Gene Co-expression Modules Research Articles

Response of young rice panicles to salt stress: insights based on phenotype and transcriptome analysis

Saline-alkali soils limit rice growth and production. With an increasing global population, enhancing rice salt tolerance is crucial for improving yields in these areas. This study investigated the developmental characteristics of young panicles and pollen fertility in two rice varieties, 58M and 58L, under salt stress. Results showed that 58M had more substantial salt tolerance during panicle development. RNA sequencing of 18 samples from both varieties under high salt stress (0 h, 6 h, and 24 h) identified 469 common differentially expressed genes (DEGs) and 2,308 DEGs between the varieties. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment highlighted significant pathways such as phenylpropanoid biosynthesis, protein processing, and flavonoid biosynthesis. Six gene co-expression modules related to salt tolerance were identified, with six candidate genes (LOC_Os05g38530, LOC_Os04g07920, LOC_Os12g02105, LOC_Os01g06580, LOC_Os06g49250, and LOC_Os06g48300) potentially linked to salt tolerance. These findings provide insights into rice salt tolerance mechanisms and offer new genetic resources for breeding salt-tolerant rice.

Employing single-cell RNA sequencing coupled with an array of bioinformatics approaches to ascertain the shared genetic characteristics between osteoporosis and obesity.

This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes. WGCNA revealed critical gene modules for OB and OP, identifying the Toll-like receptor (TLR) signalling pathway as a common factor. TLR2 was the most significant gene, with a pronounced expression in macrophages. Elevated TLR2 expression correlated with increased adipose accumulation, inflammation, and osteoclast differentiation, linking it to OP development. Our study underscores the pivotal role of TLR2 in connecting OP and OB. It highlights the influence of TLR2 in macrophages, driving both diseases through a pro-inflammatory mechanism. These insights propose TLR2 as a potential dual therapeutic target for treating OP and OB.

Integrative genomics approach identifies glial transcriptomic dysregulation and risk in the cortex of individuals with Alcohol Use Disorder.

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Past molecular studies of AUD used underpowered single cell analysis or bulk homogenates of postmortem brain tissue, which obscures gene expression changes in specific cell types. Here we performed single nuclei RNA-sequencing analysis of 73 post-mortem samples from individuals with AUD (N=36, Nnuclei= 248,873) and neurotypical controls (N=37, Nnuclei= 210,573) in both sexes across two institutional sites. We identified 32 clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glia. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and co-expression modules linked to AUD. Analysis for cell type-specific enrichment of aggregate genetic risk for AUD identified subtypes of glial and neuronal cell types as potential key players not only affected by but causally linked to the progression of AUD. Most of the enrichments for genetic risk for AUD were in glial cell types. These results highlight the importance of cell-type specific molecular changes in AUD and offer opportunities to identify novel targets for treatment.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets.

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases. This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process. The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators. This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

At the core of salinity: Divergent transcriptomic responses to neutral and alkaline salinity in Arabidopsis thaliana

In the context of current climate change, alkaline salinity is increasingly challenging crop yields, especially in arid and semiarid regions. Alkaline salinity is more detrimental to plant performance than neutral salinity and tolerance to neutral salinity may not confer tolerance to alkaline salinity. The mechanisms behind are still poorly understood. This study aims to identify physiological and genetic traits underlying this differential tolerance to neutral and alkaline salinity by exploiting the variation present in natural populations (demes) of Arabidopsis thaliana. Growth, photosynthesis, phytohormone and mineral nutrient profiles, plant water status and transcriptomic changes were analyzed in four demes with contrasting tolerance to neutral and alkaline salinity. Results of this novel holistic approach suggest low internal Fe use efficiency caused by bicarbonate as a driver of enhanced sensitivity to alkaline salinity in plants adapted to neutral salinity prompting photosynthesis inhibition and alteration of the plant’s carbon budget for primary and secondary metabolism. Moreover, alkaline salinity specifically altered the auxin and jasmonic acid signaling pathways, while sustained ABA biosynthesis was an adaptive trait under neutral salinity. Exploring the genes with non-shared expression trends between salinity types, we identified sequence variation at the BGAL4 locus associated with advantageous responses to each type of salinity. Weighted correlation network analysis (WGCNA) validated the significant involvement of gene co-expression modules targeted by the enrichment analyses, highlighting the hubs correlated with favorable responses to both salinity types. Overall, the present study points out the complex physiological and genetic mechanisms responsible for plant tolerance to alkaline salinity and proposes target genes for breeding strategies under alkaline saline soils.

