Gene Networks Research Articles

Estrogen-related receptor alpha promotes thyroid tumor cell survival via a tumor subtype-specific regulation of target gene networks.

Mortalin (encoded by HSPA9) is a mitochondrial chaperone often overexpressed in cancer through as-yet-unknown mechanisms. By searching different RNA-sequencing datasets, we found that ESRRA is a transcription factor highly correlated with HSPA9 in thyroid cancer, especially in follicular, but not C cell-originated, tumors. Consistent with this correlation, ESRRA depletion decreased mortalin expression only in follicular thyroid tumor cells. Further, ESRRA expression and activity were relatively high in thyroid tumors with oncocytic characteristics, wherein ESRRA and mortalin exhibited relatively high functional overlap. Mechanistically, ESRRA directly regulated HSPA9 transcription through a novel ESRRA-responsive element located upstream of the HSPA9 promoter. Physiologically, ESRRA depletion suppressed thyroid tumor cell survival via caspase-dependent apoptosis, which ectopic mortalin expression substantially abrogated. ESRRA depletion also effectively suppressed tumor growth and mortalin expression in the xenografts of oncocytic or ESRRA-overexpressing human thyroid tumor cells in mice. Notably, our Bioinformatics analyses of patient data revealed two ESRRA target gene clusters that contrast oncocytic-like and anaplastic features of follicular thyroid tumors. These findings suggest that ESRRA is a tumor-specific regulator of mortalin expression, the ESRRA-mortalin axis has higher significance in tumors with oncocytic characteristics, and ESRRA target gene networks can refine molecular classification of thyroid cancer.

A broken network of susceptibility genes in the monocytes of Crohn's disease patients.

Genome-wide association studies have identified over 200 genetic loci associated with inflammatory bowel disease; however, the mechanism of such a large amount of susceptibility genes remains uncertain. In this study, we integrated bioinformatics analysis and two independent single-cell transcriptome datasets to investigate the expression network of 232 susceptibility genes in Crohn's disease (CD) patients and healthy controls. The study revealed that most of the susceptibility genes are specifically and strictly expressed in the monocytes of the human intestinal tract. The susceptibility genes established a network within the monocytes of health control. The robustness of a gene network may prevent disease onset that is influenced by the genetic and environmental alteration in the expression of susceptibility genes. In contrast, we showed a sparse network in pediatric/adult CD patients, suggesting the broken network contributed to the CD etiology. The network status of susceptibility genes at the single-cell level of monocytes provided novel insight into the etiology.

Revealing the Molecular Mechanism of Sageretia theezans in the Treatment of Hemorrhoids based on Network Pharmacology.

Sageretia theezans is one of the classic medicines in ancient times, which is commonly used to treat scabies, lacquer sores, acute and chronic pharyngitis, Tonsillitis, Cholecystitis, secondary infection of hemorrhoids, and other symptoms. However, the potential molecular mechanism of Sageretia theezans is still unclear. In this study, we explored the active compounds of Sageretia theezans in the treatment of hemorrhoids (HD), predicted the potential targets of drugs, and verified their functions through network pharmacology and in vivo and in vitro experiments. First, we identified the active compounds and key targets of Sageretia theezans in treating HD through network pharmacology. The key signaling pathways related to the role of Sageretia theezans were analyzed. HUVEC Human umbilical vein endothelial cells were used to study the function of Sageretia theezans and its target in vitro. In addition, we also used the SD rat hemorrhoid model to explore the efficacy of Sageretia theezans in HD in vivo. A total of 159 drug targets were obtained from the TCMSP, ETCM, and PubChem databases. Constructing a drug component target network; differential analysis using sequencing data identified 1046 differentially expressed genes. Intersecting drug targets and differentially expressed genes obtained four intersection targets (GOT1, SLC25A10, SUCLG1, CLEC4E). Perform single gene GSEA functional enrichment analysis on intersection targets, select KEGG and GO of the top 10 for display, and merge the results. In order to investigate the interaction between intersecting genes and differentially expressed genes, we conducted a PPI protein interaction analysis on 1046 differentially expressed genes. Finally, a network of Chinese medicine active molecule intersection genes was proposed, and the genes and their corresponding active molecules (Successful acid, Taraxerone, Taraxerol) were Macromolecular docking, respectively. The results showed that these four genes could be successfully docked with the responsive active molecules and had high binding affinity. In vivo, the low-dose treatment group of Sageretia theezans, the medium-dose treatment group of Sageretia theezans, and the high-dose treatment group of Bromelia can inhibit the proliferation of HUVECs cells. In vitro, the middle dose of Sageretia theezans has the best therapeutic effect on hemorrhoids, and the treatment of Sageretia theezans on hemorrhoids is correlated with the expression of GOT1, SLC25A10, SUCLG1, and CLEC4E. To sum up, Sageretia theezans can alleviate the symptoms of hemorrhoids and is related to the expression of GOT1, SLC25A10, SUCLG1, and CLEC4E.

