Candidate Hub Research Articles

The transcription factor CaBBX10 promotes chlorophyll and carotenoid pigment accumulation in Capsicum annuum fruit.

Chlorophylls and carotenoids are two pivotal photosynthetic pigments directly influencing the economic value of pepper (Capsicum annuum L.) fruits. However, the coordinated regulatory mechanisms governing the accumulation of both chlorophylls and carotenoids during pepper fruit development remain elusive. In this study, pepper B-box 10 (CaBBX10), a candidate hub transcription factor, was found to play dual roles in the early development of pepper fruit. CaBBX10 virus-induced gene silencing and overexpression experiments demonstrated that the encoded transcription factor promotes both chlorophyll and carotenoid accumulation in pepper fruit. Further comprehensive analyses showed that CaBBX10 directly binds to the promoter of magnesium chelatase subunit D subunit (CaCHLD) and phytoene synthase 1 (CaPSY1), thereby activating their expression in the chlorophyll and carotenoid biosynthesis pathways, respectively. Additionally, the photomorphogenic factor CaCOP1 was found to physically interact with CaBBX10 and lead to its degradation. Therefore, CaBBX10 may serve as a critical link connecting chlorophyll and carotenoid biosynthesis to light signaling. Altogether, our findings reveal a mechanism for the complex transcriptional regulation that simultaneously promotes chlorophyll and carotenoid accumulation in pepper fruit.

Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia

Preeclampsia (PE) is an important research subject in obstetrics. Nevertheless, the underlying mechanisms of PE remain elusive. PE-related expression datasets (GSE96983, GSE96984 and GSE24129) were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, the differentially expressed messenger RNAs (DE-mRNAs), DE-microRNA (DE-miRNAs) and DE-long non-coding RNA (DE-lncRNAs) between PE and control cohorts were identified, and the ceRNA network was constructed. Then candidate hub genes were obtained through five algorithms by the protein-protein intersection (PPI) network of the mRNAs. Further, five hub genes were identified by receiver operating characteristic (ROC) curve and gene expression profiles: DAXX, EFNB1, NCOR2, RBBP4 and SOCS1. The function of 5 hub genes was analyzed and the interaction between drugs and hub genes was predicted. A total of 5 small molecule drugs were predicted, namely benzbromarone, 9,10-phenanthrenequinone, chembl312032, insulin and aldesleukin. AGAP2-AS1 was mainly located in exosome and cytoplasm. Agap2-as1-related regulatory subnetworks were extracted from ceRNA networks which included 41 mRNAs, 2 miRNAs and 1 lncRNA, including the regulated relationship pairs AGAP2-AS1-hsa-miR-497-5p-SRPRB, and AGAP2-AS1-hsa-miR-195-5p-RPL36. In summary, we constructed a competitive endogenous RNA (ceRNA) network to identify five potential biomarkers (DAXX, EFNB1, NCOR2, SOCS1 and RBBP4) of PE. The in-depth analysis of the AGAP2-AS1 regulatory network will help to uncover more important molecules closely related to PE and provide a scientific Reference.

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes—MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5—were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies.

Transcriptome analysis to identify genes related to programmed cell death resulted from manipulating of BnaFAH ortholog by CRISPR/Cas9 in Brassica napus

