Weighted Gene Co-expression Analysis Research Articles

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.

Transcriptome analysis of the genes and regulators involving in vitamin E biosynthesis in Elaeagnus mollis diels.

Elaeagnus mollis is an important newly developing woody oil plant species and the vitamin E (VitE) content in its kernel oil is relatively high. In the present study, the VitE component content and functional genes involving in VitE biosynthesis in E. mollis kernel at different developmental stage were investigated. The VitE content increased with kernel development, reaching up to ~ 7.96mg/g oil in kernel mature stage. The content of tocopherol was much higher than that of tocotrienol and γ-tocopherol became the dominant component. E. mollis kernel extracts had relatively strong antioxidant capacity. We identified 17 genes (16 VTEs and 1 homogentisic acid geranylgeranyl transferase (HGGT)) directly involving in VitE biosynthesis in RNA-Seq data. Phylogenetic and qRT-PCR results indicated that the annotation and reliability of the RNA-Seq were accurate. Transient overexpression of EmVTE3 and EmWRKY13 in tobacoo leaves increased and decreased the VitE content to 192.18 and 118.29µg/g, respectively. Weighted gene co-expression analysis elucidated that the blue module showed significant correlation with tocopherol content. Co-expression network analysis revealed that 2-methyl-6-phytobenzoquinone methyltransferase (MPBQ-MT/VTE3) played a vital role and EmWRKY13 may be a key negative regulator in E. mollis VitE biosynthesis. This study not only revealed the traditional VitE biosynthesis pathway in E. mollis, but also set a solid foundation for future genetic breeding of this species.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients

PurposeDespite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions.MethodsInitially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells.ResultsWe conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model’s predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells.ConclusionThis study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

Lipidomic and transcriptomic characteristics of boar seminal plasma extracellular vesicles associated with sperm motility

Seminal plasma extracellular vesicles (SPEVs) play an important role in regulating sperm motility by delivering various cargoes, such as miRNAs, mRNAs, proteins and metabolites. However, information on the lipid compositions of SPEVs and their roles in semen quality is limited. Here, we performed high-throughput transcriptomic and lipidomic analysis on SPEVs isolated from 20 boars with high or low sperm motility. Then, we evaluated the lipid composition and gene expression characteristics of SPEVs and identified the specific lipids and genes related to sperm motility. As a result, a total of 26 lipid classes were identified in SPEVs, and five subclasses, CerG2, CerG3, LPE, LPS and TG, were significantly different in boars with high and low sperm motility. In addition, 195 important lipids and 334 important genes were identified by weighted gene coexpression analysis (WGCNA) and differential expression analysis. We observed that several important genes and lipids in SPEVs potentially influence sperm motility via glycerophospholipid metabolism, glycerolipid metabolism, the sphingolipid signaling pathway and the ferroptosis pathway. Furthermore, we found a significant correlation between the content of 22 lipids and the expression levels of 67 genes (|cor| > 0.8, P < 0.05). Moreover, we observed that three important gene-lipid linkages (CerG1 (d22:0/24:0) - RCAN3, Cer (d18:1/24:0) - SCFD2 and CerG1 (d18:0/24:1) - SCFD2) were strongly correlated with sperm motility. Based on the results, some genes and lipids in SPEVs may play important roles in sperm motility by interacting with sperm through important pathways.

Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma.

Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.

System analysis based on weighted gene co-expression analysis identifies SOX7 as a novel regulator of hepatic stellate cell activation and liver fibrosis.

