BACKGROUND: Systemic Lupus Erythematosus (SLE) is a multifactorial and complex immune disease; however, the relevance of COVID-19 infection in SLE patients remains uncertain.
OBJECTIVE: This study aims to explore the key candidate genes and pathways in patients with SLE. It also seeks to employ bioinformatics analysis to unravel the molecular signatures inherent in both SLE and COVID-19 patients. The ultimate aim is to identify potential targets and markers specifically relevant to SLE patients who contract SARS-CoV-2.
METHODS: Datasets (GSE12374, GSE20864, GSE61635, GSE81622, and GSE144390) from the Gene Expression Omnibus (GEO) database were analyzed using Robust Rank Aggregation (RRA) method to identify differential expression genes (DEGs) in SLE patients compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, tissue-specific gene analysis, and Protein-protein interaction (PPI) network were performed. Finally, the Venn diagram was employed to identify the intersections of COVID-19 genes, serving as potential targets for SLE patients with COVID-19 infection.
RESULTS: A total of 154 DEGs were discovered, with GO enrichment indicating a predominant involvement in the defense response against the virus (P<0.001). KEGG pathway analysis showed enrichment in the NOD-like receptor signaling pathway and coronavirus disease, specifically COVID-19 (P<0.001). Tissue-specific genes related to the hematological and immune systems were emphasized (74%). The PPI network highlighted 22 genes, and 5 key genes, namely, IFIT1, IFIT3, MX1, MX2, and OAS3, which were identified after intersecting with COVID-19 patients\' data.
CONCLUSIONS: IFIT1, IFIT3, MX1, MX2, and OAS3 exhibiting differential expression, as well as the pathways associated with COVID-19, could potentially function as biomarkers and therapeutic targets for individuals with SLE infected with COVID-19.

Henosepilachna vigintioctomaculata is distributed in several Asian countries. The larvae and adults often cause substantial economic losses to Solanaceae crops such as potato, tomato, eggplant, and Chinese boxthorn. Even though a chromosome-level genome has been documented, the expression profiles of genes involved in development are not determined. In this study, we constructed embryonic, larval, pupal, and adult transcriptomes, generated a comprehensive RNA-sequencing dataset including ~52 Gb of clean data, and identified 602,773,686 cleaned reads and 33,269 unigenes. A total of 18,192 unigenes were successfully annotated against NCBI nonredundant protein sequences, Swissprot, Eukaryotic Orthologous Groups, Gene Ontology (GO), or Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. There were 3580, 2040, 5160, 2496, 3008, and 3895 differentially expressed genes (DEGs) between adult/egg, egg/larval, larval/pupal, adult/pupal, egg/pupal, and adult/larval samples, respectively. GO and KEGG analyses of the DEGs highlighted several critical pathways associated with specific developing stages. This is the first comprehensive transcriptomic dataset encompassing all developmental stages in H. vigintioctomaculata. Our data may facilitate the exploitation of gene targets for pest control and can serve as a valuable gene resource for future molecular investigations.

Doramectin, an essential animal anthelmintic, is synthesized through the fermentation process of Streptomyces avermitilis. This study delves into the transcriptomic profiles of two strains, namely the doramectin-producing wild-type S. avermitilis N72 and its highly doramectin-producing mutant counterpart, S. avermitilis XY-62. Comparative analysis revealed 860 up-regulated genes and 762 down-regulated genes in the mutant strain, notably impacting the expression of key genes pivotal in doramectin biosynthesis, including aveA1, aveA2, aveA3, aveA4, aveE, and aveBI. These findings shed light on the molecular mechanisms underpinning the heightened doramectin production in S. avermitilis XY-62, presenting promising avenues for optimizing doramectin production processes.

