Decoding the molecular landscape: A novel prognostic signature for uveal melanoma unveiled through programmed cell death-associated genes.

Uveal melanoma (UM) is a rare but aggressive malignant ocular tumor with a high metastatic potential and limited therapeutic options, currently lacking accurate prognostic predictors and effective individualized treatment strategies. Public databases were utilized to analyze the prognostic relevance of programmed cell death-related genes (PCDRGs) in UM transcriptomes and survival data. Consensus clustering and Lasso Cox regression analysis were performed for molecular subtyping and risk feature construction. The PCDRG-derived index (PCDI) was evaluated for its association with clinicopathological features, gene expression, drug sensitivity, and immune infiltration. A total of 369 prognostic PCDRGs were identified, which could cluster UM into 2 molecular subtypes with significant differences in prognosis and clinicopathological characteristics. Furthermore, a risk feature PCDI composed of 11 PCDRGs was constructed, capable of indicating prognosis in UM patients. Additionally, PCDI exhibited correlations with the sensitivity to 25 drugs and the infiltration of various immune cells. Enrichment analysis revealed that PCDI was associated with immune regulation-related biological processes and pathways. Finally, a nomogram for prognostic assessment of UM patients was developed based on PCDI and gender, demonstrating excellent performance. This study elucidated the potential value of PCDRGs in prognostic assessment for UM and developed a corresponding risk feature. However, further basic and clinical studies are warranted to validate the functions and mechanisms of PCDRGs in UM.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

Exploring the role of CDCA4 in liver hepatocellular carcinoma using bioinformatics analysis and experiments.

Liver hepatocellular carcinoma (LIHC) encompasses diverse therapeutic approaches, among which targeted therapy has gained significant prominence in recent years. The identification of numerous targets and the increasing clinical application of targeted drugs have greatly improved LIHC treatment. However, the precise role of CDCA4 (Cell Division Cycle Associated 4), as well as its underlying mechanisms and prognostic implications in LIHC, remains unclear. CDCA4 expression levels in LIHC were analyzed using multiple databases including the cancer genome atlas (TCGA), gene expression profiling interactive analysis (GEPIA), and ULCAN, as well as the datasets E_TABM_36, GSE144269, GSE14520, and GSE54236. The prognostic value of CDCA4 was then evaluated. Subsequently, the association between CDCA4 and immune cells was investigated. Enrichment analysis (GSEA) was utilized to investigate the functional roles and pathways linked to CDCA4. Additionally, the methylation patterns and drug sensitivity of CDCA4 were examined. A predictive model incorporating immune genes related to CDCA4 was developed. The TISCH dataset was used to investigate the single-cell expression patterns of CDCA4. Finally, validation of CDCA4 expression levels was conducted through RT-PCR, Western blotting, and immunohistochemistry. CDCA4 exhibited significant overexpression in LIHC and demonstrated significant correlations with clinical features. High expression of CDCA4 is associated with a poorer prognosis. Analysis of immune infiltration and enrichment revealed its association with the immune microenvironment. Furthermore, its expression is correlated with methylation and mutation patterns. CDCA4 is associated with 19 drugs. Prognostic models utilizing CDCA4 demonstrate favorable effectiveness. T cell subtypes were found to be associated with CDCA4 through single-cell analysis. The conclusive experiment provided evidence of significant upregulation of CDCA4 in LIHC. The high expression of CDCA4 in LIHC is associated with prognostic significance and is highly expressed in T cell subtypes, providing a new therapeutic target and potential therapeutic strategy for LIHC.

The role of the immune system in early-onset schizophrenia: identifying immune characteristic genes and cells from peripheral blood.

