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The purpose was to identify shared immune cells and co-disease genes in chronic heart failure (HF) and systemic lupus erythematosus (SLE), as well as explore the potential mechanisms of action between HF and SLE. A collection of peripheral blood mononuclear cells (PBMCs) from ten patients with HF and SLE and ten normal controls (NC) was used for transcriptome sequencing. Differentially expressed genes (DEGs) analysis, enrichment analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and machine learning were applied for the screening of shared immune cells and co-disease genes in HF and SLE. Gene expression analysis and correlation analysis were used to explore the potential mechanisms of co-disease genes and immune cells in HF and SLE. In this study, it was found that two immune cells, T cells CD4 naïve and Monocytes, displayed similar expression patterns in HF and SLE at the same time. By taking intersection of the above immune cell-associated genes with the DEGs common to both HF and SLE, four immune-associated co-disease genes, CCR7, RNASE2, RNASE3 and CXCL10, were finally identified. CCR7, as one of the four key genes, was significantly down-regulated in HF and SLE, while the rest three key genes were all significantly up-regulated in both diseases. T cells CD4 naïve and Monocytes were first revealed as possible shared immune cells of HF and SLE, and CCR7, RNASE2, RNASE3 and CXCL10 were identified as possible key genes common to HF and SLE as well as potential biomarkers or therapeutic targets for HF and SLE.

Global climate change poses significant challenges to agricultural production and food security, particularly in high-latitude regions such as northern China, where low temperatures, drought, and extreme weather severely affect crop growth and development. Addressing these challenges requires a thorough understanding of crop cold tolerance, which is crucial for ensuring food security in northern China. Cold stress, including chilling and freezing injuries, could induce a range of physiological and biochemical damages in plants. Plants, however, can perceive low-temperature signals and enhance their cold tolerance through mechanisms such as the CBF/DREB transcriptional regulatory pathway. In this review, we summarize the low-temperature response and regulatory mechanisms of major crops in northern China, including wheat, maize, rice, and potato, with a focus on the unique genes and adaptive strategies these crops have evolved to improve cold tolerance. These insights not only advance our understanding of the molecular mechanisms underlying cold tolerance in key northern crops but also provide a theoretical basis for breeding cold-tolerant varieties and developing climate-resilient agriculture in northern China.

BackgroundPelvic organ prolapse (POP) is a prevalent disease among women, and immune cell and telomere have potential associations with the pathogenesis of POP. The identification and validation of immune cell-related genes (ICRGs) and telomere-related genes (TRGs) in POP are of great significance for elucidating its mechanisms, screening diagnostic key genes, and identifying therapeutic targets.MethodsIn this study, first, ICRGs were obtained based on immune infiltration and WGCNA; key genes related to immune cell and telomere in POP were identified from public-database transcriptome data through differential expression analysis, machine learning, expression level analysis and ROC analysis. Subsequently, a comprehensive analysis including nomogram construction, correlation analysis, GSEA, molecular regulatory network construction, and drug prediction explored the molecular mechanisms of these key genes in POP.ResultsWe identified 864 differentially expressed genes (DEGs), including 833 upregulated and 31 downregulated genes. The intersection of DEGs, telomere-related genes (TRGs), and immune cell-related genes (ICRGs) yielded six candidate genes. Machine learning further pinpointed CCNL1 and NAMPT as key biomarkers, which were significantly upregulated in POP samples (p < 0.05) and validated by RT-qPCR. Subsequently, a comprehensive analysis revealed their diagnostic potential via a nomogram (AUC: 0.847), a strong positive correlation (r = 0.78, p < 0.001), and enrichment in pathways such as ubiquitin-mediated proteolysis (GSEA). Molecular regulatory network construction predicted interactions with 21 key nodes and 28 interactions, and drug prediction identified 14 potential therapeutic compounds. These findings provide a theoretical basis for understanding POP pathogenesis and identifying novel therapeutic targets.ConclusionThis study identifies CCNL1 and NAMPT as novel immune- and telomere-related biomarkers for POP. These findings provide potential targets for diagnostic development and lay a computational foundation for future therapeutic strategies, although experimental validation is required to confirm their causal roles.

Commercial cultivars of garlic are sterile, and therefore efficient breeding of this crop is impossible. Recent restoration of garlic fertility has opened new options for seed production and hybridization. Transcriptome catalogs were employed as a basis for garlic genetic studies, and in 2020 the huge genome of garlic was fully sequenced. We provide conjoint genomic and transcriptome analysis of the regulatory network in flowering garlic genotypes. The genome analysis revealed phosphatidylethanolamine-binding proteins (PEBP) and LEAFY (LFY) genes that were not found at the transcriptome level. Functions of TFL-like genes were reduced and replaced by FT-like homologs, whereas homologs of MFT-like genes were not found. The discovery of three sequences of LFY-like genes in the garlic genome and confirmation of their alternative splicing suggest their role in garlic florogenesis. It is not yet clear whether AsLFY1 acts alone as the “pioneer transcription factor” or AsLFY2 also provides these functions. The presence of several orthologs of flowering genes that differ in their expression and co-expression network advocates ongoing evolution in the garlic genome and diversification of gene functions. We propose that the process of fertility deprivation in garlic cultivars is based on the loss of transcriptional functions of the specific genes.

