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Aging is a complex trait characterized by a diverse spectrum of endophenotypes. By utilizing the SomaScan® proteomic platform in 1,025 participants of the LonGenity cohort (age range: 65–95, 55.7% females), we found that 754 of 4,265 proteins were associated with chronological age. Pleiotrophin (PTN; β[SE] = 0.0262 [0.0012]; p = 3.21 × 10−86), WNT1‐inducible‐signaling pathway protein 2 (WISP‐2; β[SE] = 0.0189 [0.0009]; p = 4.60 × 10−82), chordin‐like protein 1 (CRDL1; β[SE] = 0.0203[0.0010]; p = 1.45 × 10−77), transgelin (TAGL; β[SE] = 0.0215 [0.0011]; p = 9.70 × 10−71), and R‐spondin‐1(RSPO1; β[SE] = 0.0208 [0.0011]; p = 1.09 × 10−70), were the proteins most significantly associated with age. Weighted gene co‐expression network analysis identified two of nine modules (clusters of highly correlated proteins) to be significantly associated with chronological age and demonstrated that the biology of aging overlapped with complex age‐associated diseases and other age‐related traits. The correlation between proteomic age prediction based on elastic net regression and chronological age was 0.8 (p < 2.2E−16). Pathway analysis showed that inflammatory response, organismal injury and abnormalities, cell and organismal survival, and death pathways were associated with aging. The present study made novel associations between a number of proteins and aging, constructed a proteomic age model that predicted mortality, and suggested possible proteomic signatures possessed by a cohort enriched for familial exceptional longevity. Age‐associated proteomic changes studied using SomaScan assay showed a plethora of novel proteins and pathways to be associated with aging in older adults. Predicted age using elastic net regression captured mortality better than actual chronological age. Clusters of highly correlated proteins associated with chronological age were also associated with diverse phenotypes and traits giving clues for shared biology.

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8+ T cells while CDCA8 with CD4+ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Abscission is a process in which plants shed their parts, and is mediated by a particular set of cells, the abscission zone (AZ). In grasses (Poaceae), the position of the AZ differs among species, raising the question of whether its anatomical structure and genetic control are conserved. The ancestral position of the AZ was reconstructed. A combination of light microscopy, transmission electron microscopy, RNA-Seq analyses and RNA in situ hybridisation were used to compare three species, two (weedy rice and Brachypodium distachyon) with the AZ in the ancestral position and one (Setaria viridis) with the AZ in a derived position below a cluster of flowers (spikelet). Rice and Brachypodium are more similar anatomically than Setaria. However, the cell wall properties and the transcriptome of rice and Brachypodium are no more similar to each other than either is to Setaria. The set of genes expressed in the studied tissues is generally conserved across species, but the precise developmental and positional patterns of expression and gene networks are almost entirely different. Transcriptional regulation of AZ development appears to be extensively rewired among the three species, leading to distinct anatomical and morphological outcomes.

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl-CoA Acetyltransferase 1, death domain-containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

Botrytis cinerea (Bc) is a major pathogen of cultivated grapevine (Vitis vinifera L.), with cell wall (CW) remodelling playing a critical role in fungal colonisation. CW‐modifying enzymes, particularly pectin methylesterases (PMEs), produced by both host and pathogen, influence CW integrity and the outcome of infection. To explore the role of CW composition and remodelling in grapevine's response to Bc, we inoculated three genotypes with varying susceptibility at full flowering. Biochemical analysis of flowers and ripe berry skins revealed that the tolerant genotype exhibited significantly higher PME activity postinfection compared with the susceptible ones. Unbiased transcriptome analysis of infected flower tissues showed a more intense transcriptional response in the susceptible genotype, suggesting an ultimately ineffective attempt to restrict fungus spread. Expression profiling of 62 PME genes in this data set and public Bc‐infected berry transcriptomes identified PME10 as the most strongly induced gene upon infection. PME10 knockout mutants displayed reduced PME activity and heightened susceptibility, while overexpression lines showed enhanced PME activity and reduced disease symptoms. Gene co‐expression network analysis highlighted WRKY03, a defence‐related transcription factor, as a putative regulator of PME10. DAP‐seq, DAP‐qPCR and dual luciferase assays confirmed direct binding and activation of the PME10 promoter by WRKY03. Altogether, this study demonstrates that PME10 is a functional PME contributing to grapevine immunity against B. cinerea, establishing it as a key component of the grapevine defence machinery against fungal pathogens.

