CCR7 and CD48 as Predicted Targets in Acute Rejection Related to M1 Macrophage after Pediatric Kidney Transplantation.

Background: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research. Materials and Methods: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes. Results: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-κB signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene. Conclusions: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Identification and validation of miR-29b-3p and LIN7A as important diagnostic markers for bone non-union by WGCNA.

Bone non-union is a common fracture complication that can severely impact patient outcomes, yet its mechanism is not fully understood. This study used differential analysis and weighted co-expression network analysis (WGCNA) to identify susceptibility modules and hub genes associated with fracture healing. Two datasets, GSE125289 and GSE213891, were downloaded from the GEO website, and differentially expressed miRNAs and genes were analysed and used to construct the WGCNA network. Gene ontology (GO) analysis of the differentially expressed genes showed enrichment in cytokine and inflammatory factor secretion, phagocytosis, and trans-Golgi network regulation pathways. Using bioinformatic site prediction and crossover gene search, miR-29b-3p was identified as a regulator of LIN7A expression that may negatively affect fracture healing. Potential miRNA-mRNA interactions in the bone non-union mechanism were explored, and miRNA-29-3p and LIN7A were identified as biomarkers of skeletal non-union. The expression of miRNA-29b-3p and LIN7A was verified in blood samples from patients with fracture non-union using qRT-PCR and ELISA. Overall, this study identified characteristic modules and key genes associated with fracture non-union and provided insight into its molecular mechanisms. Downregulated miRNA-29b-3p was found to downregulate LIN7A protein expression, which may affect the healing process after fracture in patients with bone non-union. These findings may serve as a prognostic biomarker and potential therapeutic target for bone non-union.

Identification of core genes shared by ischemic stroke and myocardial infarction using an integrated approach.

BACKGROUND: Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI. METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape. RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted. CONCLUSION: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.

Decoding dysregulated genes, molecular pathways and microRNAs involved in cervical cancer.

BACKGROUND: The present study aimed to identify dysregulated genes, molecular pathways, and regulatory mechanisms in human papillomavirus (HPV)-associated cervical cancers. We have investigated the disease-associated genes along with the Gene Ontology, survival prognosis, transcription factors and the microRNA (miRNA) that are involved in cervical carcinogenesis, enabling a deeper comprehension of cervical cancer linked to HPV. METHODS: We used 10 publicly accessible Gene Expression Omnibus (GEO) datasets to examine the patterns of gene expression in cervical cancer. Differentially expressed genes (DEGs), which showed a clear distinction between cervical cancer and healthy tissue samples, were analyzed using the GEO2R tool. Additional bioinformatic techniques were used to carry out pathway analysis and functional enrichment, as well as to analyze the connection between altered gene expression and HPV infection. RESULTS: In total, 48 DEGs were identified to be differentially expressed in cervical cancer tissues in comparison to healthy tissues. Among DEGs, CCND1, CCNA2 and SPP1 were the key dysregulated genes involved in HPV-associated cervical cancer. The five common miRNAs that were identified against these genes are miR-7-5p, miR-16-5p, miR-124-3p, miR-10b-5p and miR-27a-3p. The hub-DEGs targeted by miRNA hsa-miR-27a-3p are controlled by the common transcription factor SP1. CONCLUSIONS: The present study has identified DEGs involved in HPV-associated cervical cancer progression and the various molecular pathways and transcription factors regulating them. These findings have led to a better understanding of cervical cancer resulting in the development and identification of possible therapeutic and intervention targets, respectively.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder.

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

Mass Spectrometry-Based Proteomic Analysis of Potential Host Proteins Interacting with N in PRRSV-Infected PAMs.

One of the most significant diseases in the swine business, porcine reproductive and respiratory syndrome virus (PRRSV) causes respiratory problems in piglets and reproductive failure in sows. The PRRSV nucleocapsid (N) protein is essential for the virus' assembly, replication, and immune evasion. Stages in the viral replication cycle can be impacted by interactions between the PRRSV nucleocapsid protein and the host protein components. Therefore, it is of great significance to explore the interaction between the PRRSV nucleocapsid protein and the host. Nevertheless, no information has been published on the network of interactions between the nucleocapsid protein and the host proteins in primary porcine alveolar macrophages (PAMs). In this study, 349 host proteins interacting with nucleocapsid protein were screened in the PRRSV-infected PAMs through a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics approach. Bioinformatics analysis, which included gene ontology annotation, Kyoto Encyclopedia of Genes and Genomes database enrichment, and a protein-protein interaction (PPI) network, revealed that the host proteins interacting with PRRSV-N may be involved in protein binding, DNA transcription, metabolism, and innate immune responses. This study confirmed the interaction between the nucleocapsid protein and the natural immune-related proteins. Ultimately, our findings suggest that the nucleocapsid protein plays a pivotal role in facilitating immune evasion during a PRRSV infection. This study contributes to enhancing our understanding of the role played by the nucleocapsid protein in viral pathogenesis and virus-host interaction, thereby offering novel insights for the prevention and control of PRRS as well as the development of vaccines.