Fumarylacetoacetate hydrolase (FAH) catalyzes the final step of the tyrosine degradation pathway. In this study, we isolated and characterized two homologous BnaFAH genes in Brassica napus L. variant Westar, and then used CRISPR/Cas9-mediated targeted mutagenesis to generate a series of transgene-free mutant lines either with single or double-null bnafah alleles. Among these mutant lines, the aacc (bnafah) double-null mutant line, rather than the aaCC (bnaa06fah) mutant line, exhibited programmed cell death (PCD) under short days (SD). Histochemical staining and content measurement confirmed that the accumulation of reactive oxygen species (ROS) in bnafah was significantly higher than that in bnaa06fah. To further elucidate the mechanism of PCD, we performed transcriptomic analyses of bnaa06fah and bnafah at different SD stages. A heatmap cluster of differentially expressed genes (DEGs) revealed that PCD may be related to various redox regulatory genes involved in antioxidant activity, ROS-responsive regulation and calcium signaling. Combined with the results of previous studies, our work revealed that the expression levels of BnaC04CAT2, BnaA09/C09SAL1, BnaA08/C08ACO2, BnaA07/C06ERO1, BnaA08ACA1, BnaC04BIK1, BnaA09CRK36 and BnaA03CPK4 were significantly different and that these genes might be candidate hub genes for PCD. Together, our results underscore the ability of different PCD phenotypes to alter BnaFAH orthologs through gene editing and further elucidated the molecular mechanisms of oxidative stress-induced PCD in plants.

Early mucosal responses in common carp (Cyprinus carpio) to the infection of Aeromonas hydrophila

Common carp (Cyprinus carpio), an economically important freshwater fish species, is worldwide farmed especially in Europe and Asia. The current high-density intensive rearing way leading to a high susceptibility to various pathogens, and Aeromonas hydrophila is one of the most frequently encountered bacteria, causing huge economic losses to the common carp industry. The mucosal barrier of fish constitutes the first line of defense against various pathogens, and some level of inter-connectivity exists among teleost mucosal tissues. However, the molecular basis for a common mucosal immune response at multiple sites or response at one site but during different timepoints following stimulation remains to be studied. In this study, we examined and systematically analyzed a total of 144 transcriptome files from three primary common carp mucosal tissues at four early timepoints following the infection of A. hydrophila. A total of 410 unigenes were significantly differentially expressed in all tested mucosal tissues. Through GO enrichment and KEGG pathway analysis, 28 key genes which might played critical roles in the common mucosal immune response of common carp during the early stage of bacterial infection were identified. Further WGCNA analysis showed that, besides the common response, three mucosal tissues exhibited tissue-specific gene regulatory network. Meanwhile, candidate hub genes in each tissue were identified, including trim3, rpl14, tln2, itpr1, and others. Our results will provide a fundamental basis for understanding the molecular mechanisms of teleost mucosal immunity, and might suggest strategies for developing novel teleost mucosal vaccines in aquaculture.

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn’s disease by integrating bioinformatics and machine learning

Crohn’s disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. This study aimed to identify specific diagnostic markers for CD using advanced computational approaches. Four gene expression datasets from the Gene Expression Omnibus (GEO) were analyzed, identifying differentially expressed genes (DEGs) through gene set enrichment analysis in R. Key biomarkers were selected using machine learning algorithms, including LASSO logistic regression, SVM‑RFE, and Random Forest, and their accuracy was assessed using receiver operating characteristic (ROC) curves and nomogram models. Immune cell infiltration was analyzed using the CIBERSORT algorithm, which helped reveal associations between diagnostic markers and immune cell patterns in CD. From a training set of 605 CD samples and 82 normal controls, we identified eight significant biomarkers: LCN2, FOLH1, CXCL1, FPR1, S100P, IGFBP5, CHP2, and AQP9. The diagnostic model showed high predictive power (AUC=0.954) and performed well in external validation (AUC = 1). Immune cell infiltration analysis highlighted various immune cells involved in CD, with all diagnostic markers strongly linked to immune cell interactions. Our findings propose candidate hub genes and present a nomogram for CD diagnosis, providing potential diagnostic biomarkers for clinical applications in CD.

Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics

To explore the pathogenesis and potential biomarkers of atrial fibrillation based on bioinformatics. Differentially expressed genes and module genes related to atrial fibrillation were obtained from GSE41177 and GSE79768 datasets (Chinese-origin tissue samples) through differential expression analysis and weighted gene co-expression network analysis. Candidate hub genes were obtained by taking intersections, and hub genes were obtained after gender stratification. Subsequently, functional enrichment analysis and immune infiltration analysis were performed. Four machine learning models were constructed based on the hub genes, and the optimal model was selected to construct a prediction nomogram. The prediction ability of the nomogram was verified using calibration curves and decision curves. Finally, potential therapeutic drugs for atrial fibrillation were screened from the DGIdb database. A total of 67 differentially expressed genes and 65 module genes related to atrial fibrillation were identified. Functional enrichment analysis indicated that the pathogenesis of atrial fibrillation was closely related to inflammatory response, immune response, and immune and infectious diseases. Four common hub genes (TYROBP, FCER1G, EVI2B and SOD2), and two genes specifically expressed in male (PILRA and SLC35G3) and female (HLA-DRA and GATP) patients with atrial fibrillation were obtained after gender-segregated screening. The extreme gradient boosting model had satisfactory diagnostic efficiency, and the nomogram constructed based on the hub genes, male significant variables (PILRA, SLC35G3 and SOD2), and female significant variables (FCER1G, SOD2 and TYROBP) had satisfactory predictive ability. Immune infiltration analysis demonstrated a disturbed immune infiltration microenvironment in atrial fibrillation with a higher abundance of plasma cells, neutrophils, and γδT cells, with a higher abundance of neutrophils in males and resting mast cells in females. Two potential drugs for the treatment of atrial fibrillation, valproic acid and methotrexate, were obtained by database and literature screening. The pathogenesis of atrial fibrillation is closely related to inflammation and immune response, and the microenvironment of immune cell infiltration of cardiomyocytes in the atrial tissue of patients with atrial fibrillation is disordered. TYROBP, FCER1G, EVI2B and SOD2 serve as potential diagnostic biomarkers of atrial fibrillation; PILRA and SLC35G3 serve as potential specific diagnostic biomarkers of atrial fibrillation in the male population, which can effectively predict the risk of atrial fibrillation development and are also potential targets for the treatment of atrial fibrillation.

Identification of diagnostic candidate genes in COVID-19 patients with sepsis.

Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis. We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration. The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes. Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis

BackgroundDiabetic nephropathy (DN) is a common and severe microvascular complication of diabetes. Mitochondrial dysfunction and immune inflammation are important factors in the pathogenesis of DN. However, the specific mechanisms and their intricate interactions in DN remain unclear. Besides, there are no effective specific predictive or diagnostic biomarkers for DN so far. Therefore, this study aims to elucidate the role of mitochondrial-related genes and their possibility as predictive or diagnostic biomarkers, as well as their crosstalk with immune infiltration in the progression of DN. MethodsBased on the GEO database and limma R package, the differentially expressed genes (DEGs) of DN were identified. Mitochondrial-related DEGs (MitoDEGs) were then obtained by intersecting these DEGs with mitochondria-related genes from the MitoCarta 3.0 database. Subsequently, the candidate hub genes were further screened by gene co-expression network analysis (WGCNA), and verified mRNA levels of these genes by real-time quantitative PCR (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. The verified hub genes were utilized to construct a combined diagnostic model for DN, with its diagnostic efficacy assessed across the GSE30122 and GSE96804 datasets. Additionally, the immune infiltration pattern in DN was assessed with the CIBERSORT algorithm, and the Nephroseq v5 database was used to analyze the correlation between hub genes and clinical features of DN. ResultsSeven mitochondria-related candidate hub genes were screened from 56 MitoDEGs. Subsequently, the expression levels of six of them, namely EFHD1, CASP3, AASS, MPC1, NT5DC2, and BCL2A1, exhibited significant inter-group differences in the HK-2 cell model. The diagnostic model based on the six genes demonstrated good diagnostic efficacy in both training and validation sets. Furthermore, correlation analysis indicated that EFHD1 and AASS, downregulated in DN, are positively correlated with eGFR and negatively with serum creatinine. Conversely, CASP3, NT5DC2, and BCL2A1, upregulated in DN, show opposite correlations. In addition, spearman analysis revealed that the six hub genes were significantly associated with the infiltration of immune cells, including M1 and M2 macrophages, mast cells, resting NK cells, gamma delta T cells, and follicular helper T cells. ConclusionThis study elucidated the characteristics of mitochondria-related genes and their correlation with immune cell infiltration in DN, providing new insights for exploring the pathogenesis of DN and facilitating the identification of new potential biomarkers and therapeutic targets.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features. By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029). Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.

Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD. Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes. This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

Integrated transcriptomic and proteomic analyses reveal the impact of drought and heat stress combination on Morus alba

The mulberry (Morus alba L.) tree is an economically and ecologically important perennial woody plant. With climate change, heat, and drought stresses have become more frequent and intense in mulberry growing areas with a strong influence on phenology. However, how mulberry performs in terms of physiological responses under combined drought and heat stress is unknown. Here, we firstly studied the characteristics of three forage mulberry varieties (Guisangyou 62, Guisangyou 12, Yuesang 51) response to combined drought and heat stress (WD+HS) via integrated physiological, transcriptome and proteome profiles. Weighted gene co-expression network analysis (WGCNA) was conducted and we detected four important modules and 195 candidate genes between three cultivars related to WD+HS. 1,795 differentially-expressed proteins (DEPs) and 329 proteins abundance changed significantly between three cultivars were identified by tandem mass tag (TMT) quantitative proteomic analysis. Finally, six genes including M.alba_G0006903 (HSP21), M.alba_G0008420 (HSP70T-2), M.alba_G0017894 (PPH), M.alba_G0019599 (BAG6), M.alba_G0007122 (MybSt1) and M.alba_G0012112 (GT2) in three cultivars were identified as candidate hub genes both in co-expression network and PPI network. These results lay a foundation for elucidating the molecular mechanism of the mulberry response to combined drought and heat stress.

Exploring antithrombotic mechanisms and effective constituents of Lagopsis supina using an integrated strategy based on network pharmacology, molecular docking, metabolomics, and experimental verification in rats

Ethnopharmacological relevanceThrombosis is a common cause of morbidity and mortality worldwide. Lagopsis supina (Stephan ex Willd.) Ikonn.-Gal. ex Knorring is an ancient Chinese herbal medicine used for treating thrombotic diseases. Nevertheless, the antithrombotic mechanisms and effective constituents of this plant have not been clarified. Aim of the studyThis work aimed to elucidate the pharmacodynamics and mechanism of L. supina against thrombosis. Materials and methodsSystematic network pharmacology was used to explore candidate effective constituents and hub targets of L. supina against thrombosis. Subsequently, the binding affinities of major constituents with core targets were verified by molecular docking analysis. Afterward, the therapeutic effect and mechanism were evaluated in an arteriovenous bypass thrombosis rat model. In addition, the serum metabolomics analysis was conducted using ultra-high performance liquid chromatography coupled with Q-Exactive mass spectrometry. ResultsA total of 124 intersected targets of L. supina against thrombosis were predicted. Among them, 24 hub targets were obtained and their mainly associated with inflammation, angiogenesis, and thrombosis approaches. Furthermore, 9 candidate effective constituents, including (22E,24R)-5α,8α-epidioxyergosta-6,22-dien-3β-ol, aurantiamide, (22E,24R)-5α,8α-epidioxyergosta-6,9 (11),22-trien-3β-ol, lagopsinA, lagopsin C, 15-epi-lagopsin C, lagopsin D, 15-epi-lagopsin D, and lagopsin G in L. supina and 6 potential core targets (TLR-4, TNF-α, HIF-1α, VEGF-A, VEGFR-2, and CLEC1B) were acquired. Then, these 9 constituents demonstrated strong binding affinities with the 6 targets, with their lowest binding energies were all less than −5.0 kcal/mol. The antithrombotic effect and potential mechanisms of L. supina were verified, showing a positively associated with the inhibition of inflammation (TNF-α, IL-1β, IL-6, IL-8, and IL-10) and coagulation cascade (TT, APTT, PT, FIB, AT-III), promotion of angiogenesis (VEGF), suppression of platelet activation (TXB2, 6-keto-PGF1α, and TXB2/6-keto-PGF1α), and prevention of fibrinolysis (t-PA, u-PA, PAI-1, PAI-1/t-PA, PAI-1/u-PA, and PLG). Finally, 14 endogenous differential metabolites from serum samples of rats were intervened by L. supina based on untargeted metabolomics analysis, which were closely related to amino acid metabolism, inflammatory and angiogenic pathways. ConclusionOur integrated strategy based on network pharmacology, molecular docking, metabolomics, and in vivo experiments revealed for the first time that L. supina exerts a significant antithrombotic effect through the inhibition of inflammation and coagulation cascade, promotion of angiogenesis, and suppression of platelet activation. This paper provides novel insight into the potential of L. supina as a candidate agent to treat thrombosis.

