Transcriptome Landscape of Human Folliculogenesis Reveals Oocyte and Granulosa Cell Interactions.

The dynamic transcriptional regulation and interactions of human germlines and surrounding somatic cells during folliculogenesis remain unknown. Using RNA sequencing (RNA-seq) analysis of human oocytes and corresponding granulosa cells (GCs) spanning five follicular stages, we revealed unique features in transcriptional machinery, transcription factor networks, and reciprocal interactions in human oocytes and GCs that displayed developmental-stage-specific expression patterns. Notably, we identified specific gene signatures of two cell types in particular developmental stage that may reflect developmental competency and ovarian reserve. Additionally, we uncovered key pathways that may concert germline-somatic interactions and drive the transition of primordial-to-primary follicle, which represents follicle activation. Thus, our work provides key insights into the crucial features of the transcriptional regulation in the stepwise folliculogenesis and offers important clues for improving follicle recruitment in vivo and restoring fully competent oocytes in vitro.

Identification of immune-related gene signatures for chronic obstructive pulmonary disease with metabolic syndrome: evidence from integrated bulk and single-cell RNA sequencing data.

Chronic obstructive pulmonary disease (COPD) is closely related to innate and adaptive inflammatory immune responses. It is increasingly becoming evident that metabolic syndrome (MetS) affects a significant portion of COPD patients. Through this investigation, we identify shared immune-related candidate biological markers. The Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to reveal the co-expression modules linked to COPD and MetS. The commonly expressed genes in the COPD and MetS were utilized to conduct an enrichment analysis. We adopted machine-learning to screen and validate hub genes. We also assessed the relationship between hub genes and immune cell infiltration in COPD and MetS, respectively. Moreover, associations across hub genes and metabolic pathways were also explored. Finally, we chose a single-cell RNA sequencing (scRNA-seq) dataset to investigate the hub genes and shared mechanisms at the level of the cells. We also applied cell trajectory analysis and cell-cell communication analysis to focus on the vital immune cell we were interested in. As a result, we selected and validated 13 shared hub genes for COPD and MetS. The enrichment analysis and immune infiltration analysis illustrated strong associations between hub genes and immunology. Additionally, we applied metabolic pathway enrichment analysis, indicating the significant role of reactive oxygen species (ROS) in COPD with MetS. Through scRNA-seq analysis, we found that ROS might accumulate the most in the alveolar macrophages. In conclusion, the 13 hub genes related to the immune response and metabolism may serve as diagnostic biomarkers and treatment targets of COPD with MetS.

Identification and validation of genes associated with aging-related cardiovascular disease.

Aging is acknowledged as the most significant risk factor for cardiovascular disease (CVD). This study sought to identify and validate potential aging-related genes associated with CVD by using bioinformatics. The confluence of the limma test, weighted correlation network analysis (WGCNA), and 2129 aging and senescence-associated genes led to the identification of aging-related differential expression genes (ARDEGs). By using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), potential biological roles and pathways of ARDEGs were identified. To find the significantly different functions between CVD and non-cardiovascular disease (nCVD) and to reckon the processes score, enrichment analysis of all genes was carried out using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). By using GO and KEGG, potential biological roles and pathways of ARDEGs were identified. To evaluate the immune cell composition of the immune microenvironment, we performed an immune infiltration analysis on the dataset from the training group. We were able to acquire four ARDEGs (PTGS2, MMP9, HBEGF, and FN1). Aging, cellular senescence, and nitric oxide signal transduction were selected for biological function analysis. The diagnostic value of the four ARDEGs in distinguishing CVD from nCVD samples was deemed to be favorable. This research identified four ARDEGs that are associated with CVD. This study provides insight into prospective novel biomarkers for aging-related CVD diagnosis and progression monitoring.

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. METHODS: We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. RESULTS: We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. CONCLUSION: Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Identification of STAT3 as a biomarker for cellular senescence in liver fibrosis: A bioinformatics and experimental validation study.