Low back pain (LBP) is a worldwide problem with public health. Paravertebral muscle degeneration (PMD) is believed to be associated with LBP. Increasing evidence has demonstrated that microRNA (miRNA)-mRNA signaling networks have been implicated in the pathophysiology of diseases. Research suggests that cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism are the pathogenesis of PMD; however, the miRNA-mRNA mediated the pathological process of PMD remains elusive. RNA sequencing (RNA-seq) and single cell RNA-seq (scRNA-seq) are invaluable tools for uncovering the functional biology underlying these miRNA and gene expression changes. Using scRNA-seq, we show that multiple immunocytes are presented during PMD, revealing that they may have been implicated with PMD. Additionally, using RNA-seq, we identified 76 differentially expressed genes (DEGs) and 106 differentially expressed miRNAs (DEMs), among which IL-24 and CCDC63 were the top upregulated and downregulated genes in PMD. Comprehensive bioinformatics analyses, including Venn diagrams, differential expression, functional enrichment, and protein-protein interaction analysis, were then conducted to identify six ferroptosis-related DEGs, two oxidative stress-related DEGs, eleven immunity-related DEGs, five ECM-related DEGs, among which AKR1C2/AKR1C3/SIRT1/ALB/IL-24 belong to inflammatory genes. Furthermore, 67 DEMs were predicted to be upstream miRNAs of 25 key DEGs by merging RNA-seq, TargetScan, and mirDIP databases. Finally, a miRNA-gene network was constructed using Cytoscape software and an alluvial plot. ROC curve analysis unveiled multiple key DEGs with the high clinical diagnostic value, providing novel approaches for diagnosing and treating PMD diseases.

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1\'s pivotal role validated. FDX1\'s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1\'s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Multiple sclerosis(MS), primary Sjögren syndrome (pSS), and systemic lupus erythematosus (SLE) share numerous clinical symptoms and serological characteristics. We analyzed 153550 cells of scRNA-seq data of 17 treatment-naive patients (5 MS, 5 pSS, and 7 SLE) and 10 healthy controls, and we examined the enrichment of biological processes, differentially expressed genes (DEGs), immune cell types, and their subpopulations, and cell-cell communication in peripheral blood mononuclear cells (PBMCs). The percentage of B cells, megakaryocytes, monocytes, and proliferating T cells presented significant changes in autoimmune diseases. The enrichment of cell types based on gene expression revealed an elevated monocyte. MIF, MK, and GALECTIN signaling networks were obvious differences in autoimmune diseases. Taken together, our analysis provides a comprehensive map of the cell types and states of ADs patients at the single-cell level to understand better the pathogenesis and treatment of these ADs.

Adenocarcinomas of the nasal/paranasal sinuses are uncommon, but intestinal-type adenocarcinomas (ITACs) are important. Due to the rarity of these tumors, their molecular profile is not well known. To further investigate the molecular profile and find potential oncogenic drivers, we compared the whole transcriptome and exome of ITACs at different anatomic locations in the head and neck. Twenty-one head and neck adenocarcinomas were used in this study, divided into 10 sinonasal adenocarcinomas (SNT) and 11 extrasinonasal (T) head and neck adenocarcinomas according to anatomic location and histology. Tumor samples along with normal mucosa were microdissected from formalin-fixed, paraffin-embedded samples, and RNA and DNA were subjected to whole-transcriptome and -exome shotgun sequencing. Analysis of ITACs at sinonasal locations showed 410 subtype-specific differentially expressed (DE) genes and noncoding transcripts compared with the group of other anatomic locations, with 2909 subtype-specific DE genes. The groups shared 872 genes, with 17 highly different or opposing DE genes. Whole-exome mutation analysis revealed the gene MLL3 (KMT2C) to be exhibiting the most frequent loss-of-function mutations in all adenocarcinomas investigated. The results suggest that the head and neck ITACs investigated were mainly caused by loss-of-function mutations in MLL3 that disabled chromatin methylation and remodeling of all MLL3-targeted enhancers in the tumors. This changed the activity of multiple genes/gene clusters, supporting oncogenicity mostly via pathways of signaling, dedifferentiation, proliferation, migration, and immune and inflammatory deregulation, indicating a truly epigenetic event as the root cause for the heterogenous diversity of these enteric types of cancer. The data of this study form the basis for understanding cell fate determination and cellular homeostasis in the normal respiratory mucosa at different anatomic sites and show the contribution of different mucosal components to the etiology/molecular pathology of ITAC.