The cacao gene atlas: a transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L

BackgroundTheobroma cacao, the cocoa tree, is a tropical crop grown for its highly valuable cocoa solids and fat which are the basis of a 200-billion-dollar annual chocolate industry. However, the long generation time and difficulties associated with breeding a tropical tree crop have limited the progress of breeders to develop high-yielding disease-resistant varieties. Development of marker-assisted breeding methods for cacao requires discovery of genomic regions and specific alleles of genes encoding important traits of interest. To accelerate gene discovery, we developed a gene atlas composed of a large dataset of replicated transcriptomes with the long-term goal of progressing breeding towards developing high-yielding elite varieties of cacao.ResultsWe describe the creation of the Cacao Transcriptome Atlas, its global characterization and define sets of genes co-regulated in highly organ- and temporally-specific manners. RNAs were extracted and transcriptomes sequenced from 123 different tissues and stages of development representing major organs and developmental stages of the cacao lifecycle. In addition, several experimental treatments and time courses were performed to measure gene expression in tissues responding to biotic and abiotic stressors. Samples were collected in replicates (3–5) to enable statistical analysis of gene expression levels for a total of 390 transcriptomes. To promote wide use of these data, all raw sequencing data, expression read mapping matrices, scripts, and other information used to create the resource are freely available online. We verified our atlas by analyzing the expression of genes with known functions and expression patterns in Arabidopsis (ACT7, LEA19, AGL16, TIP13, LHY, MYB2) and found their expression profiles to be generally similar between both species. We also successfully identified tissue-specific genes at two thresholds in many tissue types represented and a set of genes highly conserved across all tissues.ConclusionThe Cacao Gene Atlas consists of a gene expression browser with graphical user interface and open access to raw sequencing data files as well as the unnormalized and CPM normalized read count data mapped to several cacao genomes. The gene atlas is a publicly available resource to allow rapid mining of cacao gene expression profiles. We hope this resource will be used to help accelerate the discovery of important genes for key cacao traits such as disease resistance and contribute to the breeding of elite varieties to help farmers increase yields.

The development of extremely large male genitalia under spatial limitation.

Extensive research in evolutionary biology has focused on the exaggeration of sexual traits; however, the developmental basis of exaggerated sexual traits has only been determined in a few cases. The evolution of exaggerated sexual traits may involve the relaxation of constraints or developmental processes mitigating constraints. Ground beetles in the subgenus Ohomopterus (genus Carabus) have species-specific genitalia that show coevolutionary divergence between the sexes. Here, we examined the morphogenesis of the remarkably enlarged male and female genitalia of Carabus uenoi by X-ray microcomputed tomography. The morphogenetic processes generating the male and female genitalia at the pupal stage were qualitatively similar to those in closely related species with standard genital sizes. Higher growth rates contributed to the exaggeration of both the male and female genital parts of C. uenoi, possibly related to a gene network commonly upregulated in both sexes. Additionally, the length of the copulatory piece (CP), the enlarged male genital part stored in the aedeagus (AD), reached close to that of the AD at the later developmental stages and thereafter decelerated to grow in parallel with the AD, suggesting a structural constraint on the CP by the outer AD. Then, unlike related species, the lengths of the CP and AD increased at eclosion, suggesting a mechanism leading to further elongation of the male genitalia. These observations suggest that a developmental process allows continuous growth of the male genitalia even under the spatial limitation. These results revealed the spatio-temporal dynamics of the development of exaggerated genital structures under structural constraints.

