[Effects of salt stress on bacterial community composition and diversity in rhizosphere soil of Bletilla striata]. / ç›èƒè¿«å¯¹ç™½åŠæ ¹é™ ç»†èŒç¾¤è½ç»„æˆåŠå¤šæ ·æ€§çš„å½±å“.

Salinization environment affects the normal growth and development of plants, as well as the microbial community in the rhizosphere. To explore the succession dynamics of bacterial communities in the rhizosphere soil of Bletilla striata under salt stress condition, we performed 16S rRNA high-throughput sequencing to determine the bacterial community composition and diversity of B. striata in the rhizosphere under different salt stress concentrations, measured the effects of salt stress on the growth and development of B. striata and soil physicochemical pro-perties, and analyzed the correlation between community composition of rhizosphere bacteria and the soil environmental factors. The results showed that compared with the control, salt stress reduced growth rate and health degree of B. striata, and significantly decreased the content of soil organic matter, nitrogen and phosphorus. Under the salt stress treatment, species diversity and evenness of the bacterial communities in the rhizosphere of B. striata showed a trend of first decreasing and then increasing. There were significant differences in the relative abundance and variation trends of the dominant bacterial taxa in the rhizosphere soil of B. striata at the phylum and class levels between the control and the salt stress treatments. Salt stress intensity and duration were important factors affecting bacterial community composition in the rhizosphere soil of B. striata. Soil organic matter, available nitrogen, and total phosphorus content were key environmental factors affecting the structure of rhizosphere bacterial community composition. Functional genes related to cytoskeleton, cell motility, substance metabolism and signal transduction mechanisms may be involved in the adaptation and stress response of bacterial communities to salt stress. This study would provide theoretical basis and reference for the cultivation management of B. striatain saline area.

TGF-ß1-triggered BMI1 and SMAD2 cooperatively regulate miR-191 to modulate bone formation.

Transforming growth factor ß 1 (TGF-ß1), as the most abundant signaling molecule in bone matrix, is essential for bone homeostasis. However, the signaling transduction of TGF-ß1 in the bone-forming microenvironment remains unknown. Here, we showed that microRNA-191 (miR-191) was downregulated during osteogenesis and further decreased by osteo-favoring TGF-ß1 in bone marrow mesenchymal stem cells (BMSCs). MiR-191 was lower in bone tissues from children than in those from middle-aged individuals and it was negatively correlated with collagen type I alpha 1 chain (COL1A1). MiR-191 depletion significantly increased osteogenesis and bone formation in vivo. Hydrogels embedded with miR-191-low BMSCs displayed a powerful bone repair effect. Mechanistically, transcription factors BMI1 and SMAD2 coordinately controlled miR-191 level. In detail, BMI1 and pSMAD2 were both upregulated by TGF-ß1 under osteogenic condition. SMAD2 activated miR-191 transcription, while BMI1 competed with SMAD2 for binding to miR-191 promoter region, thus disturbing the activation of SMAD2 on miR-191 and reducing miR-191 level. Altogether, our findings reveal that miR-191 regulated by TGF-ß1-induced BMI1 and SMAD2 negatively modulated bone formation and regeneration, and inhibition of miR-191 might be therapeutically useful to enhance bone repair in clinic.

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.

The investigation of thermal stability and GC-MS analysis of Acorus tatarinowii and Atractylodes lancea volatile oils treated by ß cyclodextrin inclusion and Pickering emulsion technologies.

Objective: To investigate the stability of Acorus tatarinowii and Atractylodes lancea essential oils (ATaAL-EO) under a hot environment at 60 °C, and to analyze the differences in component, quantity, and quality changes, as well as variations in the main components, under different treatment methods of crude oil, ß-cyclodextrin inclusion of ATaAL-EO, and Pickering emulsion, to improve the stability and quality of ATaAL-EO. Methods: The stability of the ATaAL-EO group, the ß-cyclodextrin inclusion ATaAL-EO group, and the Pickering emulsion group were investigated under a 60 °C heat environment. Volatile oil retention rate and peroxide value were collected and measured. The volatile oil components of each group were determined by GC-MS, and t-tests were used to screen for differential components. PCA plots for each group were constructed using the OmicShare online platform. Line plots were generated using the Rmisc and reshape2 packages. Upset Venn diagrams under different hot environments were created using the OmicShare online platform to identify quantitative and qualitative changing components and heat map stack plots for newly generated compounds and connected line plots for disappearing compounds were produced for each group. Boxplots for the main component compounds under different hot environments were generated using the reshape2 and ggplot2 packages. Results: In a hot environment of 60 °C, the ß-cyclodextrin inclusion ATaAL-EO and Pickering emulsion group with 1, 3, and 8 h of placement showed higher retention and lower oxidation degree compared to the stability of the ATaAL-EO group. GC-MS analysis results showed that the stability of volatile components in the Pickering emulsion group and ß-cyclodextrin inclusion ATaAL-EO group was significantly improved compared to the crude oil group. Conclusion: ß-cyclodextrin inclusion complexes with ATaAL-EO, as well as Pickering emulsions, can significantly enhance the stability and quality of ATaAL-EO. Pickering emulsions have more advantages.