There is a heterogenous clinical response following chemoradiotherapy (CRT) in esophageal squamous cell carcinoma (ESCC). Therefore, we aimed to study signaling pathway genes that affect CRT sensitivity and prognosis.
Gene expression analyses were performed in the GEO and TCGA datasets. A immunohistochemistry (IHC) analysis was performed in pretreatment biopsies.
MMP13 was found to be highly expressed in the \"Pathologic Complete Response (pCR)\" and \"Complete Remission (CR)\" and \"Alive\" groups. Th17 cells and MMP9/13 showed a negative correlation in immune infiltration analysis. In GSEA analysis, IL-4 and IL-13 signaling pathways were highly enriched in patients exhibiting high MMP expression in pCR and CR groups. IHC results suggested higher MMP13 & IL-4 and lower IL-17A & RORC expression in the CR group compared to the 0.70, and the model could well distinguish high-risk and low-risk subgroups.
The above results may provide guidance for developing novel treatment and prognostic strategies in ESCC patients.

Gastrodia elata needs to establish a symbiotic relationship with Armillaria strains to obtain nutrients and energy. However, the signaling cross talk between G. elata and Armillaria strains is still unclear. During our experiment, we found that the vegetative mycelium of Armillaria gallica 012m grew significantly better in the media containing gibberellic acid (GA3) than the blank control group (BK). To explore the response mechanism, we performed an RNA-sequencing experiment to profile the transcriptome changes of A. gallica 012m cultured in the medium with exogenous GA3. The transcriptome-guided differential expression genes (DEGs) analysis of GA3 and BK showed that a total of 1309 genes were differentially expressed, including 361 upregulated genes and 948 downregulated genes. Some of those DEGs correlated with the biological process, including positive regulation of chromosome segregation, mitotic metaphase/anaphase transition, attachment of mitotic spindle microtubules to kinetochore, mitotic cytokinesis, and nuclear division. These analyses explained that GA3 actively promoted the growth of A. gallica to some extent. Further analysis of protein domain features showed that the deduced polypeptide contained 41 candidate genes of GA receptor, and 27 of them were expressed in our samples. We speculate that GA receptors exist in A. gallica 012m. Comparative studies of proteins showed that the postulated GA receptor domains of A. gallica 012m have a higher homologous correlation with fungi than others based on cluster analysis.

In Chinese cabbage breeding, hybrids have made a terrific contribution due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new, top-performing hybrids requires a large scale of human and material resources, the prediction of hybrid performance is of utmost interest to plant breeders. In our research, leaf transcriptome data from eight parents were used to investigate if they might be employed as markers to predict hybrid performance and heterosis. In Chinese cabbage, heterosis of plant growth weight (PGW) and heterosis of head weight (HW) were more obvious than other traits. The number of differential expression genes (DEGs) between parents was related to the PGW, length of the biggest outer leaf (LOL), leaf head height (LHH), leaf head width (LHW), HW, leaf number of head (LNH) and plant height (PH) of hybrids, and up-regulated DEGs number was also associated with these traits. Euclidean and binary distances of parental gene expression levels were significantly correlated with the PGW, LOL, LHH, LHW, HW and PH of hybrids. Additionally, there was a significant correlation between the parental expression levels of multiple genes involved in the ribosomal metabolic pathway and hybrid observations and heterosis in PGW, with the BrRPL23A gene showing the highest correlation with the MPH of PGW(r = 0.75). Therefore, leaf transcriptome data can preliminarily predict the hybrid performance and select parents in Chinese cabbage.