Hepatic stellate cell (HSC) activation is the essential pathological process of liver fibrosis (LF). The molecular mechanisms regulating HSC activation and LF are incompletely understood. Here, we explored the effect of transcription factor SRY-related high mobility group box7 (SOX7) on HSC activation and LF, and the underlying molecular mechanism. We found the expression levels of SOX7 were decreased in human and mouse fibrotic livers, particularly at the fibrotic foci. SOX7 was also downregulated in primary activated HSCs and TGF-β1 stimulated LX-2 cells. SOX7 knockdown promoted activation and proliferation of LX-2 cells while inhibiting their apoptosis. On the other hand, overexpression of SOX7 suppressed the activation and proliferation of HSCs. Mechanistically, SOX7 attenuates HSC activation and LF by decreasing the expression of β-catenin and phosphorylation of Smad2 and Smad3 induced by TGF-β1. Furthermore, overexpression of SOX7 using AAV8-SOX7 mouse models ameliorated the extent of LF in response to CCl4 treatment invivo. Collectively, SOX7 suppressed HSC activation and LF. Targeting SOX7, therefore, could be a potential novel strategy to protect against LF.

An integration of neuroimaging and serum proteomics analysis suggests immune and inflammation are associated with white matter microstructure changes in cerebral small vessel disease with depressive symptoms

IntroductionDepressive symptoms are a common concomitant of cerebral small vessel disease (CSVD), of which pathogenesis requires more study. White matter microstructural abnormalities and proteomic alternation have been widely reported regarding depression in the elderly with CSVD. Exploring the relationship between cerebral white matter microstructural alterations and serum proteins may complete the explanation of molecular mechanisms for the findings from neuroimaging research of CSVD combined with depressive symptoms. MethodsAn untargeted proteomics approach based on mass spectrometry was used to obtain serum proteomic profiles, which were clustered into co-expression protein modules. White matter microstructural integrity was measured using the FMRIB Software Library (FSL) and MATLAB to analyze diffusion tensor imaging (DTI) data and calculate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for 50 regions of interest (ROI). Integrating the proteome with the DTI results, weighted gene co-expression analysis (WGCNA) was used to identify protein modules related to white matter microstructural alterations, and the proteins of the corresponding modules were analyzed for functional enrichment through bioinformatics techniques. ResultsDTI measurements were analCerebral small vessel disease (CSVD); Depression; Diffusion tensor imaging (DTI); Proteomics; Inflammationyzed between individuals with CSVD and depressive symptoms (CSVD+D) (n = 24) and those without depressive symptoms (CSVD-D) (n = 35). Results showed an overall increase in MD, AD, and RD within the left hemisphere of the CSVD+D group, suggesting widespread loss of white matter integrity and axonal demyelination, including left superior longitudinal fasciculus (SLF), left posterior corona radiata (PCR) and right external capsule (EC). We identified two protein modules associated with DTI diffusivity, and functional enrichment analyses revealed that complement and coagulation cascades and immune responses participate in the alternation of white matter microstructure in the CSVD+D group. ConclusionThe results suggested immune- and inflammation-related mechanism was associated with white matter microstructure changes in CSVD with depressive symptoms

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease.

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients. Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene. A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well. The NAP1L1 gene is a potential predictive biomarker for AD.

Transcriptomics-based exploration of shared M1-type macrophage-related biomarker in acute kidney injury after kidney transplantation and acute rejection after kidney transplantation

BackgroundMacrophage type 1 (M1) cells are associated with both acute kidney injury (AKI) during kidney transplantation and acute rejection (AR) after kidney transplantation. Our study explored M1-related biomarkers involved in both AKI and AR and their potential biological functions. MethodsBased on the Gene Expression Omnibus (GEO) database, the immune cell infiltration levels and differentially expressed genes were examined in AKI and AR in the kidney transplantation; M1-related genes shared in AKI and AR were identified using weighted gene co-expression analysis (WGCNA) system. Subsequently, protein-protein interaction (PPI) networks and machine learning methods to identify Hub genes and construct diagnostic models. Both AKI model and AR rat models were built to validate the expressions of Hub genes and test the injury phenotype, oxidative stress markers, and inflammatory factors. Finally, the transcription factor (TF)-Hub gene and micro-RNA (miRNA)-Hub gene regulatory networks were constructed based on identified Hub genes. ResultsOut of 2167 differential expression genes (DEGs) in AKI and 2100 DEGs in AR, four M1-related Hub genes were obtained by PPI networks and machine learning methods, namely GBP2, TYROBP, CCR5, and TLR8. The calibration curves in the nomogram diagnostic model for these four Hub genes suggested the same predictive probability as an ideal model for AKI and AR after kidney transplantation (AUC values of the area under the ROC curve were all >0.7). The same observations were confirmed in ischemia reperfusion injury (IRI) and AR rat models by identifying common four Hub genes (GBP2, TYROBP, TLR8, and CCR5). Western blots showed that these four Hub genes were significantly different in rat models of IRI and AR (all p<0.05). Compared with the control group, IRI and AR groups showed aggravated histopathological damage and increased secretion of oxidative stress markers and inflammatory factors in rat kidneys (all p<0.05). Finally, TF-Hub and miRNA-Hub gene regulatory networks were constructed to provide a theoretical basis for the regulation of Hub genes. ConclusionWe identified four macrophage M1-related Hub genes shared among AKI and AR after kidney transplantation. These genes may be considered for diagnosis of AKI and AR after kidney transplantation.