Advanced oxidation protein products attenuate the autophagy-lysosome pathway in ovarian granulosa cells by modulating the ROS-dependent mTOR-TFEB pathway.

Oxidative stress dysfunction has recently been found to be involved in the pathogenesis of premature ovarian insufficiency (POI). Previously, we found that advanced oxidation protein products (AOPPs) in plasma were elevated in women with POI and had an adverse effect on granulosa cell proliferation. However, the mechanism underlying the effects of AOPPs on autophagy-lysosome pathway regulation in granulosa cells remains unclear. In this study, the effect of AOPPs on autophagy and lysosomal biogenesis and the underlying mechanisms were explored by a series of in vitro experiments in KGN and COV434 cell lines. AOPP-treated rat models were employed to determine the negative effect of AOPPs on the autophagy-lysosome systems in vivo. We found that increased AOPP levels activated the mammalian target of rapamycin (mTOR) pathway, and inhibited the autophagic response and lysosomal biogenesis in KGN and COV434 cells. Furthermore, scavenging of reactive oxygen species (ROS) with N-acetylcysteine and blockade of the mTOR pathway with rapamycin or via starvation alleviated the AOPP-induced inhibitory effects on autophagy and lysosomal biogenesis, suggesting that these effects of AOPPs are ROS-mTOR dependent. The protein expression and nuclear translocation of transcription factor EB (TFEB), the key regulator of lysosomal and autophagic function, were also impaired by the AOPP-activated ROS-mTOR pathway. In addition, TFEB overexpression attenuated the AOPP-induced impairment of autophagic flux and lysosomal biogenesis in KGN and COV434 cells. Chronic AOPP stimulation in vivo also impaired autophagy and lysosomal biogenesis in granulosa cells of rat ovaries. The results highlight that AOPPs lead to impairment of autophagic flux and lysosomal biogenesis via ROS-mTOR-TFEB signaling in granulosa cells and participate in the pathogenesis of POI.

Heterozygosity analysis of spontaneous 2n female gametes and centromere mapping of the diploid Hevea brasiliensis based on full-sib triploid populations.

KEY MESSAGE: Unreduced megagametophytes via second-division restitution were confirmed through heterozygosity analysis, and four candidate physical centromeres of rubber were located for the first time. The evaluation of maternal heterozygosity restitution (MHR) is vital in identifying the mechanism of 2n gametogenesis and assessing the utilization value of 2n gametes. In this study, three full-sib triploid populations were employed to evaluate the MHR of 2n female gametes of rubber tree clone GT1 and to confirm their genetic derivation. The 2n female gametes of GT1 were derived from second-division restitution (SDR) and transmitted more than half of the parental heterozygosity. In addition, low recombination frequency markers were developed, and four candidate physical centromeres of rubber tree were located for the first time. The confirmation that 2n female gametes of rubber tree clone GT1 are derived from SDR provides insights into the molecular mechanisms of 2n gametogenesis. In addition, the identified centromere location will aid in the development of centromeric markers for the rapid identification of the 2n gametogenesis mechanism.

ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction.

Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.

Usefulness of analyzing endoscopic features in identifying the colorectal serrated sessile lesions with and without dysplasia.