A modeling framework for detecting and leveraging node-level information in Bayesian network inference.

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

Screening of potentially active compounds against rheumatoid arthritis in the Juan-Bi decoction using systems pharmacology and animal experiments.

Background: The Juan-Bi decoction (JBD) is a classic traditional Chinese medicines (TCMs) prescription for the treatment of rheumatoid arthritis (RA). However, the active compounds of the JBD in RA treatment remain unclear. Aim: The aim of this study is to screen effective compounds in the JBD for RA treatment using systems pharmacology and experimental approaches. Method: Botanical drugs and compounds in the JBD were acquired from multiple public TCM databases. All compounds were initially screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical properties, and then a target prediction was performed. RA pathological genes were acquired from the DisGeNet database. Potential active compounds were screened by constructing a compound-target-pathogenic gene (C-T-P) network and calculating the cumulative interaction intensity of the compounds on pathogenic genes. The effectiveness of the compounds was verified using lipopolysaccharide (LPS)-induced RAW.264.7 cells and collagen-induced arthritis (CIA) mouse models. Results: We screened 15 potentially active compounds in the JBD for RA treatment. These compounds primarily act on multiple metabolic pathways, immune pathways, and signaling transduction pathways. Furthermore, in vivo and in vitro experiments showed that bornyl acetate (BAC) alleviated joint damage, and inflammatory cells infiltrated and facilitated a smooth cartilage surface via the suppression of the steroid hormone biosynthesis. Conclusion: We screened potential compounds in the JBD for the treatment of RA using systems pharmacology approaches. In particular, BAC had an anti-rheumatic effect, and future studies are required to elucidate the underlying mechanisms.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 and venous thromboembolism

BackgroundA growing body of research has shown that patients with coronavirus disease 2019 (COVID-19) have significantly higher rates of venous thromboembolism (VTE) than healthy. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication. MethodsThe gene expression profiles of COVID-19 and VTE were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for COVID-19 and VTE, functional annotation, a protein–protein interactions (PPI) network, module construction, and hub gene identification were performed. Finally, we constructed a transcription factor (TF)-gene regulatory network and a TF-miRNA regulatory network for hub genes. ResultsA total of 42 common DEGs were selected for subsequent analyses. Functional analyses showed that biological function and signaling pathways collectively participated in the development and progression of VTE and COVID-19. Finally, 8 significant hub genes were identified using the cytoHubba plugin, including RSL24D1, RPS17, RPS27, HINT1, COX7C, RPL35, RPL34, and NDUFA4, which had preferable values as diagnostic markers for COVID-19 and VTE. ConclusionsOur study revealed the common pathogenesis of COVID-19 and VTE. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.

A comprehensive evaluation of large language models in mining gene relations and pathway knowledge.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).