Transcriptomic Characterization of Key Factors and Signaling Pathways for the Regeneration of Partially Hepatectomized Liver in Zebrafish.

Liver regeneration induced by partial hepatectomy (PHx) has attracted intensive research interests due to the great significance for liver resection and transplantation. The zebrafish (Danio rerio) is an excellent model to study liver regeneration. In the fish subjected to PHx (the tip of the ventral lobe was resected), the lost liver mass could be fully regenerated in seven days. However, the regulatory mechanisms underlying the liver regeneration remain largely unknown. In this study, gene expression profiles during the regeneration of PHx-treated liver were explored by RNA sequencing (RNA-seq). The genes responsive to the injury of PHx treatment were identified and classified into different clusters based on the expression profiles. Representative gene ontology (GO) enrichments for the early responsive genes included hormone activity, ribosome biogenesis and rRNA processing, etc., while the late responsive genes were enriched in biological processes such as glutathione metabolic process, antioxidant activity and cellular detoxification. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments were also identified for the differentially expressed genes (DEGs) between the time-series samples and the sham controls. The proteasome was overrepresented by the up-regulated genes at all of the sampling time points. Inhibiting proteasome activity by the application of MG132 to the fish enhanced the expression of Pcna (proliferating cell nuclear antigen), an indicator of hepatocyte proliferation after PHx. Our data provide novel insights into the molecular mechanisms underlying the regeneration of PHx-treated liver.

Integrative Analysis of Hepatopancreas Transcriptome and Proteome in Female Eriocheir sinensis under Thermal Stress.

The Chinese mitten crab (Eriocheir sinensis), an economically important crustacean that is endemic to China, has recently experienced high-temperature stress. The high thermal tolerance of E. sinensis points to its promise in being highly productive in an aquacultural context. However, the mechanisms underlying its high thermal tolerance remain unknown. In this study, female E. sinensis that were heat exposed for 24 h at 38.5 °C and 33 °C were identified as high-temperature-stressed (HS) and normal-temperature-stressed (NS) groups, respectively. The hepatopancreas of E. sinensis from the HS and NS groups were used for transcriptome and proteomic analyses. A total of 2350 upregulated and 1081 downregulated differentially expressed genes (DEGs) were identified between the HS and NS groups. In addition, 126 differentially expressed proteins (DEPs) were upregulated and 35 were downregulated in the two groups. An integrated analysis showed that 2641 identified genes were correlated with their corresponding proteins, including 25 genes that were significantly differentially expressed between the two omics levels. Ten Gene Ontology terms were enriched in the DEGs and DEPs. A functional analysis revealed three common pathways that were significantly enriched in both DEGs and DEPs: fluid shear stress and atherosclerosis, leukocyte transendothelial migration, and thyroid hormone synthesis. Further analysis of the common pathways showed that MGST1, Act5C, HSP90AB1, and mys were overlapping genes at the transcriptome and proteome levels. These results demonstrate the differences between the HS and NS groups at the two omics levels and will be helpful in clarifying the mechanisms underlying the thermal tolerance of E. sinensis.

[Functions and mechanisms of testicular descent in Apodemus agrarius based on transcriptomics and metabolomics].

This study aims to explore the functions and mechanisms of testicular descent in Apodemus agrarius, and analyze the changes in genes and metabolite levels in this process. Illumina NovaSeq and liquid chromatography-mass spectrometry were used for the transcriptomic analysis and metabolomic analysis, respectively, of the normal and descending testis of A. agrarius. Gene ontology (GO) enrichment of the transcriptomic results revealed 240 differentially expressed genes (DEGs), such as Spesp1, Izumo1, Hyal5, and Fabp9. Kyoto encyclopedia of genes and genomes (KEGG) enrichment showed 52 DEGs, including Pcyt1, Pla2g4e, Gpd1l, and Lypla3. The qRT-PCR results were consistent with the transcriptomic results in terms of the expression patterns of six randomly selected genes in the normal and descending testis. The metabolomic results revealed 28 differential metabolites associated with the testicular function, including 3-dehydroquinic acid, α-linolenic acid, dihydroxyacetone phosphate, and fructose 1,6-bisphosphate. The conjoint analysis showcased that glycerophospholipid metabolism, α-linolenic acid metabolism, and arachidonic acid metabolism may be the key metabolic pathways regulating testicular descent in A. agrarius. This study will help to understand the mechanism of testicular descent and lay a theoretical foundation for exploring the mechanisms of the population changes of A. agrarius and developing laboratory animal resources.

