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ETHNOPHARMACOLOGICAL RELEVANCE: Ganoderma lucidum (G. lucidum) has been widely used as adjuvant of anti-tumor therapy for variety tumors. The bioactive ingredients of G. lucidum mainly include triterpenes, such as Ganoderic acid A, Ganoderic acid B, Ganoderenic acid A, Ganoderenic acid B, Ganoderenic acid D, and Ganoderic acid X. However, the effects and underlying mechanisms of G. lucidum are often challenging in hepatocellular carcinoma (HCC) treatment. AIM OF THE STUDY: To explore the potential role and mechanism of enhancer-associated lncRNAs (en-lncRNAs) in G. lucidum treated HCC through the in vivo and in vitro experiments. MATERIALS AND METHODS: Hepa1-6-bearing C57 BL/6 mice model were established to evaluate the therapeutic efficacy of G. lucidum treated HCC. Ki67 and TUNEL staining were used to detect the tumor cell proliferation and apoptosis in vivo. The Mouse lncRNA 4*180K array was implemented to identify the differentially expressed (DE) lncRNAs and mRNAs of G. lucidum treated tumor mice. The constructed lncRNA-mRNA co-expression network and bioinformatics analysis were used to selected core en-lncRNAs and its neighboring genes. The UPLC-MS method was used to identify the triterpenes of G. lucidum, and the in vitro experiments were used to verify which triterpene monomers regulated en-lncRNAs in tumor cells. Finally, a stable knockdown/overexpression cell lines were used to confirm the relationship between en-lncRNA and neighboring gene. RESULTS: Ki67 and TUNEL staining demonstrated G. lucidum significantly inhibited tumor growth, suppressed cell proliferation and induced apoptosis in vivo. Transcriptomic analysis revealed the existence of 126 DE lncRNAs high correlated with 454 co-expressed mRNAs in G. lucidum treated tumor mice. Based on lncRNA-mRNA network and qRT-PCR validation, 6 core lncRNAs were selected and considered high correlated with G. lucidum treatment. Bioinformatics analysis revealed FR036820 and FR121302 might act as enhancers, and qRT-PCR results suggested FR121302 might enhance Popdc2 mRNA level in HCC. Furthermore, 6 main triterpene monomers of G. lucidum were identified by UPLC-MS method, and in vitro experiments showed FR121302 and Popdc2 were significantly suppressed by Ganoderenic acid A and Ganoderenic acid B, respectively. The knock/overexpression results demonstrated that FR121302 activating and enhancing Popdc2 expression levels, and Ganoderenic acid A and Ganoderenic acid B dramatically suppressed FR121302 and decreased Popdc2 level in Hepa1-6 cells. CONCLUSIONS: Enhancer-associated lncRNA plays a crucial role as an enhancer during hepatocarcinogenesis, and triterpenes of G. lucidum significantly inhibited tumor cell proliferation and induced apoptosis by regulating en-lncRNAs. Our study demonstrated Ganoderenic acid A and Ganoderenic acid B suppressed en-lncRNA FR121302 may be one of the critical strategies of G. lucidum inhibit hepatocellular carcinoma growth.

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Apoptosis , Carcinoma Hepatocelular , Proliferación Celular , Neoplasias Hepáticas , Ratones Endogámicos C57BL , ARN Largo no Codificante , Reishi , Triterpenos , Animales , Triterpenos/farmacología , Triterpenos/aislamiento & purificación , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Carcinoma Hepatocelular/tratamiento farmacológico , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/tratamiento farmacológico , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/genética , Reishi/química , Apoptosis/efectos de los fármacos , Proliferación Celular/efectos de los fármacos , Ratones , Línea Celular Tumoral , Masculino , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Humanos , Antineoplásicos Fitogénicos/farmacología , Antineoplásicos Fitogénicos/aislamiento & purificación

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

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Background: T-cell exhaustion (Tex) can be beneficial in autoimmune diseases, but its role in Graves' disease (GD), an autoimmune disorder of the thyroid, remains unknown. This study investigated Tex-related gene expression in GD patients to discern the potential contributions of these genes to GD pathogenesis and immune regulation. Methods: Through gene landscape analysis, a protein-protein interaction network of 40 Tex-related genes was constructed. mRNA expression levels were compared between GD patients and healthy control (HCs). Unsupervised clustering categorized GD cases into subtypes, revealing distinctions in gene expression, immune cell infiltration, and immune responses. Weighted gene co-expression network analysis and differential gene expression profiling identified potential therapeutic targets. RT-qPCR validation of candidate gene expression was performed using blood samples from 112 GD patients. Correlations between Tex-related gene expression and clinical indicators were analyzed. Results: Extensive Tex-related gene interactions were observed, with six genes displaying aberrant expression in GD patients. This was associated with atypical immune cell infiltration and regulation. Cluster analysis delineated two GD subtypes, revealing notable variations in gene expression and immune responses. Screening efforts identified diverse drug candidates for GD treatment. The Tex-related gene CBL was identified for further validation and showed reduced mRNA expression in GD patients, especially in cases of relapse. CBL mRNA expression was significantly lower in patients with moderate-to-severe thyroid enlargement than in those without such enlargement. Additionally, CBL mRNA expression was negatively correlated with the disease-specific indicator thyrotropin receptor antibodies. Conclusion: Tex-related genes modulate GD pathogenesis, and their grouping aids subtype differentiation and exploration of therapeutic targets. CBL represents a potential marker for GD recurrence.