BACKGROUND: Cellular senescence is associated with a dysregulated inflammatory response, which is an important driver of the development of liver fibrosis (LF). This study aimed to investigate the effect of cellular senescence on LF and identify potential key biomarkers through bioinformatics analysis combined with validation experiments in vivo and in vitro. METHODS: The Gene Expression Omnibus (GEO) database and GeneCards database were used to download the LF dataset and the aging-related gene set, respectively. Functional enrichment analysis of differential genes was then performed using GO and KEGG. Hub genes were further screened using Cytoscape's cytoHubba. Diagnostic values for hub genes were evaluated with a receiver operating characteristic (ROC) curve. Next, CIBERSORTx was used to estimate immune cell types and ratios. Finally, in vivo and in vitro experiments validated the results of the bioinformatics analysis. Moreover, molecular docking was used to simulate drug-gene interactions. RESULTS: A total of 44 aging-related differentially expressed genes (AgDEGs) were identified, and enrichment analysis showed that these genes were mainly enriched in inflammatory and immune responses. PPI network analysis identified 6 hub AgDEGs (STAT3, TNF, MMP9, CD44, TGFB1, and TIMP1), and ROC analysis showed that they all have good diagnostic value. Immune infiltration suggested that hub AgDEGs were significantly associated with M1 macrophages or other immune cells. Notably, STAT3 was positively correlated with α-SMA, COL1A1, IL-6 and IL-1ß, and was mainly expressed in hepatocytes (HCs). Validation experiments showed that STAT3 expression was upregulated and cellular senescence was increased in LF mice. A co-culture system of HCs and hepatic stellate cells (HSCs) further revealed that inhibiting STAT3 reduced HCs senescence and suppressed HSCs activation. In addition, molecular docking revealed that STAT3 was a potential drug therapy target. CONCLUSIONS: STAT3 may be involved in HCs senescence and promote HSCs activation, which in turn leads to the development of LF. Our findings suggest that STAT3 could be a potential biomarker for LF.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses.

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Substrate selectivity and unique sequence signatures in SWEET/semiSWEET homologs of four taxonomic groups: Sequence analysis and phylogenetic studies.

The recently discovered SWEET (Sugar Will Eventually be Exported Transporter) proteins are involved in the selective transport of monosaccharides and disaccharides. The prokaryotic counterparts, semiSWEETs, form dimers with each monomer forming a triple-helix transmembrane bundle (THB). The longer eukaryotic SWEETs have seven transmembrane helices with two THBs and a linker helix. Structures of semiSWEETs/SWEETs have been determined experimentally. Experimental studies revealed the role of plant SWEETs in vital physiological processes and identified residues responsible for substrate selectivity. However, SWEETs/semiSWEETs from metazoans and bacteria are not characterized. In this study, we used structure-based sequence alignment and compared more than 2000 SWEET/semiSWEETs from four different taxonomic groups. Conservation of residue/chemical property was examined at all positions. Properties of clades/subclades of phylogenetic trees from each taxonomic group were analyzed. Conservation pattern of known residues in the selectivity-filter was used to predict the substrate preference of plant SWEETs and some clusters of metazoans and bacteria. Some residues at the gating and substrate-binding regions, pore-facing positions and at the helix-helix interface are conserved across all taxonomic groups. Conservation of polar/charged residues at specific pore-facing positions, helix-helix interface and in loops seems to be unique for plant SWEETs. Overall, the number of conserved residues is less in metazoan SWEETs. Plant and metazoan SWEETs exhibit high conservation of four and three proline residues respectively in "proline tetrad." Further experimental studies can validate the predicted substrate selectivity and significance of conserved polar/charged/aromatic residues at structurally and functionally important positions of SWEETs/semiSWEETs in plants, metazoans and bacteria.

Screening and functional analysis of StMYB transcription factors in pigmented potato under low-temperature treatment.

MYB transcription factors play an extremely important regulatory role in plant responses to stress and anthocyanin synthesis. Cloning of potato StMYB-related genes can provide a theoretical basis for the genetic improvement of pigmented potatoes. In this study, two MYB transcription factors, StMYB113 and StMYB308, possibly related to anthocyanin synthesis, were screened under low-temperature conditions based on the low-temperature-responsive potato StMYB genes family analysis obtained by transcriptome sequencing. By analyzed the protein properties and promoters of StMYB113 and StMYB308 and their relative expression levels at different low-temperature treatment periods, it is speculated that StMYB113 and StMYB308 can be expressed in response to low temperature and can promote anthocyanin synthesis. The overexpression vectors of StMYB113 and StMYB308 were constructed for transient transformation tobacco. Color changes were observed, and the expression levels of the structural genes of tobacco anthocyanin synthesis were determined. The results showed that StMYB113 lacking the complete MYB domain could not promote the accumulation of tobacco anthocyanins, while StMYB308 could significantly promote the accumulation involved in tobacco anthocyanins. This study provides a theoretical reference for further study of the mechanism of StMYB113 and StMYB308 transcription factors in potato anthocyanin synthesis.