BACKGROUND: Early-onset schizophrenia (EOS) is a type of schizophrenia (SCZ) with an age of onset of < 18 years. An abnormal inflammatory immune system may be involved in the occurrence and development of SCZ. We aimed to identify the immune characteristic genes and cells involved in EOS and to further explore the pathogenesis of EOS from the perspective of immunology. METHODS: We obtained microarray data from a whole-genome mRNA expression in peripheral blood mononuclear cells (PBMCs); 19 patients with EOS (age range: 14.79 ± 1.90) and 18 healthy controls (HC) (age range: 15.67 ± 2.40) were involved. We screened for differentially expressed genes (DEGs) using the Limma software package and modular genes using weighted gene co-expression network analysis (WGCNA). In addition, to identify immune characteristic genes and cells, we performed enrichment analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis; we also used a random forest (RF), a support vector machine (SVM), and the LASSO-Cox algorithm. RESULTS: We selected the following immune characteristic genes: CCL8, PSMD1, AVPR1B and SEMG1. We employed a RF, a SVM, and the LASSO-Cox algorithm. We identified the following immune characteristic cells: activated mast cells, CD4+ memory resting T cells, resting mast cells, neutrophils and CD4+ memory activated T cells. In addition, the AUC values of the immune characteristic genes and cells were all > 0.7. CONCLUSION: Our results indicate that immune system function is altered in SCZ. In addition, CCL8, PSMD1, AVPR1B and SEMG1 may regulate peripheral immune cells in EOS. Further, immune characteristic genes and cells are expected to be diagnostic markers and therapeutic targets of SCZ.

Genome-wide identification and expression analysis of the SET domain-containing gene family in potato (Solanum tuberosum L.).

Genes containing the SET domain can catalyse histone lysine methylation, which in turn has the potential to cause changes to chromatin structure and regulation of the transcription of genes involved in diverse physiological and developmental processes. However, the functions of SET domain-containing (StSET) genes in potato still need to be studied. The objectives of our study can be summarized as in silico analysis to (i) identify StSET genes in the potato genome, (ii) systematically analyse gene structure, chromosomal distribution, gene duplication events, promoter sequences, and protein domains, (iii) perform phylogenetic analyses, (iv) compare the SET domain-containing genes of potato with other plant species with respect to protein domains and orthologous relationships, (v) analyse tissue-specific expression, and (vi) study the expression of StSET genes in response to drought and heat stresses. In this study, we identified 57 StSET genes in the potato genome, and the genes were physically mapped onto eleven chromosomes. The phylogenetic analysis grouped these StSET genes into six clades. We found that tandem duplication through sub-functionalisation has contributed only marginally to the expansion of the StSET gene family. The protein domain TDBD (PFAM ID: PF16135) was detected in StSET genes of potato while it was absent in all other previously studied species. This study described three pollen-specific StSET genes in the potato genome. Expression analysis of four StSET genes under heat and drought in three potato clones revealed that these genes might have non-overlapping roles under different abiotic stress conditions and durations. The present study provides a comprehensive analysis of StSET genes in potatoes, and it serves as a basis for further functional characterisation of StSET genes towards understanding their underpinning biological mechanisms in conferring stress tolerance.

Analyzing the expression and clinical significance of CENPE in gastric cancer.

BACKGROUND: Gastric cancer (GC) is a prevalent type of malignant gastrointestinal tumor. Many studies have shown that CENPE acts as an oncogene in some cancers. However, its expression level and clinical value in GC are not clear. METHODS: Obtaining clinical data information on gastric adenocarcinoma from TCGA and GEO databases. The gene expression profiling interaction analysis (GEPIA) was used to evaluate the relationship between prognosis and CENPE expression in gastric cancer patients. Utilizing the UALCAN platform, the correlation between CENPE expression and clinical parameters was examined. Functions and signaling pathways of CENPE were analyzed using the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The association between immunological infiltrating cells and CENPE expression was examined using TIMER2.0. Validation was performed by real-time quantitative PCR (qPT-PCR) and immunohistochemical analysis. RESULTS: According to the analysis of the GEPIA database, the expression of CENPE is increased in gastric cancer tissues compared to normal tissues. It was also found to have an important relationship with the prognosis of the patient (p<0.05). The prognosis was worse and overall survival was lower in individuals with increased expression of CENPE. In line with the findings of the GEPIA, real-time fluorescence quantitative PCR (qPT-PCR) confirmed that CENPE was overexpressed in gastric cancer cells. Furthermore, It was discovered that H. pylori infection status and tumor grade were related to CENPE expression. Enrichment analysis revealed that CENPE expression was linked to multiple biological functions and tumor-associated pathways. CENPE expression also correlated with immune-infiltrating cells in the gastric cancer microenvironment and was positively connected to NK cells and mast cells. According to immunohistochemical examination, paracancerous tissues had minimal expression of CENPE, but gastric cancer showed significant expression of the protein. CONCLUSIONS: According to our findings, CENPE is substantially expressed in GC and may perhaps contribute to its growth. CENPE might be a target for gastric cancer therapy and a predictor of a bad prognosis.