BACKGROUND: Sessile serrated lesions (SSLs) are often missed on colonoscopy, and studies have shown this to be an essential cause of interstitial colorectal cancer. The SSLs with dysplasia (SSL-D+), in particular, have a faster rate of carcinogenesis than conventional tubular adenomas. Therefore, there is a clinical need for some endoscopic features with independent diagnostic value for SSL-D+s to assist endoscopists in making immediate diagnoses, thus improving the quality of endoscopic examination and treatment. AIM: To compare the characteristics of SSLs, including those with and without dysplasia (SSL-D+ and SSL-D-), based on white light and image-enhanced endoscopy, to achieve an immediate differential diagnosis for endoscopists. METHODS: From January 2017 to February 2023, cases of colorectal SSLs confirmed by colonoscopy and histopathology at the Gastrointestinal Endoscopy Center of Beijing Tsinghua Changgung Hospital were collected. The general, endoscopic, and histopathological data were reviewed and analyzed to determine the diagnostic utility. Univariate analysis was used to find potential diagnostic factors, and then multivariate regression analysis was performed to derive endoscopic features with independent diagnostic values for the SSL-D+. RESULTS: A total of 228 patients with 253 lesions were collected as a result. There were 225 cases of colorectal SSL-D-s and 28 cases of SSL-D+s. Compared to the colorectal SSL-D-, the SSL-D+ was more common in the right colon (P = 0.027) with complex patterns of depression, nodule, and elevation based on cloud-like surfaces (P = 0.003), reddish (P < 0.001), microvascular varicose (P < 0.001), and mixed type (Pit II, II-O, IIIL, IV) of crypt opening based on Pit II-O (P < 0.001). Multifactorial logistic regression analysis indicated that lesions had a reddish color [odds ratio (OR) = 18.705, 95% confidence interval (CI): 3.684-94.974], microvascular varicose (OR = 6.768, 95%CI: 1.717-26.677), and mixed pattern of crypt opening (OR = 20.704, 95%CI: 2.955-145.086) as the independent predictors for SSL-D+s. CONCLUSION: The endoscopic feature that has independent diagnostic value for SSL-D+ is a reddish color, microvascular varicose, and mixed pattern of crypt openings.

Development of curcumin-loaded galactosylated chitosan-coated nanoparticles for targeted delivery of hepatocellular carcinoma.

Curcumin (CUR) has good antitumor effects, but its poor aqueous solubility severely limits its clinical application and the systemic nonspecific distribution of the free drug in tumor patients is a key therapeutic challenge. In order to overcome the limitations of free drugs and improve the therapeutic efficacy, we developed novel galactosylated chitosan (GC)-modified nanoparticles (GC@NPs) based on poly (ethylene glycol) methyl ether-block-poly (lactide-co-glycolide) (PEG-PLGA), which can target asialoglycoprotein receptor (ASGPR) expressed on hepatocellular carcinoma cells and have excellent biocompatibility. The results showed that the drug loading (DL) of CUR was approximately 4.56 %. A favorable biosafety profile was maintained up to concentrations of 500 µg/mL. Furthermore, in vitro cellular assays showed that GC@NPs could be efficiently internalized by HepG2 cells via ASGPR-mediated endocytosis and successfully released CUR for chemotherapy. More importantly, in vivo anti-tumor experiments revealed that GC@NPs were able to accumulate effectively within tumor sites through EPR effect and ASGPR-mediated endocytosis, leading to superior inhibition of tumor growth compared to free CUR. Overall, GC@NPs are a promising CUR nanocarrier for enhanced tumor therapy with a good biosafety profile.

Targeted Drug Delivery Systems for Curcumin in Breast Cancer Therapy.

Breast cancer (BC) is the most prevalent type of cancer in the world and the main reason women die from cancer. Due to the significant side effects of conventional treatments such as chemotherapy and radiotherapy, the search for supplemental and alternative natural drugs with lower toxicity and side effects is of interest to researchers. Curcumin (CUR) is a natural polyphenol extracted from turmeric. Numerous studies have demonstrated that CUR is an effective anticancer drug that works by modifying different intracellular signaling pathways. CUR's therapeutic utility is severely constrained by its short half-life in vivo, low water solubility, poor stability, quick metabolism, low oral bioavailability, and potential for gastrointestinal discomfort with high oral doses. One of the most practical solutions to the aforementioned issues is the development of targeted drug delivery systems (TDDSs) based on nanomaterials. To improve drug targeting and efficacy and to serve as a reference for the development and use of CUR TDDSs in the clinical setting, this review describes the physicochemical properties and bioavailability of CUR and its mechanism of action on BC, with emphasis on recent studies on TDDSs for BC in combination with CUR, including passive TDDSs, active TDDSs and physicochemical TDDSs.

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.

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.

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.

Effects of a false-positive result in newborn congenital hypothyroidism screening on parents in Guangxi, China.