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration.

BACKGROUND: Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation. METHODS: The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes. RESULTS: A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes. CONCLUSION: The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

Network-Based Transcriptome Analysis Reveals FAM3C as a Novel Potential Biomarker for Glioblastoma.

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a high mortality rate. The aim of the present study was to investigate the clinical significance of Family with Sequence Similarity 3, Member C, FAM3C, in GBM using bioinformatic-integrated analysis. First, we performed the transcriptomic integration analysis to assess the expression profile of FAM3C in GBM using several data sets (RNA-sequencing and scRNA-sequencing), which were obtained from TCGA and GEO databases. By using the STRING platform, we investigated FAM3C-coregulated genes to construct the protein-protein interaction network. Next, Metascape, Enrichr, and CIBERSORT databases were used. We found FAM3C high expression in GBM with poor survival rates. Further, we observed, via FAM3C coexpression network analysis, that FAM3C plays key roles in several hallmarks of cancer. Surprisingly, we also highlighted five FAM3C­coregulated genes overexpressed in GBM. Specifically, we demonstrated the association between the high expression of FAM3C and the abundance of the different immune cells, which may markedly worsen GBM prognosis. For the first time, our findings suggest that FAM3C not only can be a new emerging biomarker with promising therapeutic values to GBM patients but also gave a new insight into a potential resource for future GBM studies.

Comprehensive Analysis of Predictive Value and the potential therapeutic target of NLRP3 inflammasome in glioma based on tumor microenvironment.

BACKGROUND: Glioma exhibits high recurrence rates and poor prognosis. The nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome plays a crucial role in inflammation. There is a lack of research exploring the NLRP3 in glioma. METHODS: We used several databases, networks, Western blotting, multiple immunofluorescence staining to analyze the role of NLRP3 in inflammatory tumor microenvironment (TME). RESULTS: NLRP3 is higher-expression in glioma with a low mutation load. NLRP3 expression is linked to the infiltration of immune cells, chemokines, immunomodulators, and the TME. Signaling pathways, co-expression genes and interacting proteins contribute to the up-regulation of NLRP3. Patients responding to immunotherapy positively tend to have lower NLRP3 expression relating to the overall survival based on nomogram. Sensitivity to molecular medicines is observed in relation to NLRP3. CONCLUSION: The NLRP3 inflammasome plays a pivotal role in TME which could serve as a higher predictive value biomarker and therapeutic target for glioma treatment.

Interferon-α regulates abnormally increased expression of RSAD2 in Th17 and Tfh cells in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that often involves abnormal activation of regulatory IFN genes and regulation of B cells by CD4+ T cells. Radical S-adenosyl methionine domain containing 2 (RSAD2) is a viral suppressor protein regulated by type I IFN, and it has been proven to play an important regulatory role in SLE. However, the mechanism by which RSAD2 participates in the pathogenesis of SLE is unclear. In this study, we observed higher expression levels of RSAD2 in CD4+ T-cell subsets from the peripheral blood of SLE patients than in those from healthy controls by bioinformatics analysis and validation experiments. We analyzed the expression of RSAD2 in CD4+ T cells of patients with SLE and other autoimmune diseases. In addition, we found that the expression of RSAD2 in CD4+ T cells might be regulated by IFN-α, and RSAD2 significantly affected the differentiation of Th17 cells and T follicular helper (Tfh) cells. Our findings underlined that RSAD2 may promote B-cell activation by promoting the differentiation of Th17 and Tfh cells in SLE patients, a process that is regulated by IFN-α.

Integrative analysis of cuproptosis-associated genes for predicting immunotherapy response in single-cell and multi-cohort studies.