Identification of prognostic stemness-related genes in kidney renal papillary cell carcinoma.

BACKGROUND: Kidney renal papillary cell carcinoma (KIRP) is the second most prevalent malignant cancer originating from the renal epithelium. Nowadays, cancer stem cells and stemness-related genes (SRGs) are revealed to play important roles in the carcinogenesis and metastasis of various tumors. Consequently, we aim to investigate the underlying mechanisms of SRGs in KIRP. METHODS: RNA-seq profiles of 141 KIRP samples were downloaded from the TCGA database, based on which we calculated the mRNA expression-based stemness index (mRNAsi). Next, we selected the differentially expressed genes (DEGs) between low- and high-mRNAsi groups. Then, we utilized weighted gene correlation network analysis (WGCNA) and univariate Cox analysis to identify prognostic SRGs. Afterwards, SRGs were included in the multivariate Cox regression analysis to establish a prognostic model. In addition, a regulatory network was constructed by Pearson correlation analysis, incorporating key genes, upstream transcription factors (TFs), and downstream signaling pathways. Finally, we used Connectivity map analysis to identify the potential inhibitors. RESULTS: In total, 1124 genes were characterized as DEGs between low- and high-RNAsi groups. Based on six prognostic SRGs (CCKBR, GPR50, GDNF, SPOCK3, KC877982.1, and MYO15A), a prediction model was established with an area under curve of 0.861. Furthermore, among the TFs, genes, and signaling pathways that had significant correlations, the CBX2-ASPH-Notch signaling pathway was the most significantly correlated. Finally, resveratrol might be a potential inhibitor for KIRP. CONCLUSIONS: We suggested that CBX2 could regulate ASPH through activation of the Notch signaling pathway, which might be correlated with the carcinogenesis, development, and unfavorable prognosis of KIRP.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

Identification of key eRNAs for intervertebral disc degeneration by integrated multinomial bioinformatics analysis.

BACKGROUND: Intervertebral disc degeneration (IVDD) is a common degenerative condition leading to abnormal stress distribution under load, causing intervertebral stenosis, facet joint degeneration, and foraminal stenosis. Very little is known about the molecular mechanism of eRNAs in IVDD. METHODS: Gene expression profiles of 38 annulus disc samples composed of 27 less degenerated discs (LDs) and 11 more degenerated discs (MDs) were retrieved from the GEO database. Then, differentially expressed enhancer RNAs (DEeRNAs), differentially expressed target genes (DETGs), and differentially expressed transcription factors (DETFs), hallmark of cancer signalling pathways according to GSVA; the types and quantity of immune cells according to CIBERSORT; and immune gene sets according to ssGSEA were analysed to construct an IVDD-related eRNA network. Then, multidimensional validation was performed to explore the interactions among DEeRNAs, DETFs and DEGs in space. RESULTS: A total of 53 components, 14 DETGs, 15 DEeRNAs, 3 DETFs, 5 immune cells, 9 hallmarks, and 7 immune gene sets, were selected to construct the regulatory network. After validation by online multidimensional databases, 21 interactive DEeRNA-DEG-DETF axes related to IVDD exacerbation were identified, among which the C1S-CTNNB1-CHD4 axis was the most significant. CONCLUSION: Based upon the results of our study, we theorize that the C1S-CTNNB1-CHD4 axis plays a vital role in IVDD exacerbation. Specifically, C1S recruits CTNNB1 and upregulates the expression of CHD4 in IVDD, and subsequently, CHD4 suppresses glycolysis and activates oxidative phosphorylation, thus generating insoluble collagen fibre deposits and leading to the progression of IVDD. Overall, these DEeRNAs could comprise promising therapeutic targets for IVDD due to their high tissue specificity.