Identification of immune infiltration and PANoptosis-related molecular clusters and predictive model in Alzheimer's disease based on transcriptome analysis.

This study aims to explore the expression profile of PANoptosis-related genes (PRGs) and immune infiltration in Alzheimer's disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed PRGs and immune cell infiltration in AD and explored related molecular clusters. Gene set variation analysis (GSVA) was used to analyze the expression of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in different clusters. Weighted gene co-expression network analysis was utilized to find co-expressed gene modules and core genes in the network. By analyzing the intersection genes in random forest, support vector machine, generalized linear model, and extreme gradient boosting (XGB), the XGB model was determined. Eventually, the first five genes (Signal Transducer and Activator of Transcription 3, Tumor Necrosis Factor (TNF) Receptor Superfamily Member 1B, Interleukin 4 Receptor, Chloride Intracellular Channel 1, TNF Receptor Superfamily Member 10B) in XGB model were selected as predictive genes. This research explored the relationship between PANoptosis and AD and established an XGB learning model to evaluate and screen key genes. At the same time, immune infiltration analysis showed that there were different immune infiltration expression profiles in AD.

The molecular mechanism of ferroptosis-induced spontaneous abortion based on bioinformatics approach.

Spontaneous abortion (SA) is a prevalent placental dysfunction, and ferroptosis may play a crucial role in placental dysfunction and the development of SA. In this study, we employed data mining and analysis techniques to investigate the biological mechanism of SA induced by ferroptosis, resulting in the identification of a total of 79 ferroptosis-related genes in SA were identified. Among them, 3 co-expression modules of ferroptosis risk genes, ten significant functions and six biologically significant pathways were obtained 61 pairs of differentially expressed miRNA-ferroptosis factor relationships were identified, and WIPI1 and GSN were expressed at significantly higher levels in SA. This is extremely helpful for future research on SA.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Profiling of Phakopsora pachyrhizi transcriptome revealed co-expressed virulence effectors as prospective RNA interference targets for soybean rust management.

Soybean rust (SBR), caused by an obligate biotrophic pathogen Phakopsora pachyrhizi, is a devastating disease of soybean worldwide. However, the mechanisms underlying plant invasion by P. pachyrhizi are poorly understood, which hinders the development of effective control strategies for SBR. Here we performed detailed histological characterization on the infection cycle of P. pachyrhizi in soybean and conducted a high-resolution transcriptional dissection of P. pachyrhizi during infection. This revealed P. pachyrhizi infection leads to significant changes in gene expression with 10 co-expressed gene modules, representing dramatic transcriptional shifts in metabolism and signal transduction during different stages throughout the infection cycle. Numerous genes encoding secreted protein are biphasic expressed, and are capable of inhibiting programmed cell death triggered by microbial effectors. Notably, three co-expressed P. pachyrhizi apoplastic effectors (PpAE1, PpAE2, and PpAE3) were found to suppress plant immune responses and were essential for P. pachyrhizi infection. Double-stranded RNA coupled with nanomaterials significantly inhibited SBR infection by targeting PpAE1, PpAE2, and PpAE3, and provided long-lasting protection to soybean against P. pachyrhizi. Together, this study revealed prominent changes in gene expression associated with SBR and identified P. pachyrhizi virulence effectors as promising targets of RNA interference-based soybean protection strategy against SBR.

Integrated multi-omic high-throughput strategies across-species identified potential key diagnostic, prognostic, and therapeutic targets for atherosclerosis under high glucose conditions.