Sphingosine kinase 2 regulates protein ubiquitination networks in neurons

Two sphingosine kinase isoforms, sphingosine kinase 1 (SPHK1) and sphingosine kinase 2 (SPHK2), synthesize the lipid sphingosine-1-phosphate (S1P) by phosphorylating sphingosine. SPHK1 is a cytoplasmic kinase, and SPHK2 is localized to the nucleus and other organelles. In the cytoplasm, the SPHK1/S1P pathway modulates autophagy and protein ubiquitination, among other processes. In the nucleus, the SPHK2/S1P pathway regulates transcription. Here, we hypothesized that the SPHK2/S1P pathway governs protein ubiquitination in neurons. We found that ectopic expression of SPHK2 increases ubiquitinated substrate levels in cultured neurons and pharmacologically inhibiting SPHK2 decreases protein ubiquitination. With mass spectrometry, we discovered that inhibiting SPHK2 affects lipid and synaptic protein networks as well as a ubiquitin-dependent protein network. Several ubiquitin-conjugating and hydrolyzing proteins, such as the E3 ubiquitin-protein ligases HUWE1 and TRIP12, the E2 ubiquitin-conjugating enzyme UBE2Z, and the ubiquitin-specific proteases USP15 and USP30, were downregulated by SPHK2 inhibition. Using RNA sequencing, we found that inhibiting SPHK2 altered lipid and neuron-specific gene networks, among others. Genes that encode the corresponding proteins from the ubiquitin-dependent protein network that we discovered with mass spectrometry were not affected by inhibiting SPHK2, indicating that the SPHK2/S1P pathway regulates ubiquitination at the protein level. We also show that both SPHK2 and HUWE1 were upregulated in the striatum of a mouse model of Huntington's disease, the BACHD mice, indicating that our findings are relevant to neurodegenerative diseases. Our results identify SPHK2/S1P as a novel regulator of protein ubiquitination networks in neurons and provide a new target for developing therapies for neurodegenerative diseases.

Identification of differentially expressed genes to predict the risk of heart failure in older patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a common heart disease. Old people with HCM are at high risk of heart failure (HF). This study aimed to identify differentially expressed genes (DEGs) to evaluate the risk of HF in older patients with HCM. GSE89714 and GSE116250 were downloaded from Gene Expression Omnibus (GEO) database, and DEGs were identified by using limma R package with P < 0.05 and logFC> 1 as cut off. Protein-protein interaction (PPI) network, Genome Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the identified DEGs. NetworkAnalyst online tool was applied for Gene Set Enrichment Analysis (GSEA) analysis. We identified 124 overlap DEGs from the 2 datasets. PPI network showed that COL1A1, COL3A1, COL1A2, BGN, COL5A1, LUM, TGFB2, FMOD, ASPN, and COL14A1 were the top ten genes related to HCM and HF compared with control. Functional and pathway analyses showed that the overlap genes were mainly related to ECM-receptor interaction, ECM organization, Focal adhesion, PI3K-Akt signaling, TGF-beta signaling, and Platelet activation signaling and aggregation. Among the overlap genes, COL5A1 and LUM were significantly upregulated, while TGFB2, FMOD, ASPN, and COL14A1 were significantly downregulated in HF dataset compared with HCM dataset. Bioinformatics-based analysis revealed potential genes associated with HCM and HF, which could be utilized to evaluate the risk of HF in older patients with HCM.

Investigation on thyroid gene network of aged macaques subjected to bone marrow mesenchymal stem cells treatment: Revealed from genic transcriptome analysis

Investigation on thyroid gene network of aged macaques subjected to bone marrow mesenchymal stem cells treatment: Revealed from genic transcriptome analysis

Transcriptome analysis of cynomolgus macaques throughout their lifespan reveals age-related immune patterns

Despite the different perspectives by diverse research sectors spanning several decades, aging research remains uncharted territory for human beings. Therefore, we investigated the transcriptomic characteristics of eight male healthy cynomolgus macaques, and the annual sampling was designed with two individuals in four age groups. As a laboratory animal, the macaques were meticulously shielded from all environmental factors except aging. The results showed recent findings of certain immune response and the age-associated network of primate immunity. Three important aging patterns were identified and each gene clusters represented a different immune response. The increased expression pattern was predominantly associated with innate immune cells, such as Neutrophils and NK cells, causing chronic inflammation with aging whereas the other two decreased patterns were associated with adaptive immunity, especially “B cell activation” affecting antibody diversity of aging. Furthermore, the hub gene network of the patterns reflected transcriptomic age and correlated with human illness status, aiding in future human disease prediction. Our macaque transcriptome profiling results offer systematic insights into the age-related immunological features of primates.