BACKGROUND: The role of genes associated with the cuproptosis cell signaling pathway in prognosis and immunotherapy in ovarian cancer (OC) has been extensively investigated. In this study, we aimed to explore these mechanisms and establish a prognostic model for patients with OC using bioinformatics techniques. METHODS: We obtained the single cell sequencing data of ovarian cancer from the Gene Expression Omnibus (GEO) database and preprocessed the data. We analyzed a variety of factors including cuproptosis cell signal score, transcription factors, tumorigenesis and progression signals, gene set variation analysis (GSVA) and intercellular communication. Differential gene analysis was performed between groups with high and low cuproptosis cell signal scores, as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Using bulk RNA sequencing data from The Cancer Genome Atlas, we used the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to develop cuproptosis cell signaling pathword-related gene signatures and validated them with GEO ovarian cancer datasets. In addition, we analyzed the inherent rules of the genes involved in building the model using a variety of bioinformatics methods, including immune-related analyses and single nucleotide polymorphisms. Molecular docking is used to screen potential therapeutic drugs. To confirm the analysis results, we performed various wet experiments such as western blot, cell counting kit 8 (CCK8) and clonogenesis tests to verify the role of the Von Willebrand Factor (VWF) gene in two ovarian cancer cell lines. RESULTS: Based on single-cell data analysis, we found that endothelial cells and fibroblasts showed active substance synthesis and signaling pathway activation in OC, which further promoted immune cell suppression, cancer cell proliferation and metastasis. Ovarian cancer has a high tendency to metastasize, and cancer cells cooperate with other cells to promote disease progression. We developed a signature consisting of eight cuproptosis-related genes (CRGs) (MAGEF1, DNPH1, RARRES1, NBL1, IFI27, VWF, OLFML3 and IGFBP4) that predicted overall survival in patients with ovarian cancer. The validity of this model is verified in an external GEO validation set. We observed active infiltrating states of immune cells in both the high- and low-risk groups, although the specific cells, genes and pathways of activation differed. Gene mutation analysis revealed that TP53 is the most frequently mutated gene in ovarian cancer. We also predict small molecule drugs associated with CRGs and identify several potential candidates. VWF was identified as an oncogene in ovarian cancer, and the protein was expressed at significantly higher levels in tumor samples than in normal samples. The high-score model of the cuproptosis cell signaling pathway was associated with the sensitivity of OC patients to immunotherapy. CONCLUSIONS: Our study provides greater insight into the mechanisms of action of genes associated with the cuproptosis cell signaling pathway in ovarian cancer, highlighting potential targets for future therapeutic interventions.

Bioinformatics analysis and experimental validation reveal the anti-ferroptosis effect of FZD7 in acute kidney injury.

BACKGROUND: Ferroptosis plays an important role in acute kidney injury (AKI), but the specific regulatory mechanism of ferroptosis in AKI remains unclear. This study is expected to analyze ferroptosis-related genes (FRGs) in AKI and explore their underlying mechanisms. RESULTS: A total of 479 differentially expressed genes (DEGs), including 196 up-regulated genes and 283 down-regulated genes were identified in the AKI chip GSE30718. 341 FRGs were obtained from the Genecard, OMIM and NCBI database. Totally 11 ferroptosis-related DEGs in AKI were found, in which 7 genes (CD44, TIGAR, RB1, LCN2, JUN, ARNTL, ACSL4) were up-regulated and 4 genes (FZD7, EP300, FOXC1, DLST) were down-regulated. Three core genes (FZD7, JUN, EP300) were obtained by PPI and KEGG analysis, among which the function of FZD7 in AKI is unclear. The WGCNA analysis found that FZD7 belongs to a module that was negatively correlated with AKI. Further basic experiments confirmed that FZD7 is down-regulated in mouse model of ischemia-reperfusion-AKI and cellular model of hypoxia-reoxygenation(H/R). In addition, knockdown of FZD7 could further aggravate the down-regulation of cell viability induced by H/R and Erastin, while overexpression of FZD7 can rescue its down-regulation to some extent. Furthermore, we verified that knockdown of FZD7 decreased the expression of GPX4 and overexpression of FZD7 increased the expression of GPX4, suggesting that FZD7 may inhibit ferroptosis by regulating the expression of GPX4 and plays a vital role in the onset and development of AKI. CONCLUSIONS: This article revealed the anti-ferroptosis effect of FZD7 in acute kidney injury through bioinformatics analysis and experimental validation, suggesting that FZD7 is a promising target for AKI and provided more evidence about the vital role of ferroptosis in AKI.

Genome-wide identification and mining elite allele variation of the Monoacylglycerol lipase (MAGL) gene family in upland cotton (Gossypium hirsutum L.).

