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Aims: This research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature. Background: The mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear. Objective: The datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected. Methods: The mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package. Results: The mTORC1 score and WGCNA with β = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability. Conclusion: We developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature. [ABSTRACT FROM AUTHOR]

Introduction: Neuromyelitis Optica spectrum disorder (NMOSD) is an autoimmune disease characterized by anti-aquaporin-4 (AQP4) autoantibodies. The discovery of antibodies AQP4 and myelin oligodendrocyte glycoprotein (MOG) has expanded our understanding of the pathogenesis of neuromyelitis optica. However, the molecular mechanisms underlying the disease, particularly AQP4-associated optic neuritis (AQP4-ON), remain to be fully elucidated. Methods: In this study, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the transcriptomic profiles of peripheral blood samples from patients with AQP4-ON and MOG-positive optic neuritis (MOG-ON), compared to healthy controls. Results: WGCNA revealed a brown module (ME brown) strongly associated with AQP4-ON, which correlated positively with post-onset visual acuity decline. A total of 132 critical genes were identified, mainly involved in histone modification and microtubule dynamics. Notably, genes HDAC4, HDAC7, KDM6A, and KDM5C demonstrated high AUC values in ROC analysis, indicating their potential as biomarkers for AQP4-ON. Conclusion: Our findings provide novel insights into the molecular signature of AQP4-ON and highlight the potential of systems biology approaches in identifying biomarkers for NMOSD. The identified histone modification genes warrant further investigation for their role in disease pathogenesis and as therapeutic targets. [ABSTRACT FROM AUTHOR]

Objective To investigate the main pharmacological basis and mechanism of action of Guhuaisi Kangfu Pill (GHSKF) in the treatment of steroid-induced osteonecrosis of the femoral head (SONFH) Methods The active constituents and targets of GHSKF were screened by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and other databases. The speculative targets of SONFH were screened out based on Gene Cards and Online Mendelian Inheritance in Man (OMIM) databases. The gene modules and hub genes of SONFH were identified using a weighted gene co-expression network analysis (WGCNA). The intersection of the two targets and the result of WGCNA was taken to obtain the potential targets of GHSKF for the treatment of SONFH. The key active constituents were screened with the "active constituent-target" network, which was constructed by the Cytoscape software. Then, the STRING database was used to construct the protein interaction network to screen the key targets. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of key targets was performed, and the relationship between key active constituent, key targets and key signaling pathways was explored. Finally, the molecular docking between key active constituents and key targets was verified. In addition, the SONFH rat model was used for experimental verification. Results A total of 146 compounds and the corresponding 346 targets were identified based on the TCMSP database. A total of 4 187 targets of SONFH were obtained based on Gene Cards and OMIM databases. In addition, twelve gene modules and 2 556 hub genes of SONFH were screened out based on WGCNA. Quercetin, luteolin and kaempferol were key active ingredients for the treatment of SONFH. Various signaling pathways such as PI3K/AKT were involved. Molecular docking showed the key active ingredients had good binding activity with the key targets. The results of animal experiments demonstrated that GHSKF could improve bone biological alterations by up-regulating AKT1, PI3K, RUNX2, and down- regulating the expression of Caspase-3 and IL-6 (P<0.01), which verified some results of the network pharmacology prediction. Conclusion We analyzed the potential mechanism of action of GHSKF for the treatment of SONFH using network pharmacology and animal experiments, which may provide a reference for further research on its pharmacological basis and targets. [ABSTRACT FROM AUTHOR]

Objective Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated. Methods A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers. Results The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e–04) and negatively (r = −0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production. Conclusion The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.

Abstract Aims This research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature. Background The mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear. Objective The datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected. Methods The mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package. Results The mTORC1 score and WGCNA with β = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability. Conclusion We developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature.

The therapeutic effect of most traditional Chinese medicines (TCM) on ulcerative colitis is unclear, The objective of this study was to develop a core herbal screening model aimed at facilitating the transition from active ulcerative colitis (UC) to inactive. We obtained the gene expression dataset GSE75214 for UC from the GEO database and analysed the differentially expressed genes (DEGs) between active and inactive groups. Gene modules associated with the active group were screened using WGCNA, and immune-related genes (IRGs) were obtained from the ImmPort database. The TCMSP database was utilized to acquire the herb-molecule-target network and identify the herb-related targets (HRT). We performed intersection operations on HRTs, DEGs, IRGs, and module genes to identify candidate genes and conducted enrichment analyses. Subsequently, three machine learning algorithms (SVM-REF analysis, Random Forest analysis, and LASSO regression analysis) were employed to refine the hubgene from the candidate genes. Based on the hub genes identified in this study, we conducted compound and herb matching and further screened herbs related to abdominal pain and blood in stool using the Symmap database.Besides, the stability between molecules and targets were assessed using molecular docking and molecular dynamic simulation methods. An intersection operation was performed on HRT, DEGs, IRGs, and module genes, leading to the identification of 23 candidate genes. Utilizing three algorithms (RandomForest, SVM-REF, and LASSO) for analyzing the candidate genes and identifying the intersection, we identified five core targets (CXCL2, DUOX2, LYZ, MMP9, and AGT) and 243 associated herbs. Hedysarum Multijugum Maxim. (Huangqi), Sophorae Flavescentis Radix (Kushen), Cotyledon Fimbriata Turcz. (Wasong), and Granati Pericarpium (Shiliupi) were found to be capable of relieving abdominal pain and hematochezia during active UC. Molecular docking demonstrated that the compounds of the four aforementioned herbs showed positive docking activity with their core targets. The results of molecular dynamic simulations indicated that well-docked active molecules had a more stable structure when bound to their target complexes. The study has shed light on the potential of TCMs in treating active UC from an immunomodulatory perspective, consequently, 5 core targets and 4 key herbs has been identified. These findings can provide a theoretical basis for subsequent management and treatment of active UC with TCM, as well as offer original ideas for further research and development of innovative drugs for alleviating UC. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein–protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand–receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1β were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets. [ABSTRACT FROM AUTHOR]

Sepsis is a dysregulated immune response to infection that comes with multiple organ dysfunction and high mortality. The management of sepsis relies heavily on early recognition and diagnosis, but current diagnostic methods have limitations in timeliness, sensitivity, and discriminability. This study aims to discover novel biomarkers for sepsis diagnosis. Four datasets from different regions were analyzed using weighted gene co-expression network analysis (WGCNA), and genes with high Gene Significance values across these datasets were overlapped. Finally, two genes, CD177 and ANXA3, were identified. ANXA3 was validated as a potential sepsis biomarker by checking multiple datasets and Receiver Operating Characteristic (ROC) Curve Analysis. Of note, ANXA3 could distinguish not only between adult and child sepsis patients and healthy controls, but also between septic shock and cardiogenic shock. Moreover, a murine sepsis model was established and the results showed that the transcription of ANXA3 in peripheral blood of septic mice was significantly higher than that of healthy controls, while Escherichia coli infection alone did not significantly increase the transcription level of this gene. Subsequent studies of sepsis in mice revealed that the predictive effect of Anxa3 on sepsis could be observed as early as 6 h post-modeling. Interestingly, ANXA3 expression was predominantly up-regulated in myeloid cells, up-regulated in spleen, down-regulated in lung, and not detected in liver after sepsis modeling. Taken together, this study provides a way for the discovery of biomarkers and finds that ANXA3 may be a novel diagnostic biomarker for sepsis.