Transcriptome and weighted gene co-expression network analyses reveal key genes and pathways involved in early fruit ripening in Citrus sinensis

BackgroundThe fruit ripening period is an important target trait in fruit tree crop breeding programs. Thus, citrus tree breeders seek to develop extreme early ripening cultivars that allow optimization of citrus maturation periods. In this study, we explored the regulatory network involved in fruit ripening in Citrus sinensis using the ‘Newhall’ navel orange variety and its early-ripening mutant, ‘Gannanzao’. This research will provide a basis for further research on important signaling pathways, gene functions and variety breeding of Citrus sinensis related to fruit ripening period.ResultsPhysiological analyses suggested that early fruit ripening in ‘Gannanzao’ is regulated by early accumulation of abscisic acid (ABA), persistently high levels of jasmonic acid (JA), and higher sucrose content in the pericarp. Pericarp samples from ‘Gannanzao’ and ‘Newhall’ navel oranges were sampled for RNA sequencing analysis at 180, 200, and 220 days after flowering; 1430 differentially expressed genes (DEGs) were identified. Functional enrichment analysis indicated that these DEGs were mainly enriched in the plant hormone signal transduction and sugar metabolism pathways, as well as other pathways related to fruit ripening. Important DEGs associated with fruit ripening in ‘Gannanzao’ included genes involved in ABA and JA metabolism and signal transduction, as well as sugar metabolism. Weighted gene co-expression network analysis showed that the deep pink module had the strongest correlations with ABA content, JA content, and early ripening. Based on gene functionality and gene expression analyses of 37 genes in this module, two candidate hub genes and two ethylene response factor 13 (ERF13) genes (Cs_ont_5g000690 and Cs_ont_5g000700) were identified as key genes regulated by ABA and JA signaling. These findings will help to clarify the mechanisms that underlie early citrus fruit ripening and will lead to the development of excellent genetic resources for further breeding of extreme early-ripening varieties.ConclusionsThrough analyses of the ‘Newhall’ navel orange cultivar and its early-ripening mutant ‘Gannanzao’, we identified genes involved in ABA and JA metabolism, signal transduction, and sugar metabolism that were related to fruit ripening. Among these, two ERF13 genes were inferred to be key genes in the regulation of fruit ripening. These findings provide insights into the genetic architecture related to early fruit ripening in C. sinensis.

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ–δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

Exploration of effective biomarkers for venous thrombosis embolism in Behçet’s disease based on comprehensive bioinformatics analysis

Behçet’s disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein–protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723–0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

Co-regulatory network analysis of the main secondary metabolite (SM) biosynthesis in Crocus sativus L.