FairGAT: Fairness-Aware Graph Attention Networks

Graphs can facilitate modeling various complex systems such as gene networks and power grids as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network (GNN)–based solutions, among which graph attention networks (GATs) have become one of the most widely utilized neural network structures for graph-based tasks. Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated. Motivated by this, the present study first carries out a theoretical analysis in order to demonstrate the sources of algorithmic bias in GAT-based learning for node classification. Then, a novel algorithm, FairGAT, which leverages a fairness-aware attention design, is developed based on the theoretical findings. Experimental results on real-world networks demonstrate that FairGAT improves group fairness measures while also providing comparable utility to the fairness-aware baselines for node classification and link prediction.

How does jasmonic acid improve drought tolerance? Mechanisms and future prospects

Drought stress poses a significant challenge to agriculture sustainability across the globe. Drought stress negatively affects the plant growth and productivity and the intensity of this serious abiotic stress is continuously increasing which is a serious threat across the globe. Different measures are being used to mitigate the adverse impacts of drought stress. Among these measures, the application of exogenous osmolytes and growth hormones is considered an important way to mitigate the adverse impacts of drought. Recently, jasmonic acid (JA) has emerged as an excellent growth hormone to improve drought tolerance owing to its involvement in different plant physiological and biochemical processes. Jasmonic acid improves membrane stability plant water relations, nutrient uptake, osmolyte accumulation, and antioxidant activities that can counter the toxic effects of drought. It also contributes to signaling pathways, i.e., genes network, stress-responsive proteins, signaling intermediates, and enzymes that protect the plants from the toxic effects of drought. Further, JA also protects and maintains the integrity of plant cells by up-regulating the antioxidant defense system and increasing osmolyte accumulation. In this review, we have documented the protective role of JA under drought stress. The various mechanisms of JA in inducing drought tolerance are discussed and different research gaps are also identified. This review will help the readers to learn more about the role of JA to mitigate the toxic effects of drought and it will provide new knowledge to develop the drought tolerance in plants.

Prioritization of drug targets for thyroid cancer: a multi-omics Mendelian randomization study.

Recurrence or tumor metastasis and drug resistance remain the major challenge in the treatment of thyroid cancer. It is needed to identify novel drug targets for thyroid cancer. Summary data-based Mendelian randomization (SMR) and colocalization analysis were performed to evaluate the associations between gene methylation, expression, protein levels with thyroid cancer. We additionally performed protein-protein interaction (PPI) network, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses to further explore the potential roles of identified genes in thyroid cancer. SDCCAG8 and VCAM1 genes were associated with risk of thyroid cancer with tier 1 evidence, while TCN2 gene was with tier 3 evidence. SDCCAG8 gene was associated with risk of papillary thyroid cancer with tier 1 evidence. At the level of circulating proteins, genetically predicted higher levels of SDCCAG8 (OR = 0.46, 95% CI 0.34-0.64) and VCAM1 (OR = 0.21, 95% CI 0.10-0.45) were inversely associated with thyroid cancer risk; higher level of TCN2 was associated with an increased risk of thyroid cancer (OR = 1.30, 95% CI 1.15-1.47); and the higher level of SDCCAG8 (OR = 0.40, 95% CI 0.28-0.58) was associated with a decreased risk of papillary thyroid cancer. The bioinformatics analysis showed that SDCCAG8, VCAM1 and TCN2 might play roles in immune-related pathways. SDCCAG8, VCAM1 and TCN2 genes were associated with thyroid cancer risk with evidence at multi-omics levels. There were potential roles of SDCCAG8, VCAM1 and TCN2 in immune-related pathways. Our findings might improve the understanding of the pathogenesis of thyroid cancer and discovery of novel potential drug targets for this disease.