[Potential action mechanism of Yishen Tongluo Prescription on male infertility: An analysis based on network pharmacology].

OBJECTIVE: To analyze the main active components and potential molecular mechanism of Yishen Tongluo Prescription (YTP) in the treatment of male infertility based on network pharmacological technology. METHODS: We searched and sorted the main active components of YTP and their individual potential targets in the databases of Systematic Pharmacology of Traditional Chinese Medicine (TCM) and Bioinformatics Analysis Tool of the Molecular Mechanism of TCM, and screened the targets related to male infertility diseases in the databases of Genecards, DisGeNET and OMIM. We made a Venn diagram by intersecting the predicted targets of YTP and those of male infertility diseases, constructed visualized networks for the association of the intersection targets and protein-protein interaction (PPI) using the Cytoscape software and STRING platform respectively, and conducted gene ontology (GO) and KEGG enrichment analyses using the DAVID database and R language "Cluster Profiler" software package respectively. RESULTS: A total of 99 active components, 250 targets of YTP, 4 397 targets of male infertility and 127 common targets were identified. GO analysis revealed that the biological processes of the common targets mainly included transcriptional regulation of RNA polymerase promoter Ⅱ, regulation of gene expressions, regulation of apoptosis, responses to estrogen, and cell responses to hypoxia. KEGG analysis showed significant enrichment of the common targets in the estrogen signaling pathway, cell apoptosis pathway, AGE-RAGE signaling pathway in diabetic complications, and TNF signaling pathway. CONCLUSION: Through network pharmacology, we identified the main active components of YTP and its multi-target and multi-pathway mechanism in the treatment of male infertility, which has paved the ground for animal and cell experiments in verifying the action mechanism of YTP on male infertility.

A randomized translational study on protein- and glucose metabolism in skeletal muscles evaluated by gene-ontology, following preoperative oral carbohydrate loading compared to overnight peripheral parenteral nutrition (PPN) before major cancer surgery.

BACKGROUND: Effects of preoperative drinks on muscle metabolism are unclear despite general recommendations. The aim of the present study was therefore to compare metabolic effects of a preoperative oral nutrition drink, recommended by protocols for enhanced recovery after surgery (ERAS), compared to overnight preoperative peripheral total parenteral nutrition (PPN) on skeletal muscle metabolism in patients aimed at major gastrointestinal cancer surgery. METHODS: Patients were randomized, based on diagnosis and clinical characteristics, to receive either a commercial carbohydrate-rich nutrition drink (Drink); or overnight (12 h) peripheral parenteral nutrition (PPN) as study regimens; compared to isotone Ringer-acetate as Control regimen. Arterial blood- and abdominal muscle tissue specimens were collected at start of surgery. Blood chemistry included substrate- and hormone concentrations. Muscle mRNA transcript analyses were performed by microarray and evaluated for changes in gene activities by Gene Ontology algorithms. RESULTS: Patient groups were comparable in all measured preoperative assessments. The Nutrition Drink had significant metabolic alterations on muscle glucose metabolism (p < 0.05), without any significant effects on amino acid- and protein metabolism. PPN showed similar significant effects on glucose metabolism as Drinks (p < 0.05), but indicated also major positive effects on amino acid- (p < 0.001) and protein anabolism (p < 0.05), particularly by inhibition of muscle protein degradation, related to both ubiquitination of proteins and autophagy/lysosome pathways (p < 0.05). CONCLUSION: Conventional overnight preoperative PPN seems effective to induce and support improved muscle protein metabolism in patients aimed at major cancer surgery while preoperative oral carbohydrate loading, according to ERAS-protocols, was ineffective to improve skeletal muscle catabolism and should therefore not be recommended before major cancer surgery. Trial registration Clinical trials.gov: NCT05080816, Registered June 10th 2021- Retrospectively registered. https://clinicaltrials.gov/study/NCT05080816.