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co‐expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set (GSE101702); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.

Diabetic kidney disease (DKD) represents a major complication associated with diabetes mellitus, notably contributing to patient morbidity and mortality. However, early diagnosis of DKD remains challenging due to the lack of clear diagnostic biomarkers. Therefore, in the present study, microarray and RNA-sequencing data from the Gene Expression Omnibus database were systematically analyzed. Using differential expression and weighted gene co-expression network analysis, 49 genes with marked expression changes in DKD were identified. Subsequent analyses, including functional enrichment, protein-protein interaction network construction, machine learning techniques and assessment of immune cell infiltration, led to the identification of three hub genes: Spleen-associated tyrosine kinase, apoptotic peptidase activating factor 1 and ADAM metallopeptidase domain 10, as promising diagnostic markers, which were further evaluated by receiver operating characteristic curve analysis. Expression changes of the identified hub genes were validated in both DKD mouse models and clinical patient samples. Collectively, the present study provided a novel perspective on the molecular basis of DKD, and highlighted novel candidates for potential diagnostic and therapeutic applications.

Background Hanwoo cattle are a Korean breed renowned for their cultural significance and high-quality beef, characterized by low cholesterol and a high unsaturated fat ratio. Their growth is divided into a growing stage focused on development and a fattening stage for marbling. Proper feed management, considering genetic and environmental factors, is vital for maximizing growth potential. The rumen plays a crucial role in digestion and gene expression regulation, with rumen fermentation being central to nutrient absorption and cattle health. In this study, we conduct a transcriptome analysis of the rumen at eight timepoints. Our goal is to identify genetic factors that influence the growth of Hanwoo steers to enhance our understanding of the rumen’s functions during Hanwoo growth. Results In the RNA-sequencing analysis of Hanwoo steer rumen, differential gene expression was examined over eight timepoints, highlighting significant genetic changes, particularly between 12 and 26 months. The results of a weighted gene co-expression network analysis were identified and organized into three modules: turquoise, blue, and yellow. The turquoise module, linked to immune response, showed significantly down-regulation in genes at 30 months. The blue module, associated with steroid metabolism, was notably up-regulated at 26 months. The yellow module’s genes showed a consistent increase in expression with growth. These modules and their functional annotations provide a deeper understanding of the biological processes during Hanwoo growth, highlighting the intricate relationship between gene expression and cattle development. Conclusions The growth stages of Hanwoo steers were explored in our investigation utilizing rumen transcriptome data. The rumen plays a critical role in their development, particularly during the growing and fattening stages. Proper feed management, considering the rumen’s function, is essential for optimal growth. Transcriptome analysis helps identify genes associated with growth and provides insights for cattle breeding and management practices. Understanding the complex connection between gene expression and Hanwoo development is essential for maximizing productivity and health.

Gene co‐expression network analysis is an efficient systems biology approach for the discovery of novel gene functions and trait‐associated gene modules. To identify clusters of functionally related genes involved in soybean nodule formation and development, we performed a weighted gene co‐expression network analysis. Two nodule‐specific modules (NSM‐1 and NSM‐2, containing 304 and 203 genes, respectively) were identified. The NSM‐1 gene promoters were significantly enriched in cis‐binding elements for ERF, MYB, and C2H2‐type zinc transcription factors, whereas NSM‐2 gene promoters were enriched in cis‐binding elements for TCP, bZIP, and bHLH transcription factors, suggesting a role of these regulatory factors in the transcriptional activation of nodule co‐expressed genes. The co‐expressed gene modules included genes with potential novel roles in nodulation, including those involved in xylem development, transmembrane transport, the ethylene signalling pathway, cytoskeleton organization, cytokinesis and regulation of the cell cycle, regulation of meristem initiation and growth, transcriptional regulation, DNA methylation, and histone modifications. Functional analysis of two co‐expressed genes using TILLING mutants provided novel insight into the involvement of unsaturated fatty acid biosynthesis and folate metabolism in nodule formation and development. The identified gene co‐expression modules provide valuable resources for further functional genomics studies to dissect the genetic basis of nodule formation and development in soybean. Two modules of highly co‐regulated soybean genes associated with nodule formation and development were identified using gene co‐expression network analysis.