【Objective】The key target genes related to late pregnancy, lactation and fecundity in muscle tissue of Holstein cattle were screened, with a view to providing a theoretical basis for further study of gene function.【Method】The dataset GSE62159 from the GEO database on gene expression and fecundity in the muscle of Holstein cattles during late pregnancy, early lactation, and mid lactation were selected. The weighted gene co-expression network analysis (WGCNA) method was used for gene co- expression analysis, and the obtained modules were associated with late pregnancy, mid lactation and fecundity traits. The genes with connectivity in the top 30 of each module were selected as the Hub genes. The module was analyzed by protein-protein interaction (PPI) network with String website. The gene with the top 30 node connection degree value in PPI network was selected as the core gene, and intersected with Hub gene to obtain the key target genes related to different physiological periods and fecundity in the muscle.【Result】A total of 13 modules were obtained, of which the Green and Blue modules were related to late pregnancy and mid-lactation periods respectively, and the Magenta module was related to fecundity. The functional enrichment analysis of genes in each target module showed that the main functions of genes related to the late pregnancy were substance metabolism, energy metabolism, protein synthesis and secretion, body response to oxidative stress and neurodegeneration; the functions of genes related to mid-lactation included maintenance of stem cell population, protein synthesis and secretion, antigen processing and presentation and occurrence of many diseases; while those related to fecundity included angiogenesis, intrauterine embryonic development and actin filament binding, etc. Three key genes related to late pregnancy, one key gene related to mid-lactation and eight key genes related to fecundity were screened in the muscle of Holstein cattle.【Conclusion】The genes related to and late pregnancy in the muscle of Holstein cattle were identified as EIF5A, ACO2 and EEF1G, and the gene related to mid-lactation as EIF4A2, and the genes related to fecundity as ITGB1, MYH9, TLN1, CAV1, COL4A1, COL4A2, FLNA and HSPG2.

Background: While previous studies have indicated variability in distant metastatic potential among different mismatch repair (MMR) states in colorectal cancer (CRC), their findings remain inconclusive, especially considering potential differences across various ethnic backgrounds. Furthermore, the gene regulatory networks and the underlying mechanisms responsible for these variances in metastatic potential across MMR states have yet to be elucidated. Methods: We collected 2058 consecutive primary CRC samples from the South West of China and assessed the expression of MMR proteins (MLH1, MSH2, MSH6, and PMS2) using immunohistochemistry. To explore the inconsistencies between different MMR statuses and recurrence, we performed a meta‐analysis. To delve deeper, we employed Weighted Gene Co‐expression Network Analysis (WGCNA), ClueGo, and iRegulon, pinpointing gene expression networks and key regulatory molecules linked to metastasis and recurrence in CRC. Lastly, both univariate and multivariate Cox regression analyses were applied to determine the impact of core regulatory molecules on metastasis. Results: Of the samples, 8.2% displayed deficient MMR (dMMR), with losses of MLH1 and PSM2 observed in 40.8% and 63.9%, respectively. A unique 24.3% isolated loss of PMS2 without concurrent metastasis was identified, a result that diverges from established literature. Additionally, our meta‐analysis further solidifies the reduced recurrence likelihood in dMMR CRC samples compared to proficient MMR (pMMR). Two gene expression networks tied to distant metastasis and recurrence were identified, with a majority of metastasis‐related genes located on chromosomes 8 and 18. An IRF1 positive feedback loop was discerned in the metastasis‐related network, and IRF1 was identified as a predictive marker for both recurrence‐free and distant metastasis‐free survival across multiple datasets. Conclusion: Geographical and ethnic factors might influence peculiarities in MMR protein loss. Our findings also highlight new gene expression networks and crucial regulatory molecules in CRC metastasis, enhancing our comprehension of the mechanisms driving distant metastasis. [ABSTRACT FROM AUTHOR]

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought–rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought–rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought–rewatering (D2R), and one cycle of drought–rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson's correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding. [ABSTRACT FROM AUTHOR]

Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs. The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expression, survival rate, and biological functions were assessed to explore the functions of A3C. A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage. A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival. Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co-expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module membership; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carcinoma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma [ABSTRACT FROM AUTHOR]

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography–Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets. [ABSTRACT FROM AUTHOR]

[Objective] The key target genes related to late pregnancy, lactation and fecundity in muscle tissue of Holstein cattle were screened, with a view to providing a theoretical basis for further study of gene function. [Method] The dataset GSE62159 from the GEO database on gene expression and fecundity in the muscle of Holstein cattles during late pregnancy, early lactation, and mid lactation were selected. The weighted gene co-expression network analysis (WGCNA) method was used for gene co- expression analysis, and the obtained modules were associated with late pregnancy, mid lactation and fecundity traits. The genes with connectivity in the top 30 of each module were selected as the Hub genes. The module was analyzed by protein-protein interaction (PPI) network with String website. The gene with the top 30 node connection degree value in PPI network was selected as the core gene, and intersected with Hub gene to obtain the key target genes related to different physiological periods and fecundity in the muscle. [Result] A total of 13 modules were obtained, of which the Green and Blue modules were related to late pregnancy and mid-lactation periods respectively, and the Magenta module was related to fecundity. The functional enrichment analysis of genes in each target module showed that the main functions of genes related to the late pregnancy were substance metabolism, energy metabolism, protein synthesis and secretion, body response to oxidative stress and neurodegeneration; the functions of genes related to mid-lactation included maintenance of stem cell population, protein synthesis and secretion, antigen processing and presentation and occurrence of many diseases; while those related to fecundity included angiogenesis, intrauterine embryonic development and actin filament binding, etc. Three key genes related to late pregnancy, one key gene related to mid-lactation and eight key genes related to fecundity were screened in the muscle of Holstein cattle. [Conclusion] The genes related to and late pregnancy in the muscle of Holstein cattle were identified as EIF5A, ACO2 and EEF1G, and the gene related to mid-lactation as EIF4A2, and the genes related to fecundity as ITGB1, MYH9, TLN1, CAV1, COL4A1, COL4A2, FLNA and HSPG2. [ABSTRACT FROM AUTHOR]

Background Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein–protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included “skeletal system development”, “sister chromatid cohesion”, and “ossification”. Pathways were enriched in “Wnt signaling pathway” and “proteoglycans in cancer”. The BP terms enriched in the downregulated genes included “inflammatory response”, “xenobiotic metabolic process”, and “positive regulation of inflammatory response”. The enriched pathways included “neuroactive ligand–receptor interaction” and “AMP-activated protein kinase signaling”. JUN, tumor necrosis factor α, and interleukin-1β were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH. [ABSTRACT FROM AUTHOR]

Introduction: The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage. Methods: To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools. Results: Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution. Conclusion: Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice. [ABSTRACT FROM AUTHOR]