Exploring the role of ferroptosis-related genes as biomarkers in acute kidney injury.

INTRODUCTION: Acute kidney injury (AKI) is a severe condition with high morbidity and mortality. Innovative biomarkers and treatments are essential for improving patient outcomes. This study aims to investigate the role of ferroptosis-related genes (FRGs) in AKI for identifying potential biomarkers and therapeutic targets. METHODS: We analyzed mRNA expression profiles from the Gene Expression Omnibus (GEO: GSE139061) dataset, comparing 36 AKI samples with 9 normal samples. Differentially expressed genes (DEGs) were identified using the R software package limma. Functional enrichment analyses were conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key biomarkers were validated through area under the curve (AUC) values, and immune cell infiltration was analyzed using CIBERSORT. RESULTS: We identified 78 differentially expressed FRGs, with 27 up-regulated and 51 down-regulated genes. Key signaling pathways included MAPK, ferroptosis, and p53. Five genes-NR4A1, GLRX5, USP35, AEBP2, and MDM4-were identified as potential biomarkers, each demonstrating AUC values greater than 0.800. Specifically, MDM4 showed significant potential by promoting the phosphorylation of p53 at Ser46, enhancing mitochondrial apoptotic activity. Immune analysis revealed a significant elevation of M0 macrophages in AKI samples compared to normal samples (P < 0.01). CONCLUSION: Our findings highlight the critical role of ferroptosis-related genes in AKI, identifying NR4A1, GLRX5, USP35, AEBP2, and MDM4 as key biomarkers with high diagnostic potential. These results provide novel insights into the molecular mechanisms of AKI.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Peripheral whole blood microRNA expression in relation to vascular function: a population-based study.

BACKGROUND: As key regulators of gene expression, microRNAs affect many cardiovascular mechanisms and have been associated with several cardiovascular diseases. In this study, we aimed to investigate the relation of whole blood microRNAs with several quantitative measurements of vascular function, and explore their biological role through an integrative microRNA-gene expression analysis. METHODS: Peripheral whole blood microRNA expression was assessed through RNA-Seq in 2606 participants (45.8% men, mean age: 53.93, age range: 30 to 95 years) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. Weighted gene co-expression network analysis was used to cluster microRNAs with highly correlated expression levels into 14 modules. Through linear regression models, we investigated the association between each module's expression and quantitative markers of vascular health, including pulse wave velocity, total arterial compliance index, cardiac index, stroke index, systemic vascular resistance index, reactive skin hyperemia and white matter hyperintensity burden. For each module associated with at least one trait, one or more hub-microRNAs driving the association were defined. Hub-microRNAs were further characterized through mapping to putative target genes followed by gene ontology pathway analysis. RESULTS: Four modules, represented by hub-microRNAs miR-320 family, miR-378 family, miR-3605-3p, miR-6747-3p, miR-6786-3p, and miR-330-5p, were associated with total arterial compliance index. Importantly, the miR-320 family module was also associated with white matter hyperintensity burden, an effect partially mediated through arterial compliance. Furthermore, hub-microRNA miR-192-5p was related to cardiac index. Functional analysis corroborated the relevance of the identified microRNAs for vascular function by revealing, among others, enrichment for pathways involved in blood vessel morphogenesis and development, angiogenesis, telomere organization and maintenance, and insulin secretion. CONCLUSIONS: We identified several microRNAs robustly associated with cardiovascular function, especially arterial compliance and cardiac output. Moreover, our results highlight miR-320 as a regulator of cerebrovascular damage, partly through modulation of vascular function. As many of these microRNAs were involved in biological processes related to vasculature development and aging, our results contribute to the understanding of vascular physiology and provide putative targets for cardiovascular disease prevention.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Analyzing the molecular mechanism of Scutellaria Radix in the treatment of sepsis using RNA sequencing.

