepatic IR model. In vitro, inhibition of miR-210 decreased hypoxia/reoxygenation (HR)-induced cell apoptosis of primary hepatocytes and LO2 cells, whereas overexpression of miR-210 increased cells apoptosis during HR. Mechanistically, miR-210 directly suppressed mothers against decapentaplegic homolog 4 (SMAD4) expression under normoxia and hypoxia condition by directly binding to the 3' UTR of SMAD4. The pro-apoptotic effect of miR-210 was alleviated by SMAD4, whereas short hairpin SMAD4 abrogated the anti-apoptotic role of miR-210 inhibition in primary hepatocytes. Further studies demonstrated that hypoxia-induced SMAD4 transported into nucleus, in which SMAD4 directly bound to the promoter of miR-210 and transcriptionally induced miR-210, thus forming a negative feedback loop with miR-210. CONCLUSIONS: Our study implicates a crucial role of miR-210-SMAD4 interaction in hepatic IR-induced cell death and provides a promising therapeutic approach for liver IR injury.

Sphingobacterium lumbrici sp. nov., a novel bacterium isolated from wormcast of Eisenia foetida.

A novel Gram-stain-negative, rod-shaped, non-motile, yellowish bacterium, designated strain 1.3611T, was isolated from the wormcast of Eisenia foetida. The strain grew optimally at 30-37 ℃, at pH 7.0 and with 0-1.0 % (w/v) NaCl. Based on the results of 16S rRNA gene sequence and phylogenetic analyses, strain 1.3611T showed the highest degree of 16S rRNA gene sequence similarity to Sphingobacterium olei HAL-9T (97.0 %), followed by Sphingobacterium alkalisoli Y3L14T (95.8 %). The respiratory quinone of strain 1.3611T was menaquinone-7 (MK-7) and its major cellular fatty acids were iso-C15 : 0 (41.3 %), summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c, 22.1 %) and iso-C17 : 0 3-OH (16.2 %). The major polar lipids were sphingophospholipid, phosphatidylethanolamine, four unidentified glycolipids, two unidentified phospholipids and five unidentified polar lipids. The genomic DNA G+C content was 39.0 mol%. The digital DNA-DNA hybridization and average nucleotide identity values between the genomes of strain 1.3611T and S. olei HAL-9T were 37.9 and 88.9 %, respectively. According to the phenotypic and chemotaxonomic phylogenetic results, strain 1.3611T should represent a novel species of the genus Sphingobacterium, for which the name Sphingobacterium lumbrici sp. nov. is proposed, with strain 1.3611T (=KCTC 62980T=CCTCC AB 2018349T) as the type strain.

DNA methylation and gene expression profiles to identify childhood atopic asthma associated genes.

BACKGROUND: Asthma is a chronic inflammatory disorder of the airways involving many different factors. This study aimed to screen for the critical genes using DNA methylation/CpGs and miRNAs involved in childhood atopic asthma. METHODS: DNA methylation and gene expression data (Access Numbers GSE40732 and GSE40576) were downloaded from the Gene Expression Omnibus database. Each set contains 194 peripheral blood mononuclear cell (PBMC) samples of 97 children with atopic asthma and 97 control children. Differentially expressed genes (DEGs) with DNA methylation changes were identified. Pearson correlation analysis was used to select genes with an opposite direction of expression and differences in methylation levels, and then Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Protein-protein interaction network and miRNA-target gene regulatory networks were then constructed. Finally, important genes related to asthma were screened. RESULTS: A total of 130 critical DEGs with DNA methylation changes were screened from children with atopic asthma and compared with control samples from healthy children. GO and KEGG pathway enrichment analysis found that critical genes were primarily related to 24 GO terms and 10 KEGG pathways. In the miRNA-target gene regulatory networks, 9 KEGG pathways were identified. Analysis of the miRNA-target gene network noted an overlapping KEGG signaling pathway, hsa04060: cytokine-cytokine receptor interaction, in which the gene CCL2, directly related to asthma, was involved. This gene is targeted by eight asthma related miRNAs (hsa-miR-206, hsa-miR-19a, hsa-miR-9,hsa-miR-22, hsa-miR-33b, hsa-miR-122, hsa-miR-1, and hsa-miR-23b). The genes IL2RG and CCl4 were also involved in this pathway. CONCLUSIONS: The present study provides a novel insight into the underlying molecular mechanism of childhood atopic asthma.

[Quality evaluation of Aralia taibaiensis based on spectrum-activity relationship].

A spectrum-activity relationship is established with high performance liquid chromatography(HPLC) fingerprints and the in vitro antioxidant activity to improve the quality evaluation system of Aralia taibaiensis. The HPLC profiles of 12 batches of samples were collected, and the similarity evaluation, heat map analysis and principal component analysis were conducted for the chemometric study of the fingerprint data. Combined with grey correlation analysis, the contributions of the common peaks in the fingerprints to the antioxidant activity were clarified, and the important peaks reflecting the efficacy were identified. The results showed that 17 common peaks were found in 12 batches of A. taibaiensis samples, and 6 of them were identified as saponins. Similarity evaluation, heat map analysis and principal component analysis roughly classified the A. taibaiensis herbs into two categories, i.e.,(1) S1-S10, S12 and(2) S11. Twelve batches of samples showed different antioxidant activities in a dose-dependent manner. In particular, S9 had the strongest antioxidant activity, while S11 was the weakest in antioxidant capacity, which was basically consistent with the overall score results. The results of grey correlation analysis demonstrated that the 17 common peaks scavenged DPPH radicals in the following order: X_3>X_(17)>X_4>X_8>X_7>X_(13)>X_2>X_6>X_(11)>X_(10)>X_(16)>X_(12)>X_9>X_5>X_(14)>X_1>X_(15), and scavenged ABTS radicals in the order of X_4>X_3>X_7>X_8>X_2>X_(17)>X_(13)>X_6>X_(16)>X_(11)>X_5>X_(12)>X_(10)>X_9>X_(14)>X_1>X_(15). Among them, X_3, X_4, X_7(araloside C), X_8 and X_(17) were the important peaks reflecting the efficacy of A. taibaiensis, which were basically consistent with those contained in the principal component 1. In this study, the correlation between the HPLC fingerprints of 12 batches of A. taibaiensis and its antioxidant activity provides a reference for the Q-marker screening and quality control of A. taibaiensis.