ZmPILS6 is an auxin efflux carrier required for maize root morphogenesis

Plant root systems play a pivotal role in plant physiology and exhibit diverse phenotypic traits. Understanding the genetic mechanisms governing root growth and development in model plants like maize is crucial for enhancing crop resilience to drought and nutrient limitations. This study focused on identifying and characterizing ZmPILS6, an annotated auxin efflux carrier, as a key regulator of various crown root traits in maize. ZmPILS6-modified roots displayed reduced network area and suppressed lateral root formation, which are desirable traits for the "steep, cheap, and deep" ideotype. The research revealed that ZmPILS6 localizes to the endoplasmic reticulum and plays a vital role in controlling the spatial distribution of indole-3-acetic acid (IAA or "auxin") in primary roots. The study also demonstrated that ZmPILS6 can actively efflux IAA when expressed in yeast. Furthermore, the loss of ZmPILS6 resulted in significant proteome remodeling in maize roots, particularly affecting hormone signaling pathways. To identify potential interacting partners of ZmPILS6, a weighted gene coexpression analysis was performed. Altogether, this research contributes to the growing knowledge of essential genetic determinants governing maize root morphogenesis, which is crucial for guiding agricultural improvement strategies.

Transcriptome analysis highlights the influence of temperature on hydrolase and traps in nematode-trapping fungi.

Pine wilt disease caused by Bursaphelenchus xylophilus poses a serious threat to the economic and ecological value of forestry. Nematode trapping fungi trap and kill nematodes using specialized trapping devices, which are highly efficient and non-toxic to the environment, and are very promising for use as biological control agents. In this study, we isolated several nematode-trapping fungi from various regions and screened three for their high nematocidal efficiency. However, the effectiveness of these fungi as nematicides is notably influenced by temperature and exhibits different morphologies in response to temperature fluctuations, which are categorized as "NA," "thin," "dense," and "sparse." The trend of trap formation with temperature was consistent with the trend of nematocidal efficiency with temperature. Both of which initially increased and then decreased with increasing temperature. Among them, Arthrobotrys cladodes exhibited the highest level of nematocidal activity and trap formation among the tested species. Transcriptome data were collected from A. cladodes with various trap morphologies. Hydrolase activity was significantly enriched according to GO and KEGG enrichment analyses. Eight genes related to hydrolases were found to be consistent with the trend of trap morphology with temperature. Weighted gene co-expression analysis and the Cytoscape network revealed that these 8 genes are associated with either mitosis or autophagy. This suggests that they contribute to the formation of "dense" structures in nematode-trapping fungi. One of these genes is the serine protein hydrolase gene involved in autophagy. This study reveals a potentially critical role for hydrolases in trap formation and nematocidal efficiency. And presents a model where temperature affects trap formation and nematocidal efficiency by influencing the serine protease prb1 involved in the autophagy process.