BACKGROUND: Sepsis is a life-threatening organ dysfunction, which seriously threatens human health. The clinical and experimental results have confirmed that Traditional Chinese medicine (TCM), such as Scutellariae Radix, has anti-inflammatory effects. This provides a new idea for the treatment of sepsis. This study systematically analyzed the mechanism of Scutellariae Radix treatment in sepsis based on network pharmacology, RNA sequencing and molecular docking. METHODS: Gene expression analysis was performed using Bulk RNA sequencing on sepsis patients and healthy volunteers. After quality control of the results, the differentially expressed genes (DEGs) were analyzed. The active ingredients and targets of Scutellariae Radix were identified using The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Gene Ontology (GO) and Protein-Protein Interaction (PPI) analysis were performed for disease-drug intersection targets. With the help of GEO database, Survival analysis and Meta-analysis was performed on the cross-targets to evaluate the prognostic value and screen the core targets. Subsequently, single-cell RNA sequencing was used to determine where the core targets are located within the cell. Finally, in this study, molecular docking experiments were performed to further clarify the interrelationship between the active components of Scutellariae Radix and the corresponding targets. RESULTS: There were 72 active ingredients of Scutellariae Radix, and 50 common targets of drug and disease. GO and PPI analysis showed that the intersection targets were mainly involved in response to chemical stress, response to oxygen levels, response to drug, regulation of immune system process. Survival analysis showed that PRKCD, EGLN1 and CFLAR were positively correlated with sepsis prognosis. Meta-analysis found that the three genes were highly expressed in sepsis survivor, while lowly in non-survivor. PRKCD was mostly found in Macrophages, while EGLN1 and CFLAR were widely expressed in immune cells. The active ingredient Apigenin regulates CFLAR expression, Baicalein regulates EGLN1 expression, and Wogonin regulates PRKCD expression. Molecular docking studies confrmed that the three active components of astragalus have good binding activities with their corresponding targets. CONCLUSIONS: Apigenin, Baicalein and Wogonin, important active components of Scutellaria Radix, produce anti-sepsis effects by regulating the expression of their targets CFLAR, EGLN1 and PRKCD.

Transcriptome sequencing of garlic reveals key genes related to the heat stress response.

With global warming, heat stress has become an important factor that seriously affects crop yield and quality. Therefore, understanding plant responses to heat stress is important for agricultural practice, but the molecular mechanism of high-temperature tolerance in garlic remains unclear. In this study, 'Xusuan No. 6' was used as the experimental material. After heat stress for 0 (CK), 2 and 24 h, transcriptome sequencing was used to screen metabolic pathways and differentially expressed genes (DEGs) closely related to heat stress and was further verified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 86,110 unigenes obtained from the raw transcriptome sequencing data were spliced. After 2 h of heat treatment, the expression levels of 8898 genes increased, and 3829 genes were decreased in leaves. After 24 h, the expression levels of 7167 genes were upregulated, and 3176 genes were downregulated. Gene Ontology enrichment analysis showed that DEGs were mainly enriched in seven categories: cellular processes, metabolic processes, binging, catalytic activity, cellular anatomical entity and protein-containing complex response to stimulus. Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that DEGs are involved in protein processing in the endoplasmic reticulum, plant hormone signal transduction, phenylpropanoid biosynthesis, and photosynthetic antenna proteins. Six genes were selected and further verified by qRT-PCR. In this study, the full-length transcriptome of garlic was constructed, and the regulatory genes related to the heat resistance of garlic were studied. Taken together, these findings can provide a theoretical basis for the cloning of heat resistance genes in garlic and for the analysis of heat resistance mechanisms.

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease.

Premature ovarian failure (POF), which is often comorbid with dry eye disease (DED) is a key issue affecting female health. Here, we explored the mechanism underlying comorbid POF and DED to further elucidate disease mechanisms and improve treatment. Datasets related to POF (GSE39501) and DED (GSE44101) were identified from the Gene Expression Omnibus (GEO) database and subjected to weighted gene coexpression network (WGCNA) and differentially expressed genes (DEGs) analyses, respectively, with the intersection used to obtain 158 genes comorbid in POF and DED. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of comorbid genes revealed that identified genes were primarily related to DNA replication and Cell cycle, respectively. Protein-Protein interaction (PPI) network analysis of comorbid genes obtained the 15 hub genes: CDC20, BIRC5, PLK1, TOP2A, MCM5, MCM6, MCM7, MCM2, CENPA, FOXM1, GINS1, TIPIN, MAD2L1, and CDCA3. To validate the analysis results, additional POF- and DED-related datasets (GSE48873 and GSE171043, respectively) were selected. miRNAs-lncRNAs-genes network and machine learning methods were used to further analysis comorbid genes. The DGIdb database identified valdecoxib, amorfrutin A, and kaempferitrin as potential drugs. Herein, the comorbid genes of POF and DED were identified from a bioinformatics perspective, providing a new strategy to explore the comorbidity mechanism, opening up a new direction for the diagnosis and treatment of comorbid POF and DED.