Diabetes is a well-known risk factor for atherosclerosis (AS), but the underlying molecular mechanism remains unknown. The dysregulated immune response is an important reason. High glucose is proven to induce foam cell formation under lipidemia situations in clinical patients. Exploring the potential regulatory programs of accelerated foam cell formation stimulated by high glucose is meaningful. Macrophage-derived foam cells were induced in vitro, and high-throughput sequencing was performed. Coexpression gene modules were constructed using weighted gene co-expression network analysis (WGCNA). Highly related modules were identified. Hub genes were identified by multiple integrative strategies. The potential roles of selected genes were further validated in bulk-RNA and scRNA datasets of human plaques. By transfection of the siRNA, the role of the screened gene during foam cell formation was further explored. Two modules were found to be both positively related to high glucose and ox-LDL. Further enrichment analyses confirmed the association between the brown module and AS. The high correlation between the brown module and macrophages was identified and 4 hub genes (Aldoa, Creg1, Lgmn, and Pkm) were screened. Further validation in external bulk-RNA and scRNA revealed the potential diagnostic and therapeutic value of selected genes. In addition, the survival analysis confirmed the prognostic value of Aldoa while knocking down Aldoa expression alleviated the foam cell formation in vitro. We systematically investigated the synergetic effects of high glucose and ox-LDL during macrophage-derived foam cell formation and identified that ALDOA might be an important diagnostic, prognostic, and therapeutic target in these patients.

Identification of susceptibility modules and characteristic genes to osteoarthritis by WGCNA.

The susceptibility modules and characteristic genes of patients with osteoarthritis (OA) were determined by weighted gene co-expression network analysis (WGCNA), and the role of immune cells in OA related microenvironment was analyzed. GSE98918 and GSE117999 data sets are from GEO database. R language was used to conduct difference analysis for the new data set after merging. The formation of gene co-expression network, screening of susceptibility modules and screening of core genes are all through WGCNA. GO and KEGG enrichment analyses were used for Hub genes. The characteristic genes of the disease were obtained by Lasso regression screening. SSGSEA was used to estimate immune cell abundance in sample and a series of correlation analyses were performed. WGCNA was used to form 6 gene co-expression modules. The yellow-green module is identified as the susceptible module of OA. 202 genes were identified as core genes. Finally, RHOT2, FNBP4 and NARF were identified as the characteristic genes of OA. The results showed that the characteristic genes of OA were positively correlated with plasmacytoid dendritic cells, NKT cells and immature dendritic cells, but negatively correlated with active B cells. MDSC were the most abundant immune cells in cartilage. This study identified the Hippo signaling pathway, mTOR signaling pathway, and three characteristic genes (RHOT2, FNBP4, NARF) as being associated with osteoarthritis (OA). These three genes are downregulated in the cartilage of OA patients and may serve as biomarkers for early diagnosis and targeted therapy. Proper regulation of immune cells may aid in the treatment of OA. Future research should focus on developing tools to detect these genes and exploring their therapeutic applications.

Analysis of Human Papillomavirus-Associated Cervical Cancer Differentially Expressed Genes and Identification of Prognostic Factors using Integrated Bioinformatics Approaches

Background: Human papillomavirus (HPV)-induced cervical cancer progresses through a series of steps. Despite our limited understanding of the mechanisms driving this progression, identifying the key genes involved could significantly improve early detection and treatment. Materials and Methods: Two gene expression profiles of GSE9750 and GSE6791, which included cervical cancer HPV-positive and -negative samples, were evaluated using the R limma package with established cut-off criteria of P value < 0.05 and | fold change| ≥ 1. KEGG pathway enrichment was performed to identify potential pathways. Weighted gene co-expression network analysis (WGCNA) was used to discover co-expressed gene modules and trait–module connections. Results: Considering the defined criteria, 115 differentially expressed genes (DEGs) were identified. The DEG’s KEGG pathway enrichment analysis revealed enrichment in highly relevant pathways to the HPV infection, including cell cycle, viral carcinogenesis, autophagy-animal, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, and microRNAs in cancer. WGCNA results in 13 co-expression modules, and the magenta module is identified with significant relations to HPV, cervical cancer stage, and metastasis traits. The survival analysis identified BEX1 and CDC45 as potential prognostic factors in HPV-associated cervical cancer. Conclusion: The innovation of our work lies in identifying essential genes associated with the multi-step process of cervical carcinogenesis. In fact, the current study has the potential to give a distinct viewpoint on the molecular pathways linked to cervical cancer. Considering the potential importance of the hub genes, we recommend conducting in-depth wet lab research to determine their impact on the biological mechanisms of cervical cancer.