Although the prognosis of multiple myeloma (MM) has remarkably improved with the emerge of novel agents, it remains incurable and relapses inevitably. The molecular mechanisms of MM have not been well-studied. Herein, this study aimed to identify key genes in MM. The GSE39754 dataset was used to screen differentially expressed genes (DEGs) and construct a co-expression network. Hub nodes were identified in the protein and protein interaction (PPI) network. Datasets GSE13591 and GSE2658 were used to validate hub genes. Moreover, function and gene set enrichment analyses were performed to elucidate the molecular pathogenesis of MM. In this study, 11 genes were found to be hub genes in the co-expression network, among which four genes (CD68, FCER1G, PLAUR and LCP2) were also identified as hub nodes. In the test dataset GSE13591, CD68 and FCER1G were significantly downregulated in MM. Besides, the areas under the curve (AUCs) of CD68 and FCER1G were greater than 0.8 in both the training dataset and the test dataset. Our results also confirmed that FCER1G highly expressed patients had remarkably longer survival times in MM. Function and pathway enrichment analyses suggested that hub genes were associated with epithelial mesenchymal transition, TNF-α signaling via NF-κB and inflammatory response. GSEA in our study indicated that FCER1G participated in NK cell mediated cytotoxicity and the NOD-like receptor signaling pathway. Our study identified FCER1G as a key gene in MM, providing a novel biomarker and potential molecular mechanisms of MM for further studies. [ABSTRACT FROM AUTHOR]

Nitrate uptake by plants primarily relies on two gene families: Nitrate transporter 1/peptide transporter (NPF) and Nitrate transporter 2 (NRT2). Here, we extensively characterized the NPF and NRT2 families in the durum wheat genome, revealing 211 NPF and 20 NRT2 genes. The two families share many Cis Regulatory Elements (CREs) and Transcription Factor binding sites, highlighting a partially overlapping regulatory system and suggesting a coordinated response for nitrate transport and utilization. Analyzing RNA-seq data from 9 tissues and 20 cultivars, we explored expression profiles and co-expression relationships of both gene families. We observed a strong correlation between nucleotide variation and gene expression within the NRT2 gene family, implicating a shared selection mechanism operating on both coding and regulatory regions. Furthermore, NPF genes showed highly tissue-specific expression profiles, while NRT2s were mainly divided in two co-expression modules, one expressed in roots (NAR2/NRT3 dependent) and the other induced in anthers and/ovaries during maturation. Our evidences confirmed that the majority of these genes were retained after small-scale duplication events, suggesting a neo- or sub-functionalization of many NPFs and NRT2s. Altogether, these findings indicate that the expansion of these gene families in durum wheat could provide valuable genetic variability useful to identify NUE-related and candidate genes for future breeding programs in the context of low-impact and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Acute myocardial infarction (AMI) has been responsible for 8.5 million deaths worldwide each year over the past decade and is the leading cause of death. It is a severe illness worldwide and can happen in multiple age categories. Despite the significant progress in fundamental and clinical studies of AMI, biomarkers for AMI development have not been adequately investigated. The present research aimed to characterize potential new biomarkers of AMI by comprehensive analysis and to explore the immune infiltration characteristics of this pathophysiological process. In this study, we identified 68 DEGs and performed gene set enrichment analysis, GO, disease oncology, and KEGG analysis, and the results suggested that several functional signaling pathways and essential genes were strongly related to the onset and progression of AMI. In addition, combining multiple algorithms, FCER1G, CLEC4D, SRGN, and SLC11A1 were determined to be prospective biomarkers of AMI and showed good diagnostic value. Immuno‐infiltration analysis suggested that neutrophils, CD8+ T cells, monocytes, and M0 macrophages might be involved in the onset and progress of AMI. In conclusion, a combined approach was employed to select biomarkers associated with AMI and to probe the critical function of immune cells in the progression of AMI. In addition, clinical studies were applied to analyze the correlation between the occurrence of AMI and lipid dysregulation. [ABSTRACT FROM AUTHOR]

Abstract Background While previous studies have indicated variability in distant metastatic potential among different mismatch repair (MMR) states in colorectal cancer (CRC), their findings remain inconclusive, especially considering potential differences across various ethnic backgrounds. Furthermore, the gene regulatory networks and the underlying mechanisms responsible for these variances in metastatic potential across MMR states have yet to be elucidated. Methods We collected 2058 consecutive primary CRC samples from the South West of China and assessed the expression of MMR proteins (MLH1, MSH2, MSH6, and PMS2) using immunohistochemistry. To explore the inconsistencies between different MMR statuses and recurrence, we performed a meta‐analysis. To delve deeper, we employed Weighted Gene Co‐expression Network Analysis (WGCNA), ClueGo, and iRegulon, pinpointing gene expression networks and key regulatory molecules linked to metastasis and recurrence in CRC. Lastly, both univariate and multivariate Cox regression analyses were applied to determine the impact of core regulatory molecules on metastasis. Results Of the samples, 8.2% displayed deficient MMR (dMMR), with losses of MLH1 and PSM2 observed in 40.8% and 63.9%, respectively. A unique 24.3% isolated loss of PMS2 without concurrent metastasis was identified, a result that diverges from established literature. Additionally, our meta‐analysis further solidifies the reduced recurrence likelihood in dMMR CRC samples compared to proficient MMR (pMMR). Two gene expression networks tied to distant metastasis and recurrence were identified, with a majority of metastasis‐related genes located on chromosomes 8 and 18. An IRF1 positive feedback loop was discerned in the metastasis‐related network, and IRF1 was identified as a predictive marker for both recurrence‐free and distant metastasis‐free survival across multiple datasets. Conclusion Geographical and ethnic factors might influence peculiarities in MMR protein loss. Our findings also highlight new gene expression networks and crucial regulatory molecules in CRC metastasis, enhancing our comprehension of the mechanisms driving distant metastasis.

Psoriasis is a globally prevalent chronic inflammatory skin disease. This study aimed to scrutinize the hub genes related to inflammation and potential molecular mechanisms in psoriasis. Utilizing mRNA expression profiles from public datasets GSE13355, GSE78097, and GSE14905, we set up a comprehensive analysis. Initially, we selected differentially expressed genes (DEGs) from psoriasis and control samples in GSE13355, followed by calculating inflammatory indices using genomic set variation analysis (GSVA). Weighted gene co-expression network analysis (WGCNA) was then applied to link significant modules with the inflammatory index. This process helped us identify differentially expressed inflammation-related genes (DE-IRGs). A protein-protein interaction (PPI) network was established, with the molecular complex detection (MCODE) plug-in pinpointing six chemokine genes (CCR7, CCL2, CCL19, CXCL8, CXCL1, and CXCL2) as central hub genes. These genes demonstrated pronounced immunohistochemical staining in psoriatic tissues compared to normal skin. Notably, the CCR7 gene exhibited the highest potential for m6A modification sites. Furthermore, we constructed transcription factor-microRNA-mRNA networks, identifying 139 microRNAs and 52 transcription factors associated with the hub genes. For the LASSO logistic regression model, the area under the curve (AUC) in the training set was 1, and in the two validation cohorts GSE78097 and GSE14905 were 1 and 0.872, respectively. In conclusion, our study highlights six chemokine genes (CCR7, CCL2, CCL19, CXCL8, CXCL1, and CXCL2) as potential biomarkers in psoriasis, providing insights into the immune and inflammatory responses as pivotal instances in disease pathogenesis. These findings pave the way for exploring new therapeutic targets, particularly focusing on chemokine-associated pathways in psoriasis treatment.

Objective: Chondrocyte death is the hallmark of cartilage degeneration during osteoarthritis (OA). However, the specific pathogenesis of cell death in OA chondrocytes has not been elucidated. This study aims to validate the role of CDKN1A, a key programmed cell death (PCD)-related gene, in chondrogenic differentiation using a combination of single-cell and bulk sequencing approaches. Design: OA-related RNA-seq data (GSE114007, GSE55235, GSE152805) were downloaded from Gene Expression Omnibus database. PCD-related genes were obtained from GeneCards database. RNA-seq was performed to annotate the cell types in OA and control samples. Differentially expressed genes (DEGs) among those cell types (scRNA-DEGs) were screened. A nomogram of OA was constructed based on the featured genes, and potential drugs targeting the featured genes were predicted. The presence of key genes was confirmed using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot (WB), and immunohistochemistry (IHC). Micromass culture and Alcian blue staining were used to determine the effect of CDKN1A on chondrogenesis. Results: Six cell types, namely HomC, HTC, RepC, preFC, FC, and RegC, were annotated in scRNA-seq data. Five featured genes (JUN, CDKN1A, HMGB2, DDIT3, and DDIT4) were screened by multiple biological information analysis methods. TAXOTERE had the highest ability to dock with DDIT3. Functional analysis indicated that CDKN1A was enriched in processes related to collagen catabolism and acts as a positive regulator of autophagy. Additionally, CDKN1A was found to be associated with several KEGG pathways, including those involved in acute myeloid leukemia and autoimmune thyroid disease. CDKN1A was confirmed down-regulated in the joint tissues of OA mouse model and OA model cell. Inhibiting the expression of CDKN1A can significantly suppress the differentiation of OA chondrocytes. Conclusion: Our findings highlight the critical role of CDKN1A in promoting cartilage formation in both in vivo and in vitro and suggest its potential as a therapeutic target for OA treatment.

Abstract Background Peanut is an economically-important oilseed crop and needs a large amount of calcium for its normal growth and development. Calcium deficiency usually leads to embryo abortion and subsequent abnormal pod development. Different tolerance to calcium deficiency has been observed between different cultivars, especially between large and small-seed cultivars. Results In order to figure out different molecular mechanisms in defensive responses between two cultivars, we treated a sensitive (large-seed) and a tolerant (small-seed) cultivar with different calcium levels. The transcriptome analysis identified a total of 58 and 61 differentially expressed genes (DEGs) within small-seed and large-seed peanut groups under different calcium treatments, and these DEGs were entirely covered by gene modules obtained via weighted gene co-expression network analysis (WGCNA). KEGG enrichment analysis showed that the blue-module genes in the large-seed cultivar were mainly enriched in plant-pathogen attack, phenolic metabolism and MAPK signaling pathway, while the green-module genes in the small-seed cultivar were mainly enriched in lipid metabolism including glycerolipid and glycerophospholipid metabolisms. By integrating DEGs with WGCNA, a total of eight hub-DEGs were finally identified, suggesting that the large-seed cultivar concentrated more on plant defensive responses and antioxidant activities under calcium deficiency, while the small-seed cultivar mainly focused on maintaining membrane features to enable normal photosynthesis and signal transduction. Conclusion The identified hub genes might give a clue for future gene validation and molecular breeding to improve peanut survivability under calcium deficiency.

ABSTRACT: Ketosis is a common nutritional metabolic disease during the perinatal period in dairy cows. Although various risk factors have been identified, the molecular mechanism underlying ketosis remains elusive. In this study, subcutaneous white adipose tissue (sWAT) was biopsied for transcriptome sequencing on 10 Holstein cows with type II ketosis [blood β-hydroxybutyric acid (BHB) >1.4 mmol/L; Ket group] and another 10 cows without type II ketosis (BHB ≤1.4 mmol/L; Nket group) at d 10 after calving. Serum concentrations of nonesterified fatty acids (NEFA) and BHB, as indicators of excessive fat mobilization and circulating ketone bodies, respectively, were significantly higher in the Ket group than in the Nket group. Aspartate transaminase (AST) and total bilirubin (TBIL), as indicators of liver damage, were higher in the Ket group than in the Nket group. Weighted gene co-expression network analysis (WGCNA) of the sWAT transcriptome revealed modules significantly correlated with serum BHB, NEFA, AST, TBIL, and total cholesterol. The genes in these modules were enriched in the regulation of the lipid biosynthesis process. Neurotrophic tyrosine kinase receptor type 2 (NTRK2) was identified as the key hub gene by intramodular connectivity, gene significance, and module membership. Quantitative reverse transcription PCR analyses for these samples, as well as a set of independent samples, validated the downregulation of NTRK2 expression in the sWAT of dairy cows with type II ketosis. NTRK2 encodes tyrosine protein kinase receptor B (TrkB), which is a high-affinity receptor for brain-derived neurotrophic factor, suggesting that abnormal lipid mobilization in cows with type II ketosis might be associated with impaired central nervous system regulation of adipose tissue metabolism, providing a novel insight into the pathogenesis underlying type II ketosis in dairy cows.

Ziyue Luo,1,&ast; Guifang Lu,2,&ast; Qiang Yang,1 Juncan Ding,1 Tianyu Wang,1 Pengfei Hu3 1Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People’s Republic of China; 2Department of Rheumatology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310005, People’s Republic of China; 3Department of Cardiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310005, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Pengfei Hu, Department of Cardiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310005, People’s Republic of China, Tel +86 15267037741, Email 20064012@zcmu.edu.cnPurpose: The purpose was to identify shared immune cells and co-disease genes in chronic heart failure (HF) and systemic lupus erythematosus (SLE), as well as explore the potential mechanisms of action between HF and SLE.Methods: A collection of peripheral blood mononuclear cells (PBMCs) from ten patients with HF and SLE and ten normal controls (NC) was used for transcriptome sequencing. Differentially expressed genes (DEGs) analysis, enrichment analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and machine learning were applied for the screening of shared immune cells and co-disease genes in HF and SLE. Gene expression analysis and correlation analysis were used to explore the potential mechanisms of co-disease genes and immune cells in HF and SLE.Results: In this study, it was found that two immune cells, T cells CD4 naïve and Monocytes, displayed similar expression patterns in HF and SLE at the same time. By taking intersection of the above immune cell-associated genes with the DEGs common to both HF and SLE, four immune-associated co-disease genes, CCR7, RNASE2, RNASE3 and CXCL10, were finally identified. CCR7, as one of the four key genes, was significantly down-regulated in HF and SLE, while the rest three key genes were all significantly up-regulated in both diseases.Conclusion: T cells CD4 naïve and Monocytes were first revealed as possible shared immune cells of HF and SLE, and CCR7, RNASE2, RNASE3 and CXCL10 were identified as possible key genes common to HF and SLE as well as potential biomarkers or therapeutic targets for HF and SLE.Graphical Abstract: Keywords: heart failure, systemic lupus erythematosus, immunology, transcriptome sequencing, machine learning

ABSTRACTPurpose: Although the prognosis of multiple myeloma (MM) has remarkably improved with the emerge of novel agents, it remains incurable and relapses inevitably. The molecular mechanisms of MM have not been well-studied. Herein, this study aimed to identify key genes in MM.Materials and Methods: The GSE39754 dataset was used to screen differentially expressed genes (DEGs) and construct a co-expression network. Hub nodes were identified in the protein and protein interaction (PPI) network. Datasets GSE13591 and GSE2658 were used to validate hub genes. Moreover, function and gene set enrichment analyses were performed to elucidate the molecular pathogenesis of MM.Results: In this study, 11 genes were found to be hub genes in the co-expression network, among which four genes (CD68, FCER1G, PLAUR and LCP2) were also identified as hub nodes. In the test dataset GSE13591, CD68 and FCER1G were significantly downregulated in MM. Besides, the areas under the curve (AUCs) of CD68 and FCER1G were greater than 0.8 in both the training dataset and the test dataset. Our results also confirmed that FCER1G highly expressed patients had remarkably longer survival times in MM. Function and pathway enrichment analyses suggested that hub genes were associated with epithelial mesenchymal transition, TNF-α signaling via NF-κB and inflammatory response. GSEA in our study indicated that FCER1G participated in NK cell mediated cytotoxicity and the NOD-like receptor signaling pathway.Conclusion: Our study identified FCER1G as a key gene in MM, providing a novel biomarker and potential molecular mechanisms of MM for further studies.

Abstract Background RNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation. Methods Four thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR. Results One thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought–rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought–rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought–rewatering (D2R), and one cycle of drought–rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson’s correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding.

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography–Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.

Sepsis is a life-threatening condition, especially in very low birth weight (VLBW) infants, and its pathogenesis remains unclear. Effective biomarkers need to be found to diagnose and treat the disease at an early stage. The Gene Expression Omnibus (GEO) database was screened and analyzed for differentially expressed genes (DEGs) in VLBW infants with sepsis. DEGs were then analyzed for functional enrichment. A weighted gene co-expression network analysis (WCGNA) was performed to identify the key modules and genes. The optimal feature genes (OFGs) were created using three machine learning algorithms. The single-sample Gene Set Enrichment Analysis (ssGSEA) scored the degree of immune cell enrichment between septic and control patients, and the correlation between OFGs and immune cells was evaluated. A total of 101 DEGs were identified between the sepsis and control samples. DEGs were mainly associated with immune responses and inflammatory signaling pathways in the enrichment analysis. In the WGCNA analysis, the MEturquoise module was significantly correlated with sepsis in VLBW infants (cor = 0.57, P < 0.001). By intersecting OFGs derived from three machine learning algorithms, two biomarkers were identified: glycogenin 1 (GYG1) and resistin (RETN). The area under the curves of GYG1 and RETN was greater than 0.97 in the testing set. The ssGSEA indicated immune cells infiltration in septic VLBW infants, and GYG1 and RETN revealed close correlations with immune cells. New biomarkers offer promising insights into the diagnosis and treatment of sepsis in VLBW infants.

Background: Peanut is an economically-important oilseed crop and needs a large amount of calcium for its normal growth and development. Calcium deficiency usually leads to embryo abortion and subsequent abnormal pod development. Different tolerance to calcium deficiency has been observed between different cultivars, especially between large and small-seed cultivars. Results: In order to figure out different molecular mechanisms in defensive responses between two cultivars, we treated a sensitive (large-seed) and a tolerant (small-seed) cultivar with different calcium levels. The transcriptome analysis identified a total of 58 and 61 differentially expressed genes (DEGs) within small-seed and large-seed peanut groups under different calcium treatments, and these DEGs were entirely covered by gene modules obtained via weighted gene co-expression network analysis (WGCNA). KEGG enrichment analysis showed that the blue-module genes in the large-seed cultivar were mainly enriched in plant-pathogen attack, phenolic metabolism and MAPK signaling pathway, while the green-module genes in the small-seed cultivar were mainly enriched in lipid metabolism including glycerolipid and glycerophospholipid metabolisms. By integrating DEGs with WGCNA, a total of eight hub-DEGs were finally identified, suggesting that the large-seed cultivar concentrated more on plant defensive responses and antioxidant activities under calcium deficiency, while the small-seed cultivar mainly focused on maintaining membrane features to enable normal photosynthesis and signal transduction. Conclusion: The identified hub genes might give a clue for future gene validation and molecular breeding to improve peanut survivability under calcium deficiency. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to identify biomarkers for acute and chronic brucellosis using advanced proteomic and bioinformatic methods. Methods: Blood samples from individuals with acute brucellosis, chronic brucellosis, and healthy controls were analyzed. Proteomic techniques and differential expression analysis were used to identify differentially expressed proteins. Co-expression modules associated with brucellosis traits were identified using weighted gene co-expression network analysis (WGCNA). Results: 763 differentially expressed proteins were identified, and two coexpression modules were found to be significantly associated with brucellosis traits. 25 proteins were differentially expressed in all three comparisons, and 20 hub proteins were identified. Nine proteins were found to be both differentially expressed and hub proteins, indicating their potential significance. A random forest model based on these nine proteins showed good classification performance. Discussion: The identified proteins are involved in processes such as inflammation, coagulation, extracellular matrix regulation, and immune response. They provide insights into potential therapeutic targets and diagnostic biomarkers for brucellosis. This study improves our understanding of brucellosis at the molecular level and paves the way for further research in targeted therapies and diagnostics. [ABSTRACT FROM AUTHOR]