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance.

BACKGROUND: The transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs). RESULTS: A total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites. CONCLUSION: This study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. CONCLUSION: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Transcriptional profiles associated with coronary artery disease in type 2 diabetes mellitus.

Background: Coronary artery disease (CAD) is a common complication of Type 2 diabetes mellitus (T2DM). Understanding the pathogenesis of this complication is essential in both diagnosis and management. Thus, this study aimed to characterize the presence of CAD in T2DM using molecular markers and pathway analyses. Methods: The study is a sex- and age-frequency matched case-control design comparing 23 unrelated adult Filipinos with T2DM-CAD to 23 controls (DM with CAD). Healthy controls served as a reference. Total RNA from peripheral blood mononuclear cells (PBMCs) underwent whole transcriptomic profiling using the Illumina HumanHT-12 v4.0 expression beadchip. Differential gene expression with gene ontogeny analyses was performed, with supporting correlational analyses using weighted correlation network analysis (WGCNA). Results: The study observed that 458 genes were differentially expressed between T2DM with and without CAD (FDR<0.05). The 5 top genes the transcription factor 3 (TCF3), allograft inflammatory factor 1 (AIF1), nuclear factor, interleukin 3 regulated (NFIL3), paired immunoglobulin-like type 2 receptor alpha (PILRA), and cytoskeleton-associated protein 4 (CKAP4) with AUCs >89%. Pathway analyses show differences in innate immunity activity, which centers on the myelocytic (neutrophilic/monocytic) theme. SNP-module analyses point to a possible causal dysfunction in innate immunity that triggers the CAD injury in T2DM. Conclusion: The study findings indicate the involvement of innate immunity in the development of T2DM-CAD, and potential immunity markers can reflect the occurrence of this injury. Further studies can verify the mechanistic hypothesis and use of the markers.

Analysis of drought and heat stress response genes in rice using co-expression network and differentially expressed gene analyses.

Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2, and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1, although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Transcriptome analysis reveals important regulatory factors for condensed tannins synthesis in acorn.

Condensed tannins are widely present in the fruits and seeds of plants and effectively prevent them from being eaten by animals before maturity due to their astringent taste. In addition, condensed tannins are a natural compound with strong antioxidant properties and significant antibacterial effects. Four samples of mature and near-mature Quercus fabri acorns, with the highest and lowest condensed tannin content, were used for genome-based transcriptome sequencing. The KEGG enrichment analysis revealed that the differentially expressed genes (DEGs) were highly enriched in phenylpropanoid biosynthesis and starch and sucrose metabolism. Given that the phenylpropanoid biosynthesis pathway is a crucial step in the synthesis of condensed tannins, we screened for significantly differentially expressed transcription factors and structural genes from the transcriptome data of this pathway and found that the expression levels of four MADS-box, PAL, and 4CL genes were significantly increased in acorns with high condensed tannin content. The quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) experiment further validated this result. In addition, yeast one-hybrid assay confirmed that three MADS-box transcription factors could bind the promoter of the 4CL gene, thereby regulating gene expression levels. This study utilized transcriptome sequencing to discover new important regulatory factors that can regulate the synthesis of acorn condensed tannins, providing new evidence for MADS-box transcription factors to regulate the synthesis of secondary metabolites in fruits.

Transcriptome-wide identification of ARF gene family in medicinal plant Polygonatum kingianum and expression analysis of PkARF members in different tissues.