Duodenal transcriptomics demonstrates signatures of tissue inflammation and immune cell infiltration in children with environmental enteric dysfunction across global centers

BackgroundEnvironmental enteric dysfunction (EED) is an inflammatory condition of the small intestine that is prevalent in children residing in low- and middle-income countries. EED is accompanied by profound histopathologic changes in the small bowel, loss of absorptive capacity, increased intestinal permeability, increased microbial translocation, and nutrient loss. ObjectivesWe sought to identify dysregulated genes and pathways that might underlie pediatric EED. MethodsRNA-sequencing libraries were generated from endoscopically obtained duodenal tissue from undernourished children with EED from 3 prospective cohorts of children with EED. The EED transcriptome was defined in comparison to North American children without EED. Weighted gene coexpression network analysis (WGCNA) was tested for gene modules associated with EED and its histologic features. ResultsThe 1784 upregulated genes in EED were highly enriched for immune and inflammatory processes, including IL-17 and JAK-STAT signaling, and cytokine–cytokine receptor interactions. The 1388 downregulated genes included genes corresponding to xenobiotic metabolism, detoxification, and antioxidant capacities. A gene coexpression module enriched for antimicrobial responses and chemokine activity was significantly associated with villous blunting, goblet cell depletion, and overall histologic severity of EED. ConclusionsThe transcriptome signatures of EED include specific innate and adaptive immune responses that are consistently elevated across study centers, coupled with reduced detoxification and antioxidant capacities. These data may have implications for targeted interventions to improve EED outcomes.

Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers

BackgroundIn recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored.MethodThe xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model.ResultsIn this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC.ConclusionThis 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection.

High Glycolytic Activity Signature Reveals CCNB2 as a Key Therapeutic Target in Triple-Negative Breast Cancer.

Aerobic glycolysis and the cell cycle are well-established tumor hallmarks. Understanding their relationship could help to unravel the pathogenic mechanisms of breast cancer (BC) and suggest potential new strategies for treatment. Glycolysis-related genes (GRGs) were downloaded from the Reactome database and screened using univariate Cox analysis. The consensus clustering method was employed to identify a glycolytic activity signature (GAS) using the Gene Expression Omnibus (GEO) dataset. A nomogram risk prediction model was constructed using coefficients from univariate Cox analysis. Immune cell infiltration was evaluated using single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE algorithm. Gene co-expression modules were created using weighted correlation network analysis (WGCNA) to identify hub genes. Gene expression in three BC cell lines was quantified using Quantitative Reverse Transcriptase Polymera (qRT-PCR). Single-cell RNA sequencing (scRNA-seq) data was used to examine the relationship between GAS and hub genes. The sensitivity of different groups to cell cycle-related clinical drugs was also examined. BC with high GAS (HGAS) showed high tumor grade and recurrence rate. HGAS was a prognostic indicator of worse overall survival (OS) in BC patients. HGAS BC showed more abundant immune cells and significantly higher expression of immunomodulators compared to BC with low GAS (LGAS). HGAS BC also showed enhanced cell cycle pathway, with high mRNA and protein expression levels of Cyclin B2 (CCNB2), a key component of the cell cycle pathway. Importantly, scRNA-seq analysis revealed that elevated CCNB2 expression was positively correlated with HGAS in triple-negative BC (TNBC). This was validated in clinical samples from TNBC patients. High expression of CCNB2 was found in three BC cell lines, and was also an indicator of poor prognosis. HGAS BC showed high sensitivity to several cell cycle-related clinical drugs, with 9 of these also showing activity in BC with high CCNB2 expression. HGAS was associated with enhanced cell cycle pathway and immune activity in BC. These results suggest that CCNB2 is a potential key therapeutic target in BC patients.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

PTBP1-mediated repression of neuron-specific CDC42 splicing constitutes a genomic alteration-independent, developmentally conserved vulnerability in IDH-wildtype glioblastoma.

Gene co-expression networks may encode hitherto inadequately recognized vulnerabilities for adult gliomas. By identifying evolutionally conserved gene co-expression modules around EGFR (EM) or PDGFRA (PM), we recently proposed an EM/PM classification scheme, which assigns IDH-wildtype glioblastomas (GBM) into the EM subtype committed in neural stem cell compartment, IDH-mutant astrocytomas and oligodendrogliomas into the PM subtype committed in early oligodendrocyte lineage. Here, we report the identification of EM/PM subtype-specific gene co-expression networks and the characterization of hub gene polypyrimidine tract-binding protein 1 (PTBP1) as a genomic alteration-independent vulnerability in IDH-wildtype GBM. Supervised by the EM/PM classification scheme, we applied weighted gene co-expression network analysis to identify subtype-specific global gene co-expression modules. These gene co-expression modules were characterized for their clinical relevance, cellular origin and conserved expression pattern during brain development. Using lentiviral vector-mediated constitutive or inducible knockdown, we characterized the effects of PTBP1 on the survival of IDH-wildtype GBM cells, which was complemented with the analysis of PTBP1-depedent splicing pattern and overexpression of splicing target neuron-specific CDC42 (CDC42-N) isoform. Transcriptomes of adult gliomas can be robustly assigned into 4 large gene co-expression modules that are prognostically relevant and are derived from either malignant cells of the EM/PM subtypes or tumor microenvironment. The EM subtype is associated with a malignant cell-intrinsic gene module involved in pre-mRNA splicing, DNA replication and damage response, and chromosome segregation, and a microenvironment-derived gene module predominantly involved in extracellular matrix organization and infiltrating immune cells. The PM subtype is associated with two malignant cell-intrinsic gene modules predominantly involved in transcriptional regulation and mRNA translation, respectively. Expression levels of these gene modules are independent prognostic factors and malignant cell-intrinsic gene modules are conserved during brain development. Focusing on the EM subtype, we identified PTBP1 as the most significant hub for the malignant cell-intrinsic gene module. PTBP1 is not altered in most glioma genomes. PTBP1 represses the conserved splicing of CDC42-N. PTBP1 knockdown or CDC42-N overexpression disrupts actin cytoskeleton dynamics, causing accumulation of reactive oxygen species and cell apoptosis. PTBP1-mediated repression of CDC42-N splicing represents a potential genomic alteration-independent, developmentally conserved vulnerability in IDH-wildtype GBM.

Integrating GWAS with a gene co-expression network better prioritizes candidate genes associated with root metaxylem phenes in maize.

Root metaxylems are phenotypically diverse structures whose function is particularly important under drought stress. Significant research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but that of monocots are relatively underexplored. In maize (Zea mays), a robust pipeline integrated a genome-wide association study (GWAS) of root metaxylem phenes under well-watered and water-stress conditions with a gene co-expression network to prioritize the strongest gene candidates. We identified 244 candidate genes by GWAS, of which 103 reside in gene co-expression modules most relevant to xylem development. Several candidate genes may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Of those, six gene candidates were detected in multiple root metaxylem phenes in both well-watered and water-stress conditions. We posit that candidate genes that are more essential to network function based on gene co-expression (i.e., hubs or bottlenecks) should be prioritized and classify 33 essential genes for further investigation. Our study demonstrates a new strategy for identifying promising gene candidates and presents several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals.

Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis.

Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining lung homeostasis. However, a comprehensive investigation into their cellular diversity, molecular features, and clinical implications is currently lacking. In this study, we conducted a comprehensive examination of single-cell RNA sequencing data from both normal and fibrotic lung tissues. We analyzed alterations in cellular composition between IPF and normal tissue and investigated differentially expressed genes across each cell population. This analysis revealed the presence of two distinct subpopulations of IPF-related alveolar epithelial type II cells (IR_AT2). Subsequently, three unique gene co-expression modules associated with the IR_AT2 subtype were identified through the use of hdWGCNA. Furthermore, we refined and identified IPF-related AT2-related gene (IARG) signatures using various machine learning algorithms. Our analysis demonstrated a significant association between high IARG scores in IPF patients and shorter survival times (p-value < 0.01). Additionally, we observed a negative correlation between the percent predicted diffusing capacity for lung carbon monoxide (% DLCO) and increased IARG scores (cor = -0.44, p-value < 0.05). The cross-validation findings demonstrated a high level of accuracy (AUC > 0.85, p-value < 0.01) in the prognostication of patients with IPF utilizing the identified IARG signatures. Our study has identified distinct molecular and biological features among AT2 subpopulations, specifically highlighting the unique characteristics of IPF-related AT2 cells. Importantly, our findings underscore the prognostic relevance of specific genes associated with IPF-related AT2 cells, offering valuable insights into the advancement of IPF.

Genetic parameters, genome-wide association study, and selection perspective on gestation length in 16 French cattle breeds

Lifetime productivity is a trait of great importance to dairy cattle populations as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics data sets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the Restricted Maximum Likelihood (REML) method. Genome-wide association studies (GWAS) and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-seq data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE) while the genetic correlation with 305d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle and important candidate genes were identified by integrating genomic and transcriptomic data sets.