Ketosis is a common nutritional metabolic disease during the perinatal period in dairy cows. Although various risk factors have been identified, the molecular mechanism underlying ketosis remains elusive. In this study, subcutaneous white adipose tissue (sWAT) was biopsied for transcriptome sequencing on 10 Holstein cows with type II ketosis [blood β-hydroxybutyric acid (BHB) >1.4 mmol/L; Ket group] and another 10 cows without type II ketosis (BHB ≤1.4 mmol/L; Nket group) at d 10 after calving. Serum concentrations of nonesterified fatty acids (NEFA) and BHB, as indicators of excessive fat mobilization and circulating ketone bodies, respectively, were significantly higher in the Ket group than in the Nket group. Aspartate transaminase (AST) and total bilirubin (TBIL), as indicators of liver damage, were higher in the Ket group than in the Nket group. Weighted gene co-expression network analysis (WGCNA) of the sWAT transcriptome revealed modules significantly correlated with serum BHB, NEFA, AST, TBIL, and total cholesterol. The genes in these modules were enriched in the regulation of the lipid biosynthesis process. Neurotrophic tyrosine kinase receptor type 2 (NTRK2) was identified as the key hub gene by intramodular connectivity, gene significance, and module membership. Quantitative reverse transcription PCR analyses for these samples, as well as a set of independent samples, validated the downregulation of NTRK2 expression in the sWAT of dairy cows with type II ketosis. NTRK2 encodes tyrosine protein kinase receptor B (TrkB), which is a high-affinity receptor for brain-derived neurotrophic factor, suggesting that abnormal lipid mobilization in cows with type II ketosis might be associated with impaired central nervous system regulation of adipose tissue metabolism, providing a novel insight into the pathogenesis underlying type II ketosis in dairy cows. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression and are known to mediate endocrine and chemotherapy resistance through paracrine signaling. Additionally, they directly influence the expression and growth dependence of ER in Luminal breast cancer (LBC). This study aims to investigate stromal CAF-related factors and develop a CAF-related classifier to predict the prognosis and therapeutic outcomes in LBC. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to obtain mRNA expression and clinical information from 694 and 101 LBC samples, respectively. CAF infiltrations were determined by estimating the proportion of immune and cancer cells (EPIC) method, while stromal scores were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify stromal CAF-related genes. A CAF risk signature was developed through univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. The Spearman test was used to evaluate the correlation between CAF risk score, CAF markers, and CAF infiltrations estimated through EPIC, xCell, microenvironment cell populations-counter (MCP-counter), Results: We constructed a 5-gene prognostic model consisting of RIN2, THBS1, IL1R1, RAB31, and COL11A1 for CAF. Using the median CAF risk score as the cutoff, we classified LBC patients into high- and low-CAF-risk groups and found that those in the high-risk group had a significantly worse prognosis. Spearman correlation analyses demonstrated a strong positive correlation between the CAF risk score and stromal and CAF infiltrations, with the five model genes showing positive correlations with CAF markers. In addition, the TIDE analysis revealed that high-CAF-risk patients were less likely to respond to immunotherapy. Gene set enrichment analysis (GSEA) identified significant enrichment of ECM receptor interaction, regulation of actin cytoskeleton, epithelial-mesenchymal transition (EMT), and TGF-β signaling pathway gene sets in the high-CAF-risk group patients. Conclusion: The five-gene prognostic CAF signature presented in this study was not only reliable for predicting prognosis in LBC patients, but it was also effective in estimating clinical immunotherapy response. These findings have significant clinical implications, as the signature may guide tailored anti-CAF therapy in combination with immunotherapy for LBC patients. and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The TIDE algorithm was further utilized to assess the response to immunotherapy. Additionally, Gene set enrichment analysis (GSEA) was applied to elucidate the molecular mechanisms underlying the findings. [ABSTRACT FROM AUTHOR]

Abstract Background Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human. Results In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery. Conclusions We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Background In this study, we investigated the expression profile of this disease to identify new hub genes to help diagnose hepatocellular carcinoma (HCC). Materials and Methods: Weighted gene co-expression network (WGCNA) analysis was used in this study to identify key modules and hub genes associated with HCC in the GSE176271 dataset. We also looked at the clinical significance of key genes and the biological pathways linked to them in external databases. We validated the identified hub genes using data from the GEPIA and XenaBrowser databases. Results: The Midnight blue module was found to be significantly related to the pathological stage (r=0.94, P=1e-11). Five hub genes (CLEC4M, CLEC4G, FCN2, OIT3, and ASPG) were associated with prognosis using DEG identification and WGCNA analysis. The three biological pathways associated with the Midnight blue module were copper ion detoxification, cell ion homeostasis, and complement activation, as well as the lectin pathway. Conclusion: The current study's findings provide new and effective molecular targets for the detection of HCC, which can improve patients’ prognosis. [ABSTRACT FROM AUTHOR]

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation. [ABSTRACT FROM AUTHOR]

Background: RNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation. Methods: Four thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR. Results: One thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P < 0.001; HR:3.682; CI:2.377–5.705) and ROC curves indicated it has an effective predictive performance (1-year AUC: 0.653; 3-year AUC:0.673; 5-year AUC: 0.777). GSEA indicated that high risk score was correlated with several cancer-related pathways, including cytokine-cytokine receptor cross talk, ECM receptor cross talk, HEDGEHOG signaling cascade and JAK/STAT signaling cascade. ssGSEA analysis exhibited a significant correlation between immune status and the risk signature. Noscapine and clofazimine were screened as potential drugs for CRC patients with high-risk scores. TDRD5 and GPC1 were identified as hub genes and their expression were validated in 15 pairs of surgically resected CRC tissues. Conclusion: Our research provides a depth insight of RBPs' role in CRC and the proposed signature are helpful to the personalized treatment and prognostic judgement. [ABSTRACT FROM AUTHOR]

Background: Understanding the complex regulatory network underlying plant nitrogen (N) responses associated with high Nitrogen Use Efficiency (NUE) is one of the main challenges for sustainable cropping systems. Nitrate (NO3 -), acting as both an N source and a signal molecule, provokes very fast transcriptome reprogramming, allowing plants to adapt to its availability. These changes are genotype- and tissue-specific; thus, the comparison between contrasting genotypes is crucial to uncovering high NUE mechanisms. Methods: Here, we compared, for the first time, the spatio-temporal transcriptome changes in both root and shoot of two NUE contrasting tomato genotypes, Regina Ostuni (high-NUE) and UC82 (low-NUE), in response to short-term (within 24 h) low (LN) and high (HN) NO3 - resupply. Results: Using time-series transcriptome data (0, 8, and 24 h), we identified 395 and 482 N-responsive genes differentially expressed (DEGs) between RO and UC82 in shoot and root, respectively. Protein kinase signaling plant hormone signal transduction, and phenylpropanoid biosynthesis were the main enriched metabolic pathways in shoot and root, respectively, and were upregulated in RO compared to UC82. Interestingly, several N transporters belonging to NRT and NPF families, such as NRT2.3, NRT2.4, NPF1.2, and NPF8.3, were found differentially expressed between RO and UC82 genotypes, which might explain the contrasting NUE performances. Transcription factors (TFs) belonging to several families, such as ERF, LOB, GLK, NFYB, ARF, Zinc-finger, and MYB, were differentially expressed between genotypes in response to LN. A complementary Weighted Gene Co-expression Network Analysis (WGCNA) allowed the identification of LN-responsive co-expression modules in RO shoot and root. The regulatory network analysis revealed candidate genes that might have key functions in short-term LN regulation. In particular, an asparagine synthetase (ASNS), a CBL-interacting serine/threonine-protein kinase 1 (CIPK1), a cytokinin riboside 5'-monophosphate phosphoribohydrolase (LOG8), a glycosyltransferase (UGT73C4), and an ERF2 were identified in the shoot, while an LRR receptor-like serine/threonine-protein kinase (FEI1) and two TFs NF-YB5 and LOB37 were identified in the root. Discussion: Our results revealed potential candidate genes that independently and/or concurrently may regulate short-term low-N response, suggesting a key role played by cytokinin and ROS balancing in early LN regulation mechanisms adopted by the N-use efficient genotype RO. [ABSTRACT FROM AUTHOR]

Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function. Though progress has been made to elucidate the process, molecular mechanisms of different classes of cardiomyopathies remain elusive. This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM). We firstly detected the co-expressed modules using the weighted gene co-expression network analysis (WGCNA). Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient (PCC) between the modules and the phenotype of DCM/ICM. The differentially expressed genes in the modules were selected to perform functional enrichment. The potential transcription factors (TFs) prediction was conducted for transcription regulation of hub genes. Apoptosis and cardiac conduction were perturbed in DCM and ICM, respectively. TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different, which helps make more accurate discrimination between them at molecular levels. In conclusion, comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression. Thus, this understanding may promote the development of innovative diagnoses and treatments.