Early life adversity is associated with differential gene expression in immune cells: A cluster-based analysis across an acute psychosocial stressor

Elucidating mechanisms by which early-life adversity (ELA) contributes to increased disease risk is important for mitigating adverse health outcomes. Prior work has found differences in immune cell gene expression related to inflammation and mitochondrial activity. Using a within-person between-group experimental design, we investigated differences in gene expression clusters across acute psychosocial stress and no-stress conditions. Participants were young adults (N = 29, aged 18 – 25 years, 62 % female, 47 % with a history of ELA). Gene expression was assessed in peripheral blood mononuclear cells collected at 8 blood draws spanning two 5-hour sessions (stress vs. no-stress) separated by a week, 4 across each session (number of observations = 221). We applied two unsupervised gene clustering methods – latent profile analysis (LPA) and weighted gene co-expression analysis (WGCNA) – to cluster genes with similar expression patterns across participants. LPA identified 11 clusters, 7 of which were significantly associated with ELA-status. WGCNA identified 5 clusters, 3 of which were significantly associated with ELA-status. LPA- and WGCNA-identified clusters were correlated, and all clusters were highly preserved across sessions and time. There was no significant effect of acute stress on cluster gene expression, but there was a significant effect of time, and significant differences by ELA-status. ELA-associated clusters related to RNA splicing/processing, inflammation, leukocyte differentiation and division, and mitochondrial activity were differentially expressed across time: ELA-exposed individuals showed decreased expression of these clusters at 90-minutes while controls showed increased expression. Our findings replicate previous work in this area and highlight additional mechanisms by which ELA may contribute to disease risk.

Unraveling the Heterogeneity of CD8+ T-Cell Subsets in Liver Cirrhosis: Implications for Disease Progression.

: Liver cirrhosis involves chronic inflammation and progressive fibrosis. Among various immune cells, CD8+ T cells are considered a major contributor to hepatic inflammation and fibrosis. However, the exact molecular pathways governing CD8+ T-cell-mediated effects in cirrhosis remain unclear. : This study analyzed transcriptomic and single-cell sequencing data to elucidate CD8+ T-cell heterogeneity and implications in cirrhosis. : Weighted gene co-expression analysis of bulk RNA-seq data revealed an association between cirrhosis severity and activated T-cell markers like HLA and chemokine genes. Furthermore, single-cell profiling uncovered eight CD8+ T-cell subtypes, notably, effector memory (Tem) and exhausted (Tex) T cells. Tex cells, defined by PDCD1, LAG3, and CXCL13 expression, were increased in cirrhosis, while Tem cells were decreased. Lineage tracing and differential analysis highlighted CXCL13+ Tex cells as a terminal, exhausted subtype of cells with roles in PD-1 signaling, glycolysis, and T-cell regulation. CXCL13+ Tex cells displayed T-cell exhaustion markers like PDCD1, HAVCR2, TIGIT, and TNFRSF9. Functional analysis implicated potential roles of these cells in immunosuppression. Finally, a CXCL13+ Tex-cell gene signature was found that correlated with cirrhosis severity and poorer prognosis of liver cancer. : In summary, this comprehensive study defines specialized CD8+ T-cell subpopulations in cirrhosis, with CXCL13+ Tex cells displaying an exhausted phenotype associated with immune dysregulation and advanced disease. Key genes and pathways regulating these cells present potential therapeutic targets.

Accumulation mechanism of metabolite markers identified by machine learning between Qingyuan and Xiushui counties in Polygonatum cyrtonema Hua

Polygonatum cyrtonema Hua is a traditional Chinese medicinal plant acclaimed for its therapeutic potential in diabetes and various chronic diseases. Its rhizomes are the main functional parts rich in secondary metabolites, such as flavonoids and saponins. But their quality varies by region, posing challenges for industrial and medicinal application of P. cyrtonema. In this study, 482 metabolites were identified in P. cyrtonema rhizome from Qingyuan and Xiushui counties. Cluster analysis showed that samples between these two regions had distinct secondary metabolite profiles. Machine learning methods, specifically support vector machine-recursive feature elimination and random forest, were utilized to further identify metabolite markers including flavonoids, phenolic acids, and lignans. Comparative transcriptomics and weighted gene co-expression analysis were performed to uncover potential candidate genes including CHI, UGT1, and PcOMT10/11/12/13 associated with these compounds. Functional assays using tobacco transient expression system revealed that PcOMT10/11/12/13 indeed impacted metabolic fluxes of the phenylpropanoid pathway and phenylpropanoid-related metabolites such as chrysoeriol-6,8-di-C-glucoside, syringaresinol-4'-O-glucopyranosid, and 1-O-Sinapoyl-D-glucose. These findings identified metabolite markers between these two regions and provided valuable genetic insights for engineering the biosynthesis of these compounds.

Lysosome-related biomarkers in preeclampsia and cancers: Machine learning and bioinformatics analysis

BackgroundLysosomes serve as regulatory hubs, and play a pivotal role in human diseases. However, the precise functions and mechanisms of action of lysosome-related genes remain unclear in preeclampsia and cancers. This study aimed to identify lysosome-related biomarkers in preeclampsia, and further explore the biomarkers shared between preeclampsia and cancers. Materials and methodsWe obtained GSE60438 and GSE75010 datasets from the Gene Expression Omnibus database, pre-procesed them and merged them into a training cohort. The limma package in R was used to identify the differentially expressed mRNAs between the preeclampsia and normal control groups. Differentially expressed lysosome-related genes were identified by intersecting the differentially expressed mRNAs and lysosome-related genes obtained from Gene Ontology and GSEA databases. Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed using the DAVID database. The CIBERSORT method was used to analyze immune cell infiltration. Weighted gene co-expression analyses and three machine learning algorithm were used to identify lysosome-related diagnostic biomarkers. Lysosome-related diagnostic biomarkers were further validated in the testing cohort GSE25906. Nomogram diagnostic models for preeclampsia were constructed. In addition, pan-cancer analysis of lysosome-related diagnostic biomarkers were identified by was performed using the TIMER, Sangebox and TISIDB databases. Finally, the Drug-Gene Interaction, TheMarker and DSigDB Databases were used for drug-gene interactions analysis. ResultsA total of 11 differentially expressed lysosome-related genes were identified between the preeclampsia and control groups. Three molecular clusters connected to lysosome were identified, and enrichment analysis demonstrated their strong relevance to the development and progression of preeclampsia. Immune infiltration analysis revealed significant immunity heterogeneity among different clusters. GBA, OCRL, TLR7 and HEXB were identified as lysosome-related diagnostic biomarkers with high AUC values, and validated in the testing cohort GSE25906. Nomogram, calibration curve, and decision curve analysis confirmed the accuracy of predicting the occurrence of preeclampsia based on OCRL and HEXB. Pan-cancer analysis showed that GBA, OCRL, TLR7 and HEXB were associated with the prognosis of patients with various tumors and tumor immune cell infiltration. Twelve drugs were identified as potential drugs for the treatment of preeclampsia and cancers. ConclusionThis study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.

Spatiotemporal transcriptomic plasticity in barley roots: unravelling water deficit responses in distinct root zones

BackgroundDrought poses a major threat to agricultural production and thus food security. Understanding the processes shaping plant responses to water deficit is essential for global food safety. Though many studies examined the effect of water deficit on the whole-root level, the distinct functions of each root zone and their specific stress responses remain masked by this approach.ResultsIn this study, we investigated the effect of water deficit on root development of the spring barley (Hordeum vulgare L.) cultivar Morex and examined transcriptomic responses at the level of longitudinal root zones. Water deficit significantly reduced root growth rates after two days of treatment. RNA-sequencing revealed root zone and temporal gene expression changes depending on the duration of water deficit treatment. The majority of water deficit-regulated genes were unique for their respective root zone-by-treatment combination, though they were associated with commonly enriched gene ontology terms. Among these, we found terms associated with transport, detoxification, or cell wall formation affected by water deficit. Integration of weighted gene co-expression analyses identified differential hub genes, that highlighted the importance of modulating energy and protein metabolism and stress response.ConclusionOur findings provide new insights into the highly dynamic and spatiotemporal response cascade triggered by water deficit and the underlying genetic regulations on the level of root zones in the barley cultivar Morex, providing potential targets to enhance plant resilience against environmental constraints. This study further emphasizes the importance of considering spatial and temporal resolution when examining stress responses.