Background: As more than 500,000 neonates participate in newborn congenital hypothyroidism (CH) screening in Guangxi Zhuang Autonomous Region each year, the overall number of false-positive (FP) cases has increased. We aim to assess the parental stress in parents of neonates with FP CH results in Guangxi, find out the influence factors related to demographics, and provide the basis for personalized health education. Methods: The parents of neonates with FP CH results were invited to participate in the FP group, and the parents of neonates with all negative results were invited to participate in the control group. The parents completed a questionnaire on demographics, knowledge of CH, and the parental stress index (PSI) in the hospital for the first time. The follow-up visits for PSI were conducted 3, 6, and 12 months afterward through telephone and online. Results: A total of 258 and 1,040 parents participated in the FP and control groups, respectively. The parents in the FP group had better knowledge of CH and higher PSI scores than the parents in the control group. The result of logistic regression showed that the major influence factors related to the knowledge of CH were FP experience and source of knowledge. The parents in the FP group who were well-informed during the recall phone call had lower PSI scores than the other parents. The parents in the FP group showed decreasing PSI scores gradually in follow-up visits. Conclusion: The results suggested that FP screening results may affect parental stress and parent-child relationship. FP results increased the stress on the parents and increased their knowledge of CH passively.

[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.

Inferring cancer common and specific gene networks via multi-layer joint graphical model.

Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which is a crucial step towards precision cancer diagnosis and treatment. In this study, we propose a new sparse regularized multi-layer decomposition graphical model to jointly estimate the gene networks of multiple cancer types. Our model can handle various types of gene expression data and decomposes each cancer-type-specific network into three components, i.e., globally shared, partially shared and cancer-type-unique components. By identifying the globally and partially shared gene network components, our model can explore the heterogeneous similarities between different cancer types, and our identified cancer-type-unique components can help to reveal the regulatory mechanisms unique to each cancer type. Extensive experiments on synthetic data illustrate the effectiveness of our model in joint estimation of multiple gene networks. We also apply our model to two real data sets to infer the gene networks of multiple cancer subtypes or cell lines. By analyzing our estimated globally shared, partially shared, and cancer-type-unique components, we identified a number of important genes associated with common and specific regulatory mechanisms across different cancer types.

Plasma metabolomic characterization of premature ovarian insufficiency.

BACKGROUND: Premature ovarian insufficiency (POI) patients are predisposed to metabolic disturbances, including in lipid metabolism and glucose metabolism, and metabolic disorders appear to be a prerequisite of the typical long-term complications of POI, such as cardiovascular diseases or osteoporosis. However, the metabolic changes underlying the development of POI and its subsequent complications are incompletely understood, and there are few studies characterizing the disturbed metabolome in POI patients. The aim of this study was to characterize the plasma metabolome in POI by using ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) metabolomics and to evaluate whether these disturbances identified in the plasma metabolome relate to ovarian reserve and have diagnostic value in POI. METHODS: This observational study recruited 30 POI patients and 30 age- and body mass index (BMI)-matched controls in the Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, from January 2018 to October 2020. Fasting venous blood was collected at 9:00 am on days 2-4 of the menstrual cycle and centrifuged for analysis. An untargeted quantitative metabolomic analysis was performed using UHPLC-MS/MS. RESULTS: Our study identified 48 upregulated and 21 downregulated positive metabolites, and 13 upregulated and 48 downregulated negative metabolites in the plasma of POI patients. The differentially regulated metabolites were involved in pathways such as caffeine metabolism and ubiquinone and other terpenoid-quinone biosynthesis. Six metabolites with an AUC value > 0.8, including arachidonoyl amide, 3-hydroxy-3-methylbutanoic acid, dihexyl nonanedioate, 18-HETE, cystine, and PG (16:0/18:1), were correlated with ovarian reserve and thus have the potential to be diagnostic biomarkers of POI. CONCLUSION: This UHPLC-MS/MS untargeted metabolomics study revealed differentially expressed metabolites in the plasma of patients with POI. The differential metabolites may not only be involved in the aetiology of POI but also contribute to its major complications. These findings offer a panoramic view of the plasma metabolite changes caused by POI, which may provide useful diagnostic and therapeutic clues for POI disease.

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.

scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation.

The single-cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. We develop scTSSR2, a new imputation method that combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.

Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network.

OBJECTIVE: To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy. METHODS: GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT-qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software. RESULTS: A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2, MME, SLC14A1, and TRIM44 were identified as hub genes. Three OP-associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules. CONCLUSION: An mRNA-miRNA-circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment.