Gene Co-expression Analysis Research Articles

Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma.

Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events and the primary signaling center are co-localized in the ancestral dGRN; remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation.

Molecular Regulation of Photosynthetic Carbon Assimilation in Oat Leaves Under Drought Stress

Common oat (Avena sativa L.) is one of the important minor grain crops in China, and drought stress severely affects its yield and quality. To investigate the drought resistance characteristics of oat seedlings, this study used Baiyan 2, an oat cultivar at the three-leaf stage, as the experimental material. Drought stress was simulated using polyethylene glycol (PEG) to treat the seedlings. The photosynthetic parameters and physicochemical indices of the treatment groups at 6 h and 12 h were measured and compared with the control group at 0 h. The results showed that drought stress did not significantly change chlorophyll content, but it significantly reduced net photosynthetic rate and other photosynthetic parameters while significantly increasing proline content. Transcriptome analysis was conducted using seedlings from both the control and treatment groups, comparing the two treatment groups with the control group using Tbtool software (v2.136). This analysis identified 344 differentially expressed genes. Enrichment analysis of these differentially expressed genes revealed significant enrichment in physiological pathways such as photosynthesis and ion transport. Ten differentially expressed genes related to the physiological process of photosynthetic carbon assimilation were identified, all of which were downregulated. Additionally, seven differentially expressed genes were related to ion transport. Through gene co-expression analysis combined with promoter region structure analysis, 11 transcription factors (from MYB, AP2/ERF, C2C2-dof) were found to regulate the expression of 10 genes related to photosynthetic carbon assimilation. Additionally, five transcription factors regulate the expression of two malate transporter protein-related genes (from LOB, zf-HD, C2C2-Dof, etc.), five transcription factors regulate the expression of two metal ion transporter protein-related genes (from MYB, zf-HD, C2C2-Dof), five transcription factors regulate the expression of two chloride channel protein-related genes (from MYB, bZIP, AP2/ERF), and two transcription factors regulate the expression of one Annexin-related gene (from NAC, MYB). This study provides a theoretical foundation for further research on the molecular regulation of guard cells and offers a molecular basis for enhancing drought resistance in oats.

Multiplexed perturbation of yew reveals cryptic proteins that enable a total biosynthesis of baccatin III and Taxol precursors.

Plants make complex and potent therapeutic molecules, but difficulties in sourcing from natural producers or chemical synthesis can challenge their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (Taxol ® ). Identification of the full paclitaxel biosynthetic pathway would enable heterologous drug production, but it has eluded discovery despite a half century of intensive research. Within the search space of Taxus' large, enzyme-rich genome, we suspected the complex paclitaxel pathway would be difficult to resolve using conventional gene co-expression analysis and small sample sets. To improve the resolution of gene set identification, we developed a multiplexed perturbation strategy to transcriptionally profile cell states spanning tissues, cell types, developmental stages, and elicitation conditions. This approach revealed a set of paclitaxel biosynthetic genes that segregate into expression modules that suggest consecutive biosynthetic sub-pathways. These modules resolved seven new genes that, when combined with previously known enzymes, are sufficient for the de novo biosynthesis and isolation of baccatin III, an industrial precursor for Taxol, in Nicotiana benthamiana leaves at levels comparable to the natural abundance in Taxus needles. Included are taxane 1β-hydroxylase (T1βH), taxane 9α-hydroxylase (T9αH), taxane 7β- O -acyltransferase (T7ΑΤ), taxane 7β- O -deacetylase (T7dA), taxane 9α- O -deacetylase (T9dA), and taxane 9-oxidase (T9ox). Importantly, the T9αH we discovered is distinct and independently evolved from those recently reported, which failed to yield baccatin III with downstream enzymes. Unexpectedly, we also found a nuclear transport factor 2 (NTF2)-like protein (FoTO1) crucial for high yields of taxanes; this gene promotes the formation of the desired product during the first taxane oxidation step, resolving a longstanding bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA-ligase, the eight genes discovered in this study enables the complete reconstitution of 3'- N -debenzoyl-2'-deoxy-paclitaxel with a 20-enzyme pathway in Nicotiana plants. More broadly, we establish a generalizable approach for pathway discovery that scales the power of co-expression studies to match the complexity of specialized metabolism, enabling discovery of gene sets responsible for high-value biological functions.

Microglia contribute to the production of the amyloidogenic ABri peptide in familial British dementia

Mutations in ITM2B cause familial British, Danish, Chinese, and Korean dementias. In familial British dementia (FBD), a mutation in the stop codon of the ITM2B gene (also known as BRI2) causes a C-terminal cleavage fragment of the ITM2B/BRI2 protein to be extended by 11 amino acids. This fragment, termed amyloid-Bri (ABri), is highly insoluble and forms extracellular plaques in the brain. ABri plaques are accompanied by tau pathology, neuronal cell death and progressive dementia, with striking parallels to the aetiology and pathogenesis of Alzheimer’s disease. The molecular mechanisms underpinning FBD are ill-defined. Using patient-derived induced pluripotent stem cells, we show that expression of ITM2B/BRI2 is 34-fold higher in microglia than neurons and 15-fold higher in microglia compared with astrocytes. This cell-specific enrichment is supported by expression data from both mouse and human brain tissue. ITM2B/BRI2 protein levels are higher in iPSC-microglia compared with neurons and astrocytes. The ABri peptide was detected in patient iPSC-derived microglial lysates and conditioned media but was undetectable in patient-derived neurons and control microglia. The pathological examination of post-mortem tissue supports the presence of ABri in microglia that are in proximity to pre-amyloid deposits. Finally, gene co-expression analysis supports a role for ITM2B/BRI2 in disease-associated microglial responses. These data demonstrate that microglia are major contributors to the production of amyloid forming peptides in FBD, potentially acting as instigators of neurodegeneration. Additionally, these data also suggest ITM2B/BRI2 may be part of a microglial response to disease, motivating further investigations of its role in microglial activation. These data have implications for our understanding of the role of microglia and the innate immune response in the pathogenesis of FBD and other neurodegenerative dementias including Alzheimer’s disease.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

Transcriptomic study reveals alteration in the expression of long non-coding RNAs (lncRNAs) during reversal of HIV-1 latency in monocytic cell line.

The presence of latent HIV reservoirs continues to be the biggest obstacle to achieving an HIV cure. Thus, long non-coding RNAs (lncRNAs) may serve as the preferred targets for HIV latency reversal. The goal of the study was to identify prospective lncRNAs for subsequent in vitro molecular and functional characterization. RNA-sequencing was performed in latently HIV-infected monocytic cell line (U1) under stimulated and unstimulated condition using Illumina-HiSeqX platform, followed by its validation using qRT-PCR assay. Gene ontology (GO), KEGG pathway, and co-expression analyses were performed to identify the enriched biological processes and pathways in U1 cells post-stimulation with the latency reversal agent SAHA. A total of 3,576 and 1,467 significantly altered lncRNAs and protein-coding genes respectively, were identified in SAHA-stimulated U1 cells compared to unstimulated ones. The GO and KEGG pathway analyses of the differentially expressed protein-coding genes showed the enrichment of diverse biological processes and pathways respectively, in SAHA-stimulated U1 cells. Co-expression analysis between lncRNAs and protein-coding gene pairs, helped predict potential pathways with which these lncRNAs are associated. Further in vitro validation in HIV-infected monocytes showed that the expression of the top two candidate lncRNAs, LINC01231 and LINC00560, are specific to HIV infection. Transcriptome analysis revealed changes in the expression of numerous lncRNAs and protein-coding genes following stimulation with SAHA. Co-expression analysis identified candidate lncRNAs and their associated biological pathways. However, additional in vitro experimental exploration using gene knockdown strategies is needed to ascertain the specific role of LINC01231 and LINC00560 lncRNAs in latently infected monocytes.

Consensus gene co-expression analysis across multiple intestinal tissues to identify key genes and pathways associated with abdominal fat deposition in broilers

ABSTRACT 1. Abdominal fat deposition (AFD) is regulated by multiple intestinal tissues, and changes in the function of intestinal tissues are associated with AFD. Currently, integration of transcriptomic data across multiple intestinal tissues to explore excessive AFD has rarely been reported in broilers. 2. In this study, a consensus gene co-expression network across the duodenum, jejunum, ileum and caecum of high- and low-abdominal fat broiler lines (HL and LL) was constructed using a publicly available transcriptomic data set. Combining the results of functional enrichment analyses and differential gene expression analyses, this investigated the genes and biological pathways across the four intestinal tissues that might influence AFD. 3. In one expression module, NDUFA5, NDUFS6, NDUFA4, NDUFS4, ATP5H, ATP5J and ATP5C1 were significantly enriched in the oxidative phosphorylation pathway, with GPX2 and GSR significantly enriched in the glutathione metabolism pathway. These genes were significantly downregulated in the four intestinal tissues of the HL compared to LL chickens, which may be associated with AFD by increasing intestinal permeability. 4. Lipid metabolism relevant genes were identified in other modules (ALDH7A1, ACSBG1, THEM4 and DECR1), which may be linked to AFD through regulation of lipid metabolism. Interestingly, in the first module, 12 genes were significantly enriched in the proteasome pathway and significantly downregulated in the four intestinal tissues in HL birds compared to LL birds, indicating a link between the proteasome and AFD.

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.

Gene co-expression analysis reveals conserved and distinct gene networks between resistant and susceptible Lens ervoides challenged by hemibiotrophic and necrotrophic pathogens

As field crops are likely to be challenged by multiple pathogens during their development, the investigation of broad-spectrum resistance in the host is of great interest for crop genetic enhancement. In this study, we attempted to address this question by adopting a weighed gene co-expression approach to study the temporal transcriptome dynamics of resistant and susceptible recombinant inbred lines (RILs) derived from an intraspecific Len ervoides cross during the infection process with either the necrotrophic pathogens Ascochyta lentis or Stemphylium botryosum, or the hemibiotrophic pathogen Colletotrichum lentis. By comparing networks of resistant and susceptible RILs, seven network module pairs were found to possess high correlation coefficients (R > 0.70) and large number of overlapping genes (n > 100). The conserved co-regulation of genes in these network module pairs were involved in plant cell wall synthesis, cell division, cytoskeleton organization, and protein ubiquitin related processes and appeared to be common disease responses against these pathogens. On the other hand, we also identified eight modules with low correlation between resistance and susceptibility networks. Among those, a stronger gene co-expression in R-genes and small RNA processes in the resistant hosts may be enhancing L. ervoides resistance against A. lentis, C. lentis, and S. botryosum, whereas the higher level of synergistic regulation in the synthesis of arginine and glutamine and phospholipid and glycerophospholipids in the susceptible hosts may contribute to increased susceptibility in L. ervoides.

No two clones are alike: characterization of heterologous subpopulations in a transgenic cell line of the model diatom Phaeodactylum tricornutum

BackgroundConjugation-based episome delivery is a highly efficient method used to transfer DNA into the diatom Phaeodactylum tricornutum, facilitating the production of recombinant proteins and high-value metabolites. However, previous reports have indicated phenotypic heterogeneity among individual cells from clonally propagated exconjugant cell lines, potentially affecting the stability of recombinant protein production in the diatom.ResultsHere, we characterized the differences between subpopulations with distinct fluorescence intensity phenotypes derived from a single exconjugant colony of P. tricornutum expressing the enhanced green fluorescent protein (eGFP). We analyzed the expression cassette sequence integrity, plasmid copy number, and global gene expression. Our findings reveal that lower copy numbers and the deletion of the expression cassette in part of the population contributed to low transgene expression. Gene co-expression analysis identified a set of genes with similar expression pattern to eGFP including a gene encoding a putative Flp recombinase, which may be related to variations in fluorescence intensity. These genes thus present themselves as potential candidates for increasing recombinant proteins production in P. tricornutum episomal expression system.ConclusionsOverall, our study elucidates genetic and transcriptomic differences between distinct subpopulations in a clonally propagated culture, contributes to a better understanding of heterogeneity in diatom expression systems for synthetic biology applications.

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.

Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.)

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Gene expression differences associated with alcohol use disorder in human brain.

Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (NNAc = 28 AUD, 29 non-AUD; NPFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.

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.

Deciphering the molecular mechanisms of heat stress tolerance in goats: Insights from transcriptome and Gene Co-expression analysis

Climate change poses a significant threat to the sustainability of livestock production systems in developing countries, particularly impacting small ruminants like goats, which are highly susceptible to heat stress. This stressor not only reduces productivity but also undermines economic viability. This study aimed to delve into the molecular mechanisms underlying heat stress tolerance in goats by conducting a comprehensive transcriptome analysis of heat-tolerant (HT, n = 4) and heat-susceptible (HS, n = 6) Jamunapari goats. Physiological metrics, such as rectal temperature, respiratory rate, and heart rate, were meticulously monitored under extreme environmental conditions (Temperature Humidity Index >92) to effectively classify goats based on their distinct heat stress responses. Samples of blood were obtained, and peripheral blood mononuclear cells (PBMCs) were extracted for subsequent RNA extraction. RNA-Seq analysis revealed a sum of 734 differentially expressed genes (DEGs), comprising 251 upregulated and 483 downregulated genes in HT goats compared to their HS counterparts. The WGCNA revealed three key modules, darkorange (tolerance), paleturquoise (respiration rate), and darkmagenta (heart rate). Moreover, functional enrichment analysis revealed that DEGs within these modules played intricate roles in crucial biological processes and pathways, including mitochondrial function, cardiac function, immune response, genomic stability, and metabolic regulation. This research notably enhances our comprehension of the genetic underpinnings of thermo-tolerance in goats and provides invaluable guidance for formulating breeding strategies aimed at bolstering livestock resilience against the challenges of climate change.

Comprehensive RNA-Seq Gene Co-Expression Analysis Reveals Consistent Molecular Pathways in Hepatocellular Carcinoma across Diverse Risk Factors.

Hepatocellular carcinoma (HCC) has the highest mortality rate and is the most frequent of liver cancers. The heterogeneity of HCC in its etiology and molecular expression increases the difficulty in identifying possible treatments. To elucidate the molecular mechanisms of HCC across grades, data from The Cancer Genome Atlas (TCGA) were used for gene co-expression analysis, categorizing each sample into its pre-existing risk factors. The R library BioNERO was used for preprocessing and gene co-expression network construction. For those modules most correlated with a grade, functional enrichments from different databases were then tested, which appeared to have relatively consistent patterns when grouped by G1/G2 and G3/G4. G1/G2 exhibited the involvement of pathways related to metabolism and the PI3K/Akt pathway, which regulates cell proliferation and related pathways, whereas G3/G4 showed the activation of cell adhesion genes and the p53 signaling pathway, which regulates apoptosis, cell cycle arrest, and similar processes. Module preservation analysis was then used with the no history dataset as the reference network, which found cell adhesion molecules and cell cycle genes to be preserved across all risk factors, suggesting they are imperative in the development of HCC regardless of potential etiology. Through hierarchical clustering, modules related to the cell cycle, cell adhesion, the immune system, and the ribosome were found to be consistently present across all risk factors, with distinct clusters linked to oxidative phosphorylation in viral HCC and pentose and glucuronate interconversions in non-viral HCC, underscoring their potential roles in cancer progression.

Regulatory Network of the Late-Recruited Primary Decarboxylase C4NADP-ME in Sugarcane.

In agronomically important C4 grasses, efficient CO2 delivery to Rubisco is facilitated by NADP-malic enzyme (C4NADP-ME), which decarboxylates malate in bundle sheath cells. However, understanding the molecular regulation of the C4NADP-ME gene in sugarcane (Saccharum spp.) is hindered by its complex genetic background. Enzymatic activity assays demonstrated that decarboxylation in sugarcane Saccharum spontaneum predominantly relies on the NADP-ME pathway, similar to sorghum (Sorghum bicolor) and maize (Zea mays). Comparative genomics analysis revealed the recruitment of eight core C4 shuttle genes, including C4NADP-ME (SsC4NADP-ME2), in the C4 pathway of sugarcane. Contrasting to sorghum and maize, the expression of SsC4NADP-ME2 in sugarcane is regulated by different transcription factors (TFs). We propose a gene regulatory network for SsC4NADP-ME2, involving candidate TFs identified through gene co-expression analysis and yeast one-hybrid experiment. Among these, ABA INSENSITIVE5 (ABI5) was validated as the predominant regulator of SsC4NADP-ME2 expression, binding to a G-box within its promoter region. Interestingly, the core element ACGT within the regulatory G-box was conserved in sugarcane, sorghum, maize, and rice (Oryza sativa), suggesting an ancient regulatory code utilized in C4 photosynthesis. This study offers insights into SsC4NADP-ME2 regulation, crucial for optimizing sugarcane as a bioenergy crop.

FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via MFAP4/FAK signal axis

Based on the joint analysis of multi-omic data and the biological experiments, we demonstrate that FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via regulating MFAP4/FAK signal axis in this study. The levels of FOXF1 and MFAP4 are significantly down-regulated in LUAD, and the increased levels of two genes can improve the clinical prognosis of LUAD patients. Fluorescein reporter gene determination, chromatin immunoprecipitation and gene co-expression analysis indicate that MFAP4 level is positively regulated by transcription factor FOXF1. The function enrichment analysis shows that the levels of FOXF1 and MFAP4 are closely associated with an enrichment of tumor metastasis signatures. FOXF1 can inhibit the migration and invasion of LAUD cells by transcriptionally activating MFAP4 expression. And the overexpression of FOXF1/MFAP4 can reduce focal adhesion kinase (FAK) phosphorylation, while their knockdown result in the opposite effects. The increased levels of FOXF1/MFAP4 enhance the antitumor immunity by increasing the infiltration of dendritic cells and CD4+ T cells, and the interactions between LUAD cells and immune cells, and activating multiple anti-tumor immunity-related pathways. In conclusion, our study reveals the potential function of FOXF1/MFAP4/FAK signal axis in inhibiting metastasis of LUAD cells and modulating anti-tumor immunity of LUAD patients.

Trait association and prediction through integrative k-meranalysis.

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.