Background and aimsinflammation plays an important role in atrial fibrillation (AF). In this study, we investigated the significance of immune cell infiltration in AF and identified the potential Hub genes involved in the regulation of immune cell infiltration in AF.Methodswe obtained AF datasets from the GEO database and analyzed them for obtaining differentially expressed genes (DEGs) by R software. Then, we performed GO, KEGG, and GSEA enrichment analyses of DEGs. The Hub genes of AF were determined by least absolute shrinkage selection operator (LASSO) regression analysis and weighted gene co-expression network analysis (WGCNA). Their validation was verified by using quantitative polymerase chain reaction (qPCR) in the AF rat model. Finally, we used a single sample GSEA (ssGSEA) to analyze immune cell infiltration and its relationship with hub genes.ResultsWe obtained 298 DGEs from the heatmap and found that DGEs were closely related to inflammation, immunity, and cytokine interactions by enrichment analyses. We obtained 10 co-expression modules by WGCNA. Among them, the module including CLEC4A, COTL1, EVI2B, FCER1G, GAPT, HCST, NCF2, PILRA, TLR8, and TYROBP had the highest correlation with AF. Four Hub genes (PILRA, NCF2, EVI2B, GAPT) were obtained further by LASSO analysis. The results suggested that the expression level of PILRA was significantly elevated in the rats with AF by qPCR, compared to the rats without AF. The results revealed that the infiltration of neutrophils, macrophages, monocytes, mast cells, immature B cells, myeloid-derived suppressor cell (MDSC), dendritic cell, and T cells and their partial subpopulations were closely related to AF by ssGSEA analysis, and PILRA was positively correlated with immature B cell, monocyte, macrophage, mast cell, dendritic cell, and T cells and their partial subpopulations by Spearman correlation analysis.ConclusionsPILRA was closely related to multiple types of immune cell infiltration, which may be associated with AF. PILRA may be a novel target of intervention for AF.

Objective This study was conducted to identify the functional long non-coding RNAs (lncRNAs) for swine lactation by RNA-seq data of mammary gland. Methods According to the RNA-seq data of swine mammary gland, we screened lncRNAs, performed differential expression analysis, and confirmed the functional lncRNAs for swine lactation by validation of genome wide association study (GWAS) signals, functional annotation and weighted gene co-expression network analysis (WGCNA). Results We totally identified 286 differentially expressed (DE) lncRNAs in mammary gland at different stages from 14 days prior to (-) parturition to day 1 after (+) parturition, and the expressions of most of lncRNAs were strongly changed from day −2 to day +1. Further, the GWAS signals of sow milk ability trait were significantly enriched in DE lncRNAs. Functional annotation revealed that these DE lncRNAs were mainly involved in mammary gland and lactation developing, milk composition metabolism and colostrum function. By performing weighted WGCNA, we identified 7 out of 12 lncRNA-mRNA modules that were highly associated with the mammary gland at day −14, day −2, and day +1, in which, 35 lncRNAs and 319 mRNAs were involved. Conclusion This study suggested that 18 lncRNAs and their 20 target genes were promising candidates for swine parturition and colostrum occurrence processes. Our research provided new insights into lncRNA profiles and their regulating mechanisms from colostrogenesis to lactogenesis in swine.

BackgroundIschemic cardiomyopathy (ICM) induced heart failure (HF) is one of the most common causes of death worldwide. This study aimed to find candidate genes for ICM-HF and to identify relevant biomarkers by machine learning (ML).MethodsThe expression data of ICM-HF and normal samples were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ICM-HF and normal group were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and gene ontology (GO) annotation analysis, protein–protein interaction (PPI) network, gene pathway enrichment analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were performed. Weighted gene co-expression network analysis (WGCNA) was applied to screen for disease-associated modules, and relevant genes were derived using four ML algorithms. The diagnostic values of candidate genes were assessed using receiver operating characteristic (ROC) curves. The immune cell infiltration analysis was performed between the ICM-HF and normal group. Validation was performed using another gene set.ResultsA total of 313 DEGs were identified between ICM-HF and normal group of GSE57345, which were mainly enriched in biological processes and pathways related to cell cycle regulation, lipid metabolism pathways, immune response pathways, and intrinsic organelle damage regulation. GSEA results showed positive correlations with pathways such as cholesterol metabolism in the ICM-HF group compared to normal group and lipid metabolism in adipocytes. GSEA results also showed a positive correlation with pathways such as cholesterol metabolism and a negative correlation with pathways such as lipolytic presentation in adipocytes compared to normal group. Combining multiple ML and cytohubba algorithms yielded 11 relevant genes. After validation using the GSE42955 validation sets, the 7 genes obtained by the machine learning algorithm were well verified. The immune cell infiltration analysis showed significant differences in mast cells, plasma cells, naive B cells, and NK cells.ConclusionCombined analysis using WGCNA and ML identified coiled-coil-helix-coiled-coil-helix domain containing 4 (CHCHD4), transmembrane protein 53 (TMEM53), acid phosphatase 3 (ACPP), aminoadipate-semialdehyde dehydrogenase (AASDH), purinergic receptor P2Y1 (P2RY1), caspase 3 (CASP3) and aquaporin 7 (AQP7) as potential biomarkers of ICM-HF. ICM-HF may be closely related to pathways such as mitochondrial damage and disorders of lipid metabolism, while the infiltration of multiple immune cells was identified to play a critical role in the progression of the disease.

BackgroundHeart failure (HF) is a syndrome with global clinical and socioeconomic burden worldwide owing to its poor prognosis. Jiashen Prescription (JSP), a traditional Chinese medicine (TCM) formula, exhibits unambiguous effects on treating HF. Previously, we have reported that underlying mechanisms of JSP by an untargeted metabolomics approach, but the contribution of gut microbiota and metabolic interaction to the cardioprotective efficacy of JSP remains to be elucidated.Materials and methodsFirstly, the rat model of heart failure was established by the permanent ligation of the left anterior descending coronary artery. The efficacy evaluation of JSP in treating HF rats was per-formed by left ventricular ejection fraction (LVEF). Then, 16S rRNA gene sequencing and LC/MS-based metabolomic analysis were utilized to explore the characteristics of cecal-contents microecology and plasma metabolic profile, respectively. After that, the correlation between intestinal micro-ecological characteristics and plasma metabolic characteristics was analyzed to explore the potential mechanism of the JSP treatment in HF.ResultsJSP could improve the cardiac function of heart failure rats and thus ameliorate heart failure via enhancing rat LVEF. Results of intestinal flora analysis revealed that JSP not only adjusted gut microbiota disturbances by enriching species diversity, reducing the abundance of pathogenic bacteria (such as Allobaculum, Brevinema), as well as increasing the abundance of beneficial bacteria (such as Lactobacillus, Lachnospiraceae_NK4A136_group), but also improved metabolic disorders by reversing metabolite plasma levels to normality. Through the conjoint analysis of 8 metabolites and the OTUs relative abundance data in the 16srRNA sequencing results by WGCNA method, 215 floras significantly related to the eight compounds were identified. The results of the correlation analysis demonstrated a significant association between intestinal microbiota and plasma metabolic profile, especially the significant correlation of Ruminococcaceae_UCG-014 and Protoporphyrin IX, Ruminococcaceae_UCG-005, Christensenellaceae_R-7_group and nicotinamide, dihydrofolic acid.ConclusionThe present study illustrated the underlying mechanism of JSP to treat heart failure by affecting intestinal flora and plasma metabolites, provide a potential therapeutic strategy against heart failure.