The changes of peripheral blood hub genes in 24-week-old APP/PS1/Tau triple transgenic mouse model based on weighted gene co-expression network analysis.

Peripheral regulation emerges as a promising intervention in the early stages of Alzheimer's disease (AD). The hub genes in the peripheral blood of MCI patients from GEO database (GSE63060, GSE63061) were screened using weighted gene co-expression analysis (WGCNA). Meanwhile, behavioral tests, HE staining and Nissl staining were used to detect the memory impairment and histopathological changes in 24-week-old male 3×Tg-AD mice. Thioflavin-S and immunohistochemical staining were used to determine the Aβ deposition in both intracellular and extracellular neurons. Subsequently, the MCI-hub genes were verified by quantitative real-time PCR (qRT-PCR) in the peripheral blood of 3×Tg-AD mice. The research revealed ten hub genes associated with MCI were identified WGCNA. Short-term memory loss, intracellular Aβ deposition and limited of extracellular amyloid plaques in 3×Tg-AD mice. The qRT-PCR analysis of peripheral blood from these mice revealed significantly down-regulation in the expression levels of ATP5C1, ITGB2, EFTUD2 and RPS27A genes; whereas the expression level of VCP gene was significantly up-regulated. These findings confirmed that 24-week-old male 3×Tg-AD mice were a valuable animal model for simulating the early symptomatic stages of AD. Additionally, the peripheral blood MCI-hub genes related to immune response, energy metabolism and ribosomal coding efficiency provide potential biomarkers for this stage.

MeJA-induced hairy roots in Plumbago auriculata L. by RNA-seq profiling and key synthase provided new insights into the sustainable production of plumbagin and saponins.

Naturally synthesized secondary metabolites in plants are considered an important source of drugs, food additives, etc. Among them, research on natural plant medicinal components and their synthesis mechanisms has always been of high concern. We identified a novel medicinal floral crop, Plumbago auriculata L., that can be treated with methyl jasmonate (MeJA) for the rapid or sustainable production of natural bioactives from hairy roots. In the study, we globally analyzed the changes in the accumulation of plumbagin and others in the hairy roots of Plumbago auriculata L. hairy roots (PAHR) 15834 in P. auriculata L. based on 100 μmol/L of MeJA treatment by RNA-seq profiling, and we found that there was a significant increase in the accumulation of plumbagin and saponin before 24h. To explain the principle of co-accumulation, it showed that MeJA induced JA signaling and the shikimic acid pathway, and the methylvaleric acid (MVA) pathway was activated downstream subsequently by the Mfuzz and weighted gene co-expression analysis. Under the shared metabolic pathway, the high expression of PAL3 and HMGR promoted the activity of the "gateway enzymes" phenylalanine ammonia lyase (PAL) and 3-hydroxy-3-methylglutaryl CoA reductase (HMGR), which respectively induced the high expression of key reaction enzyme genes, including chalcone synthase (CHS), isopentenyl diphosphate (IPP), and farnesyl pyrophosphate synthase (FPS), that led to the synthesis of plumbagin and saponin. We speculated that large amounts of ketones and/or aldehydes were formed under the action of these characteristic enzymes, ultimately achieving their co-accumulation through polyketone and high-level sugar and amino acid metabolism. The study results provided a theoretical basis for carrying out the factory refinement and biosynthesis of plumbagin and saponins and also provided new ideas for fully exploiting multifunctional agricultural crops and plants and developing new agricultural by-products.