BACKGROUND: Monoacylglycerol lipase (MAGL) genes belong to the alpha/beta hydrolase superfamily, catalyze the terminal step of triglyceride (TAG) hydrolysis, converting monoacylglycerol (MAG) into free fatty acids and glycerol. RESULTS: In this study, 30 MAGL genes in upland cotton have been identified, which have been classified into eight subgroups. The duplication of GhMAGL genes in upland cotton was predominantly influenced by segmental duplication events, as revealed through synteny analysis. Furthermore, all GhMAGL genes were found to contain light-responsive elements. Through comprehensive association and haplotype analyses using resequencing data from 355 cotton accessions, GhMAGL3 and GhMAGL6 were detected as key genes related to lipid hydrolysis processes, suggesting a negative regulatory effect. CONCLUSIONS: In summary, MAGL has never been studied in upland cotton previously. This study provides the genetic mechanism foundation for the discover of new genes involved in lipid metabolism to improve cottonseed oil content, which will provide a strategic avenue for marker-assisted breeding aimed at incorporating desirable traits into cultivated cotton varieties.

Genome-wide identification and stress response analysis of BcaCPK gene family in amphidiploid Brassica carinata.

BACKGROUND: Calcium-dependent protein kinases (CPKs) are crucial for recognizing and transmitting Ca2+ signals in plant cells, playing a vital role in growth, development, and stress response. This study aimed to identify and detect the potential roles of the CPK gene family in the amphidiploid Brassica carinata (BBCC, 2n = 34) using bioinformatics methods. RESULTS: Based on the published genomic information of B. carinata, a total of 123 CPK genes were identified, comprising 70 CPK genes on the B subgenome and 53 on the C subgenome. To further investigate the homologous evolutionary relationship between B. carinata and other plants, the phylogenetic tree was constructed using CPKs in B. carinata and Arabidopsis thaliana. The phylogenetic analysis classified 123 family members into four subfamilies, where gene members within the same subfamily exhibited similar conserved motifs. Each BcaCPK member possesses a core protein kinase domain and four EF-hand domains. Most of the BcaCPK genes contain 5 to 8 introns, and these 123 BcaCPK genes are unevenly distributed across 17 chromosomes. Among these BcaCPK genes, 120 replicated gene pairs were found, whereas only 8 genes were tandem duplication, suggesting that dispersed duplication mainly drove the family amplification. The results of the Ka/Ks analysis indicated that the CPK gene family of B. carinata was primarily underwent purification selection in evolutionary selection. The promoter region of most BcaCPK genes contained various stress-related cis-acting elements. qRT-PCR analysis of 12 selected CPK genes conducted under cadmium and salt stress at various points revealed distinct expression patterns among different family members in response to different stresses. Specifically, the expression levels of BcaCPK2.B01a, BcaCPK16.B02b, and BcaCPK26.B02 were down-regulated under both stresses, whereas the expression levels of other members were significantly up-regulated under at least one stress. CONCLUSION: This study systematically identified the BcaCPK gene family in B. carinata, which contributes to a better understanding the CPK genes in this species. The findings also serve as a reference for analyzing stress responses, particularly in relation to cadmium and salt stress in B. carinata.

A correlation study of adhesion G protein-coupled receptors as potential therapeutic targets for breast cancer.

BACKGROUND: Adhesion G protein-coupled receptors (aGPCRs), a distinctive subset of the G protein-coupled receptor (GPCR) superfamily, play crucial roles in various physiological and pathological processes, with implications in tumor development. Despite the global prevalence of breast cancer (BRCA), specific aGPCRs as potential drug targets or biomarkers remain underexplored. METHODS: UALCAN, GEPIA, Kaplan-Meier Plotter, MethSurv, cBiopportal, String, GeneMANIA, DAVID, Timer, Metascape, and qPCR were applied in this work. RESULTS: Our analysis revealed significantly increased transcriptional levels of ADGRB2, ADGRC1, ADGRC2, ADGRC3, ADGRE1, ADGRF2, ADGRF4, and ADGRL1 in BRCA primary tumors. Further analysis indicated a significant correlation between the expressions of certain aGPCRs and the pathological stage of BRCA. High expression of ADGRA1, ADGRF2, ADGRF4, ADGRG1, ADGRG2, ADGRG4, ADGRG6, and ADGRG7 was significantly correlated with poor overall survival (OS) in BRCA patients. Additionally, high expression of ADGRF2 and ADGRF4 indicated inferior recurrence-free survival (RFS) in BRCA patients. The RT-qPCR experiments also confirmed that the mRNA levels of ADGRF2 and ADGRF4 were higher in BRCA cells and tissues. Functional analysis highlighted the diverse roles of aGPCRs, encompassing GPCR signaling and metabolic energy reserves. Moreover, aGPCRs may exert influence or actively participate in the development of BRCA through their impact on immune status. CONCLUSION: aGPCRs, particularly ADGRF2 and ADGRF4, hold promise as immunotherapeutic targets and prognostic biomarkers in BRCA.

Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage.

Unexplained recurrent miscarriage (URM) is a common pregnancy complication with few effective therapies. Moreover, little is known regarding the role of pyroptosis in the regulation of the URM immune microenvironment. To address this issue, gene expression profiles of publicly available placental datasets GSE22490 and GSE76862 were downloaded from the Gene Expression Omnibus database. Pyroptosis-related differentially expressed genes were identified and a total of 16 differentially expressed genes associated with pyroptosis were detected, among which 1 was upregulated and 15 were downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the functionally enriched modules and pathways of these genes are closely related to immune and inflammatory responses. Four hub genes were identified: BTK, TLR8, NLRC4, and TNFSF13B. BTK, TLR8, and TNFSF13B were highly connected with immune cells, according to the correlation analysis of four hub genes and 20 different types of immune cells (p < 0.05). The four hub genes were used as research objects to construct the interaction networks. Chorionic villus tissue was used for quantitative real-time polymerase chain reaction and western blot to confirm the expression levels of hub genes, and the results showed that the expression of the four hub genes was significantly decreased in the chorionic villus tissue in the URM group. Collectively, the present study indicates that perhaps pyroptosis is essential to the diversity and complexity of the URM immune microenvironment, and provides a theoretical basis and research ideas for subsequent target gene verification and mechanism research.

Systematic Analysis of the MIO-forming Residues of Aromatic Ammonia Lyases.

Aromatic ammonia lyases (AALs) and tyrosine/phenylalanine ammonia mutases (TAM/PAM) are 3,5-dihydro-5-methylidene-4H-imidazol-4-one (MIO)-dependent enzymes. Usually, the MIO moiety is autocatalytically formed from the tripeptide Ala-Ser-Gly (ASG) and acts as an electrophile during the enzymatic reaction. However, the MIO-forming residues (ASG) have some diversity in this enzyme class. In this work, a systematic investigation on the variety of MIO-forming residues was carried out using in-depth sequence analyses. Several protein clusters of AAL-like enzymes with unusual MIO-forming residues such as ACG, TSG, SSG, and CSG were identified, including two novel histidine ammonia lyases and one PAM with CSG and TSG residues, respectively, as well as three novel ergothioneine trimethylammonia lyases without MIO motif. The mutagenesis of common MIO-groups confirmed the function of these MIO variants, which provides good starting points for future functional prediction and mutagenesis research of AALs.

NKAIN1, as an oncogene, promotes the proliferation and metastasis of breast cancer, affecting its prognosis.

Na, K-ATPase interaction (NKAIN) is a transmembrane protein family, which can interact with Na, K-ATPase ß1 subunit. NKAIN1 plays an important role in alcohol-dependent diseases such as endometrial and prostate cancers. However, the relationship between NKAIN1 and human breast cancer has not been studied. Hence, this study aimed to explore the relationship between NKAIN1 expression and breast cancer. Data used in this study were mainly from the Cancer Genome Atlas, including differential expression analysis, Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, multiple Cox regression analysis, co-expression gene analysis, and gene set enrichment analysis. Analyses were performed using reverse transcription-quantitative polymerase chain reaction, western blot analysis, and immunohistochemistry on 46 collected samples. The knockdown or overexpression of NKAIN1 in vitro in MCF-7 and MDA-MB-231 cell lines altered the proliferation and migration abilities of tumor cells. In vivo experiments further confirmed that NKAIN1 knockdown effectively inhibited the proliferation and migration of cancer cells. Therefore, our study identified NKAIN1 as an oncogene that is highly expressed in breast cancer tissues. The findings highlight the potential of NKAIN1 as a molecular biomarker of breast cancer.