BACKGROUND: Polygonatum kingianum holds significant importance in Traditional Chinese Medicine due to its medicinal properties, characterized by its diverse chemical constituents including polysaccharides, terpenoids, flavonoids, phenols, and phenylpropanoids. The Auxin Response Factor (ARF) is a pivotal transcription factor known for its regulatory role in both primary and secondary metabolite synthesis. However, our understanding of the ARF gene family in P. kingianum remains limited. METHODS AND RESULTS: We employed RNA-Seq to sequence three distinct tissues (leaf, root, and stem) of P. kingianum. The analysis revealed a total of 31,558 differentially expressed genes (DEGs), with 43 species of transcription factors annotated among them. Analyses via gene ontology and the Kyoto Encyclopedia of Genes and Genomes demonstrated that these DEGs were predominantly enriched in metabolic pathways and secondary metabolite biosynthesis. The proposed temporal expression analysis categorized the DEGs into nine clusters, suggesting the same expression trends that may be coordinated in multiple biological processes across the three tissues. Additionally, we conducted screening and expression pattern analysis of the ARF gene family, identifying 12 significantly expressed PkARF genes in P. kingianum roots. This discovery lays the groundwork for investigations into the role of PkARF genes in root growth, development, and secondary metabolism regulation. CONCLUSION: The obtained data and insights serve as a focal point for further research studies, centred on genetic manipulation of growth and secondary metabolism in P. kingianum. Furthermore, these findings contribute to the understanding of functional genomics in P. kingianum, offering valuable genetic resources.

Transcriptome analysis and functional validation reveal the novel role of LhCYCL in axillary bud development in hybrid Liriodendron.

Shoot branching significantly influences yield and timber quality in woody plants, with hybrid Liriodendron being particularly valuable due to its rapid growth. However, understanding of the mechanisms governing shoot branching in hybrid Liriodendron remains limited. In this study, we systematically examined axillary bud development using morphological and anatomical approaches and selected four distinct developmental stages for an extensive transcriptome analysis. A total of 9,449 differentially expressed genes have been identified, many of which are involved in plant hormone signal transduction pathways. Additionally, we identified several transcription factors downregulated during early axillary bud development, including a noteworthy gene annotated as CYC-like from the TCP TF family, which emerged as a strong candidate for modulating axillary bud development. Quantitative real-time polymerase chain reaction results confirmed the highest expression levels of LhCYCL in hybrid Liriodendron axillary buds, while histochemical ß-glucuronidase staining suggested its potential role in Arabidopsis thaliana leaf axil development. Ectopic expression of LhCYCL in A. thaliana led to an increase of branches and a decrease of plant height, accompanied by altered expression of genes involved in the plant hormone signaling pathways. This indicates the involvement of LhCYCL in regulating shoot branching through plant hormone signaling pathways. In summary, our results emphasize the pivotal role played by LhCYCL in shoot branching, offering insights into the function of the CYC-like gene and establishing a robust foundation for further investigations into the molecular mechanisms governing axillary bud development in hybrid Liriodendron.

Transcriptome analysis of long non-coding RNAs in Mycobacterium avium complex-infected macrophages.

Mycobacterium avium complex (MAC) is a non-tuberculous mycobacterium widely distributed in the environment. Even though MAC infection is increasing in older women and immunocompromised patients, to our knowledge there has been no comprehensive analysis of the MAC-infected host-cell transcriptome-and particularly of long non-coding RNAs (lncRNAs). By using in vitro-cultured primary mouse bone-marrow-derived macrophages (BMDMs) and Cap analysis of gene expression, we analyzed the transcriptional and kinetic landscape of macrophage genes, with a focus on lncRNAs, during MAC infection. MAC infection of macrophages induced the expression of immune/inflammatory response genes and other genes similar to those involved in M1 macrophage activation, consistent with previous reports, although Nos2 (M1 activation) and Arg1 (M2 activation) had distinct expression profiles. We identified 31 upregulated and 30 downregulated lncRNA promoters corresponding respectively to 18 and 26 lncRNAs. Upregulated lncRNAs were clustered into two groups-early and late upregulated-predicted to be associated with immune activation and the immune response to infection, respectively. Furthermore, an Ingenuity Pathway Analysis revealed canonical pathways and upstream transcription regulators associated with differentially expressed lncRNAs. Several differentially expressed lncRNAs reported elsewhere underwent expressional changes upon M1 or M2 preactivation and subsequent MAC infection. Finally, we showed that expressional change of lncRNAs in MAC-infected BMDMs was mediated by toll-like receptor 2, although there may be other mechanisms that sense MAC infection. We identified differentially expressed lncRNAs in MAC-infected BMDMs, revealing diverse features that imply the distinct roles of these lncRNAs in MAC infection and macrophage polarization.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion: RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.