Saffron (Crocus sativus L.) is being embraced as the most important medicinal plant and the commercial source of saffron spice. Despite the beneficial economic and medicinal properties of saffron, the regulatory mechanism of the correlation of TFs and genes related to the biosynthesis of the apocarotenoids pathway is less obvious. Realizing these regulatory hierarchies of gene expression networks related to secondary metabolites production events is the main challenge owing to the complex and extensive interactions between the genetic behaviors. Recently, high throughput expression data have been highly feasible for constructing co-regulation networks to reveal the regulated processes and identifying novel candidate hub genes in response to complex processes of the biosynthesis of secondary metabolites. Herein, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method, to identify 11 regulated modules and hub TFs related to secondary metabolites. Three specialized modules were found in the apocarotenoids pathway. Several hub TFs were identified in notable modules, including MADS, C2H2, ERF, bZIP, HD-ZIP, and zinc finger protein MYB and HB, which were potentially associated with apocarotenoid biosynthesis. Furthermore, the expression levels of six hub TFs and six co-regulated genes of apocarotenoids were validated with RT-qPCR. The results confirmed that hub TFs specially MADS, C2H2, and ERF had a high correlation (P < 0.05) and a positive effect on genes under their control in apocarotenoid biosynthesis (CCD2, GLT2, and ADH) among different C. sativus ecotypes in which the metabolite contents were assayed. Promoter analysis of the co-expressed genes of the modules involved in apocarotenoids biosynthesis pathway suggested that not only are the genes co-expressed, but also share common regulatory motifs specially related to hub TFs of each module and that they may describe their common regulation. The result can be used to engineer valuable secondary metabolites of C. sativus by manipulating the hub regulatory TFs.

Modeling and design of solar+storage-powered community resilience hubs across California.

Distributed clean, reliable energy resources like solar plus battery storage (solar+storage) can reduce harmful emissions while supporting resilience. Solar+storage-powered resilience hubs provide energy for critical services during disasters while increasing human adaptive capacity year round. We studied where utility rates, local climate, and historical injustice make solar+storage resilience hubs more valuable and more challenging. We modeled the economic and climate impacts of outfitting candidate hub sites across California with solar+storage for everyday operations and identified designs and costs required to withstand a range of outages considering weather impacts on energy needs and availability. We integrated sociodemographic data to prioritize the siting of resilience hubs, to focus potential policy and funding priorities on regions where solar+storage for resilience hubs is hard or expensive, and where populations are most in need. We identified almost 20,000 candidate buildings with more than 8 GW of total rooftop solar potential capable of reducing CO2 emissions by 5 million tons per year while providing energy for community resilience. Hub capacity for one of the most challenging missions-providing emergency shelter during a power outage and smoke event-could have a statewide average lifetime cost of less than $2000 per seat. We identified regional challenges including insufficient rooftop solar capacity in cities, low sunlight in northern coastal California, and high costs driven by utility rate structures in Sacramento and the Imperial Valley. Results show that rates and net metering rules that incentivize solar+storage during everyday operations decrease resilience costs.

Insights into Co-Expression Network Analysis of MicroProteins and their Target Transcription Factors in Plant Embryo Development

Background: Gene expression is regulated in a spatiotemporal manner, and the roles of microProteins (MiPs) in this concept have started to become clear in plants. Methods: Here, a microarray data analysis was carried out to decipher the spatiotemporal role of MiPs in embryo development. The guilt-by-association method was used to determine the corresponding regulatory factors. Results: Module network analyses and protein-protein interaction (PPI) assays suggested 13 modules for embryo development in the Arabidopsis model plant. Various biological processes such as metabolite biosynthesis, hormone transition and regulation, fatty acid and storage protein biosynthesis, and photosynthesis-related processes were prevalent. Different transcription factors (TFs) at different stages of embryo development were found and reviewed. Furthermore, 106 putative MiPs were identified that might be involved in the regulation of embryo development. Candidate hub MiPs (15) at embryo developmental stages were identified by PPI network analysis and their putative regulatory roles were discussed. Previously reported MiPs, AT1G14760 (KNOX), AT5G39860 (PRE1), and AT2G46410 (CPC), were noted to be present in modules M3 and M8. Conclusion: Molecular comprehension of regulatory factors including MiPs and TFs during embryo development allows targeted breeding of the corresponding traits and genome-based engineering of value-added new varieties.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D. The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database. A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes. "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.