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction in graph structures. In the pursuit of predicting gene expression, an endeavor shared by predecessors like the L1000 and Enformer methods, we introduce a novel spatial graph-neural network (GNN) approach. This innovative strategy incorporates graph features, encompassing both regulatory and structural elements. The regulatory elements include pair-wise gene correlation, biological pathways, protein-protein interaction networks, and transcription factor regulation. The spatial structural elements include chromosomal distance, histone modification and Hi-C inferred 3D genomic features. Principal Node Aggregation models, validated independently, emerge as frontrunners, demonstrating superior performance compared to traditional regression and other deep learning models. By embracing the spatial GNN paradigm, our method significantly advances the description of the intricate network of gene interactions, surpassing the performance, predictable scope, and initial requirements set by previous methods.

Functional Validation of the Cytochrome P450 Family PgCYP309 Gene in Panax ginseng.

Ginseng (Panax ginseng C. A. Meyer) is an ancient and valuable Chinese herbal medicine, and ginsenoside, as the main active ingredient of ginseng, has received wide attention because of its various pharmacological active effects. Cytochrome P450 is the largest family of enzymes in plant metabolism and is involved in the biosynthesis of terpenoids, alkaloids, lipids, and other primary and secondary plant metabolites. It is significant to explore more PgCYP450 genes with unknown functions and reveal their roles in ginsenoside synthesis. In this study, based on the five PgCYP450 genes screened in the pre-laboratory, through the correlation analysis with the content of ginsenosides and the analysis of the interactions network of the key enzyme genes for ginsenoside synthesis, we screened out those highly correlated with ginsenosides, PgCYP309, as the target gene from among the five PgCYP450 genes. Methyl jasmonate-induced treatment of ginseng adventitious roots showed that the PgCYP309 gene responded to methyl jasmonate induction and was involved in the synthesis of ginsenosides. The PgCYP309 gene was cloned and the overexpression vector pBI121-PgCYP309 and the interference vector pART27-PgCYP309 were constructed. Transformation of ginseng adventitious roots by the Agrobacterium fermentum-mediated method and successful induction of transgenic ginseng hairy roots were achieved. The transformation rate of ginseng hairy roots with overexpression of the PgCYP309 gene was 22.7%, and the transformation rate of ginseng hairy roots with interference of the PgCYP309 gene was 40%. Analysis of ginseng saponin content and relative gene expression levels in positive ginseng hairy root asexual lines revealed a significant increase in PPD, PPT, and PPT-type monomeric saponins Re and Rg2. The relative expression levels of PgCYP309 and PgCYP716A53v2 genes were also significantly increased. PgCYP309 gene promotes the synthesis of ginsenosides, and it was preliminarily verified that PgCYP309 gene can promote the synthesis of dammarane-type ginsenosides.

Regulatory network analysis based on integrated miRNA-TF reveals key genes in heart failure

The etiology and pathophysiology of heart failure are still unknown. Increasing evidence suggests that abnormal microRNAs (miRNAs) and transcription factors (TFs) expression may be associated with the development of heart failure. Therefore, this study aims to explore key miRNAs, TFs, and related genes in heart failure to gain a greater understanding of the pathogenesis of heart failure. To search and download the dataset of mRNA chips related to heart failure from the GEO database (GSE59867, GSE9128, and GSE134766), we analyzed differential genes and screened the common differentially expressed genes on two chips using R language software. The binary interactions and circuits among miRNAs, TFs, and corresponding genes were determined by Pearson correlation coefficient. A regulatory network of miRNAs, TFs, and target genes was constructed based on bioinformatics. By comparing the sequences of patients with and without heart failure, five downregulated genes with hypermethylated mRNA and three upregulated genes with hypomethylated mRNA were identified. The miRNA-TF gene regulatory network consisted of 26 miRNAs, 22 TFs and six genes. GO and KEGG analysis results revealed that BP terms like cellular response to organic substance, cellular response to cytokine stimulus, and KEGG pathways like osteoclast differentiation, MAPK signaling pathway, and legionellosis were enriched of the DEGs. TMEM87A, PPP2R2A, DUSP1, and miR-92a have great potential as biomarkers for heart failure. The integrated analysis of the mRNA expression spectrum and microRNA-transcription factor-gene revealed the regulatory network of heart failure, which may provide clues to its alternative treatment.