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Background Huntington’s disease (HD) is a hereditary neurological disorder caused by mutations in HTT , leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. Results We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. Conclusion We developed the model capable of analyzing HD at single-cell transcriptomic level.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Sweet orange (Citrus sinensis) is recognized as one of the most significant citrus fruits globally. The sugar content of fruits is the most critical internal quality associated with taste in sweet oranges, serving as a vital determinant of fruit quality and commercial value. Therefore, a comprehensive exploration of the regulatory mechanisms governing sugar accumulation during fruit ripening holds substantial value for high-quality fruit breeding. In this study, we investigated citrus sugar accumulation using the flesh of the Newhall navel orange and its high-sugar-content mutant cultivar, Ganmi, as experimental materials. RNA sequencing of the flesh from both Ganmi and Newhall oranges at 180 and 200 days after flowering identified 642 and 493 differentially expressed genes (DEGs), respectively. Functional enrichment analysis indicated that DEGs were mainly enriched in the sugar metabolism pathways, sugar transporters, and plant hormone signal transduction. Important DEGs associated with fruit sugar accumulation in Ganmi included Cs_ont_2g004470 (CsNAC73) and Cs_ont_9g005250 (CsSTP13) involved in sugar accumulation. Weighted gene co-expression network analysis showed that 20 co-expression modules were obtained, and the brown1 module had the strongest correlations with sugar content. Based on gene functionality and gene expression analyses of 1189 genes in this module, three genes (Cs_ont_2g004470 (CsNAC73), Cs_ont_5g050360 (CsMYC2) and Cs_ont_3g002820 (CsBBX21)) were identified as key genes potentially related to sugar accumulation during the ripening. These findings may contribute to elucidating the mechanisms underlying sugar accumulation during ripening and provide insights for the molecular breeding of citrus varieties.

Type 2 diabetes (T2D) is a crucial risk factor for both pancreatic cancer (PC) and kidney cancer (KC). However, effective common drugs for treating PC and/or KC patients who are also suffering from T2D are currently lacking, despite the probability of their co-occurrence. Taking disease-specific multiple drugs during the co-existence of multiple diseases may lead to adverse side effects or toxicity to the patients due to drug-drug interactions. This study aimed to identify T2D-, PC and KC-causing common genomic biomarkers (cGBs) highlighting their pathogenetic mechanisms to explore effective drugs as their common treatment. We analyzed transcriptomic profile datasets, applying weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis approaches to identify T2D-, PC-, and KC-causing cGBs. We then disclosed common pathogenetic mechanisms through gene ontology (GO) terms, KEGG pathways, regulatory networks, and DNA methylation of these cGBs. Initially, we identified 78 common differentially expressed genes (cDEGs) that could distinguish T2D, PC, and KC samples from controls based on their transcriptomic profiles. From these, six top-ranked cDEGs (TOP2A, BIRC5, RRM2, ALB, MUC1, and E2F7) were selected as cGBs and considered targets for exploring common drug molecules for each of three diseases. Functional enrichment analyses, including GO terms, KEGG pathways, and regulatory network analyses involving transcription factors (TFs) and microRNAs, along with DNA methylation and immune infiltration studies, revealed critical common molecular mechanisms linked to PC, KC, and T2D. Finally, we identified six top-ranked drug molecules (NVP.BHG712, Irinotecan, Olaparib, Imatinib, RG-4733, and Linsitinib) as potential common treatments for PC, KC and T2D during their co-existence, supported by the literature reviews. Thus, this bioinformatics study provides valuable insights and resources for developing a genome-guided common treatment strategy for PC and/or KC patients who are also suffering from T2D.

Background Pigmented potatoes ( Solanum tuberosum L.) are rich in anthocyanin, which have antioxidantiy and play an important role in health and medical. Nevertheless, the regulation mechanism of anthocyanins in purple potato at different growth stages remain unclear. Results In this study, through using the high-throughput sequencing and systematic bioinformatics analysis, a total of 7,176 significantly different expressed genes (DEGs) were discovered from the purple potato Huasong 66 tubers at different developmental stages. Through GO and KEGG enrichment analysis, it was found that, 43 DEGs were mainly enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis, and phenylalanine metabolism, which biological processes are closely related to anthocyanin biosynthesis. The quantitative RT-PCR were verified the reliability of transcriptome data. We demonstrated that DEGs or transcription factors (TFs) which related to flavonoid metabolism were involved in the anthocyanins biosynthesis, such as the protein-coding genes PAL , CHS , CHI , 4CL , F3H , UFGT , LAR , and the TFs MYB, bHLH, and HY5. Conclusion The key genes involved in anthocyanin synthesis in potato tubers were identificated, it provides new insights for molecular breeding new cultivars. These results are valuable for improving the anthocyanin in potato.