BackgroundUnderstanding the complex regulatory network underlying plant nitrogen (N) responses associated with high Nitrogen Use Efficiency (NUE) is one of the main challenges for sustainable cropping systems. Nitrate (NO3-), acting as both an N source and a signal molecule, provokes very fast transcriptome reprogramming, allowing plants to adapt to its availability. These changes are genotype- and tissue-specific; thus, the comparison between contrasting genotypes is crucial to uncovering high NUE mechanisms.MethodsHere, we compared, for the first time, the spatio-temporal transcriptome changes in both root and shoot of two NUE contrasting tomato genotypes, Regina Ostuni (high-NUE) and UC82 (low-NUE), in response to short-term (within 24 h) low (LN) and high (HN) NO3- resupply. ResultsUsing time-series transcriptome data (0, 8, and 24 h), we identified 395 and 482 N-responsive genes differentially expressed (DEGs) between RO and UC82 in shoot and root, respectively. Protein kinase signaling plant hormone signal transduction, and phenylpropanoid biosynthesis were the main enriched metabolic pathways in shoot and root, respectively, and were upregulated in RO compared to UC82. Interestingly, several N transporters belonging to NRT and NPF families, such as NRT2.3, NRT2.4, NPF1.2, and NPF8.3, were found differentially expressed between RO and UC82 genotypes, which might explain the contrasting NUE performances. Transcription factors (TFs) belonging to several families, such as ERF, LOB, GLK, NFYB, ARF, Zinc-finger, and MYB, were differentially expressed between genotypes in response to LN. A complementary Weighted Gene Co-expression Network Analysis (WGCNA) allowed the identification of LN-responsive co-expression modules in RO shoot and root. The regulatory network analysis revealed candidate genes that might have key functions in short-term LN regulation. In particular, an asparagine synthetase (ASNS), a CBL-interacting serine/threonine-protein kinase 1 (CIPK1), a cytokinin riboside 5’-monophosphate phosphoribohydrolase (LOG8), a glycosyltransferase (UGT73C4), and an ERF2 were identified in the shoot, while an LRR receptor-like serine/threonine-protein kinase (FEI1) and two TFs NF-YB5 and LOB37 were identified in the root. DiscussionOur results revealed potential candidate genes that independently and/or concurrently may regulate short-term low-N response, suggesting a key role played by cytokinin and ROS balancing in early LN regulation mechanisms adopted by the N-use efficient genotype RO.

In general, insects grow (increase in body size) through molting. To the opposite, the body size of the males of the horned gall aphid, Schlechtendalia chinensis, gets smaller after molting and as they age. To understand the molecular bases of this rare phenomenon, transcriptomes were generated from 1–5 days old male and the data were analyzed via a weighted gene co-expression network analysis (WGCNA). A total of 15 partitioned modules with different topological overlaps were obtained, and four modules were identified as highly significant for male body length (p < 0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that a portion of genes in the four modules are likely involved in autophagy and apoptosis. In addition, a total of 40 hub genes were obtained in the four modules, and among them eight genes were highly expressed in males compared to individuals of other generations of S. chinensis. These eight genes were associated with autophagy and apoptosis. Our results reveal the unique negative growth phenomenon in male S. chinensis after molting, and also suggest that the male S. chinensis with no ability to feed probably decompose their own substances via autophagy and apoptosis to provide energy for life activities such as germ cell development.

Background: Calcific aortic valve disease (CAVD) is a progressive fibrocalcific disease that can be treated only through valve replacement. This study aimed to determine the role of hub genes and immune cell infiltration in CAVD progression. Methods: In this study, bioinformatics analysis was used to identify hub genes involved in CAVD. The datasets were downloaded from the Gene Expression Omnibus (GEO) database. Gene expression differences were evaluated via pathway and Gene Ontology analyses. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes were used to screen hub genes. The CIBERSORT algorithm was used to compare immune infiltration into the calcified aortic valve based on the hub genes between high- and lowexpression groups. We also performed single-cell RNA sequencing based on six different human aortic valve leaflets. The expression of hub genes was identified in human and mouse samples through quantitative real-time polymerase chain reaction (qPCR), immunohistochemistry, immunofluorescence, and ELISA, and clinical features of the patients were investigated. Results: In total, 454 differentially expressed genes were obtained from the GEO database. WGCNA was used to find 12 co-expression modules in the Array Express database, of which one hub module (brown module) was most correlated with CAVD. Two hub genes were identified after combining the differentially expressed genes S100A8 and S100A9. Regarding these genes, the immune infiltration profiles varied between high- and low-expression groups. Compared with that in the low hub gene expression group, the high hub gene expression group had a higher proportion of activated NK cells (p < 0.01) and M1 macrophages (p < 0.05). The expression of S100A8 and S100A9 was consistent with single-gene RNA sequencing results, confirming that the expression levels of these two hub genes are significantly upregulated in patients with CAVD (p < 0.01). Furthermore, these results were verified using mouse and human samples by performing immunofluorescence, immunohistochemistry, qPCR, and ELISA analyses. Finally, the localization of S100A8 and S100A9 in monocytes and macrophages was confirmed via immunofluorescence using human aortic valves. Conclusion: These results demonstrate that S100A8 and S100A9 are two hub genes involved in CAVD, which might play an important role in its development through immune-related signaling pathways. [ABSTRACT FROM AUTHOR]

Huaxin Zhu, Jiacong Tan, Yeyu Zhao, Zhihua Wang, Zhiwu Wu, Meihua Li Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of ChinaCorrespondence: Meihua Li, Email limeihua2000@sina.comBackground: Intracranial aneurysm (IA) is the most common and is the main cause of spontaneous subarachnoid hemorrhage (SAH). The underlying molecular mechanisms for preventing IA progression have not been fully identified. Our research aimed to identify the key genes and critical pathways of IA through gene co-expression networks.Methods: Gene Expression Omnibus (GEO) datasets GSE13353, GSE54083 and GSE75436 were used in the study. The genetic data were analyzed by weighted gene co-expression network analysis (WGCNA). Then the clinically significant modules were identified and the differentially expressed genes (DEGs) with the genes were intersected in these modules. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the function or pathway. In addition, the composition of immune cells was analyzed by CIBERSORT algorithm. Finally, the hub genes and key genes were identified by GSE122897.Results: A total of 266 DEGs and two modules with clinical significance were identified. The inflammatory response and immune response were identified by GO and KEGG. CCR5, CCL4, CCL20, and FPR3 were the key genes in the module correlated with IA. The proportions of infiltrating immune cells in IA and normal tissues were different, especially in terms of macrophages and mast cells.Conclusion: The chemotactic system has been identified as a key pathway of IA, and interacting macrophages may regulate this pathological process.Keywords: intracranial aneurysm, IA, weighted gene co-expression network analysis, WGCNA, Gene Expression Omnibus, GEO, chemotaxis, immune infiltration