Triosephosphate isomerase 1 may be a risk predictor in laryngeal squamous cell carcinoma: a multi-centered study integrating bulk RNA, single-cell RNA, and protein immunohistochemistry

BackgroundAlthough great progress has been made in anti-cancer therapy, the prognosis of laryngeal squamous cell carcinoma (LSCC) patients remains unsatisfied. Quantities of studies demonstrate that glycolytic reprograming is essential for the progression of cancers, where triosephosphate isomerase 1 (TPI1) serves as a catalytic enzyme. However, the clinicopathological significance and potential biological functions of TPI1 underlying LSCC remains obscure.MethodsWe collected in-house 82 LSCC tissue specimens and 56 non-tumor tissue specimens. Tissue microarrays (TMA) and immunohistochemical (IHC) experiments were performed. External LSCC microarrays and bulk RNA sequencing data were integrated to evaluate the expression of TPI1. We used a log-rank test and the CIBERSORT algorithm to assess the prognostic value of TPI1 and its association with the LSCC microenvironment. Malignant laryngeal epithelial cells and immune-stromal cells were identified using inferCNV and CellTypist. We conducted a comprehensive analysis to elucidate the molecular functions of TPI1 in LSCC tissue and single cells using Pearson correlation analysis, high dimensional weighted gene co-expression analysis, gene set enrichment analysis, and clustered regularly interspaced short palindromic repeats (CRISPR) screen. We explored intercellular communication patterns between LSCC single cells and immune-stromal cells and predicted several therapeutic agents targeting TPI1.ResultsBased on the in-house TMA and IHC analysis, TPI1 protein was found to have a strong positive expression in the nucleus of LSCC cells but only weakly positive activity in the cytoplasm of normal laryngeal cells (p < 0.0001). Further confirmation of elevated TPI1 mRNA expression was obtained from external datasets, comparing 251 LSCC tissue samples to 136 non-LSCC tissue samples (standardized mean difference = 1.06). The upregulated TPI1 mRNA demonstrated a high discriminative ability between LSCC and non-LSCC tissue (area under the curve = 0.91; sensitivity = 0.87; specificity = 0.79), suggesting its potential as a predictive marker for poor prognosis (p = 0.037). Lower infiltration abundance was found for plasma cells, naïve B cells, monocytes, and neutrophils in TPI-high expression LSCC tissue. Glycolysis and cell cycle were significantly enriched pathways for both LSCC tissue and single cells, where heat shock protein family B member 1, TPI1, and enolase 1 occupied a central position. Four outgoing communication patterns and two incoming communication patterns were identified from the intercellular communication networks. TPI1 was predicted as an oncogene in LSCC, with CRISPR scores less than -1 across 71.43% of the LSCC cell lines. TPI1 was positively correlated with the half maximal inhibitory concentration of gemcitabine and cladribine.ConclusionsTPI1 is dramatically overexpressed in LSCC than in normal tissue, and the high expression of TPI1 may promote LSCC deterioration through its metabolic and non-metabolic functions. This study contributes to advancing our knowledge of LSCC pathogenesis and may have implications for the development of targeted therapies in the future.

Co-Expression Network Analysis of the Transcriptome Identified Hub Genes and Pathways Responding to Saline–Alkaline Stress in Sorghum bicolor L.

Soil salinization, an intractable problem, is becoming increasingly serious and threatening fragile natural ecosystems and even the security of human food supplies. Sorghum (Sorghum bicolor L.) is one of the main crops growing in salinized soil. However, the tolerance mechanisms of sorghum to saline–alkaline soil are still ambiguous. In this study, RNA sequencing was carried out to explore the gene expression profiles of sorghum treated with sodium bicarbonate (150 mM, pH = 8.0, treated for 0, 6, 12 and 24 h). The results show that 6045, 5122, 6804, 7978, 8080 and 12,899 differentially expressed genes (DEGs) were detected in shoots and roots after 6, 12 and 24 h treatments, respectively. GO, KEGG and weighted gene co-expression analyses indicate that the DEGs generated by saline–alkaline stress were primarily enriched in plant hormone signal transduction, the MAPK signaling pathway, starch and sucrose metabolism, glutathione metabolism and phenylpropanoid biosynthesis. Key pathway and hub genes (TPP1, WRKY61, YSL1 and NHX7) are mainly related to intracellular ion transport and lignin synthesis. The molecular and physiological regulation processes of saline–alkali-tolerant sorghum are shown by these results, which also provide useful knowledge for improving sorghum yield and quality under saline–alkaline conditions.