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Enfermedad de Graves , Humanos , Enfermedad de Graves/genética , Enfermedad de Graves/inmunología , Masculino , Femenino , Adulto , Linfocitos T/inmunología , Linfocitos T/metabolismo , Persona de Mediana Edad , Perfilación de la Expresión Génica , Mapeo Cromosómico , Mapas de Interacción de Proteínas , Estudios de Casos y Controles , Proteínas Proto-Oncogénicas c-cbl/genética , Redes Reguladoras de Genes , Agotamiento de Células T

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This study unveils the pivotal roles of taurine metabolic reprogramming and its implications in the development and progression of Abdominal Aortic Aneurysm (AAA). Leveraging an integrated approach that combines single-cell RNA sequencing (scRNA-seq) and Weighted Gene Co-expression Network Analysis (WGCNA), our research investigates the intricate transcriptional and gene expression dynamics crucial to AAA. Our findings uniquely link metabolic shifts to the integrity of the extracellular matrix (ECM) and the functionality of smooth muscle cells (SMCs), key elements in the pathology of AAA. Utilizing scRNA-seq data from a mouse model (GSE152583 dataset), we identified critical alterations in cellular composition during AAA progression, particularly highlighting shifts in fibroblasts and inflammatory cells. Concurrently, WGCNA of human AAA tissue samples has outlined distinct gene expression patterns correlated with disease severity and progression, offering comprehensive insights into both molecular and cellular disease mechanisms. Moreover, this study introduces innovative metabolic profiling techniques to identify differential metabolites in AAA, integrating extensive metabolomic analyses with pathway enrichment strategies. This novel approach has pinpointed potential biomarkers and therapeutic targets, notably within taurine metabolism pathways, crucial for crafting non-surgical interventions. By merging state-of-the-art bioinformatics with thorough molecular analysis, our study not only enhances the understanding of AAA's complex pathophysiology but also catalyzes the development of targeted therapeutic strategies. This research represents a significant advancement in the molecular characterization of AAA, with substantial implications for its future diagnosis and treatment strategies.

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Aneurisma de la Aorta Abdominal , Progresión de la Enfermedad , Taurina , Aneurisma de la Aorta Abdominal/patología , Aneurisma de la Aorta Abdominal/metabolismo , Aneurisma de la Aorta Abdominal/genética , Taurina/metabolismo , Animales , Humanos , Ratones , Modelos Animales de Enfermedad , Miocitos del Músculo Liso/metabolismo , Miocitos del Músculo Liso/patología , Masculino , Análisis de la Célula Individual , Matriz Extracelular/metabolismo , Matriz Extracelular/patología , Metabolómica/métodos , Reprogramación Metabólica

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Nitrogen is an essential nutrient for plants and a major determinant of plant growth and crop yield. Plants acquire nitrogen mainly in the form of nitrate and ammonium. Both nitrogen sources affect plant responses and signaling pathways in a different way, but these signaling pathways interact, complicating the study of nitrogen responses. Extensive transcriptome analyses and the construction of gene regulatory networks, mainly in response to nitrate, have significantly advanced our understanding of nitrogen signaling and responses in model plants and crops. In this study, we aimed to generate a more comprehensive gene regulatory network for the major crop, rice, by incorporating the interactions between ammonium and nitrate. To achieve this, we assessed transcriptome changes in rice roots and shoots over an extensive time course under single or combined applications of the two nitrogen sources. This dataset enabled us to construct a holistic co-expression network and identify potential key regulators of nitrogen responses. Next to known transcription factors, we identified multiple new candidates, including the transcription factors OsRLI and OsEIL1, which we demonstrated to induce the primary nitrate-responsive genes OsNRT1.1b and OsNIR1. Our network thus serves as a valuable resource to obtain novel insights in nitrogen signaling.

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Introduction: Hemibiotrophic Phytophthora are a group of agriculturally and ecologically important pathogenic oomycetes causing severe decline in plant growth and fitness. The lifestyle of these pathogens consists of an initial biotrophic phase followed by a switch to a necrotrophic phase in the latter stages of infection. Between these two phases is the biotrophic to necrotrophic switch (BNS) phase, the timing and controls of which are not well understood particularly in Phytophthora spp. where host resistance has a purely quantitative genetic basis. Methods: To investigate this we sequenced and annotated the genome of Phytophthora medicaginis, causal agent of root rot and substantial yield losses to Fabaceae hosts. We analyzed the transcriptome of P. medicaginis across three phases of colonization of a susceptible chickpea host (Cicer arietinum) and performed co-regulatory analysis to identify putative small secreted protein (SSP) effectors that influence timing of the BNS in a quantitative pathosystem. Results: The genome of P. medicaginis is ~78 Mb, comparable to P. fragariae and P. rubi which also cause root rot. Despite this, it encodes the second smallest number of RxLR (arginine-any amino acid-leucine-arginine) containing proteins of currently sequenced Phytophthora species. Only quantitative resistance is known in chickpea to P. medicaginis, however, we found that many RxLR, Crinkler (CRN), and Nep1-like protein (NLP) proteins and carbohydrate active enzymes (CAZymes) were regulated during infection. Characterization of one of these, Phytmed_10271, which encodes an RxLR effector demonstrates that it plays a role in the timing of the BNS phase and root cell death. Discussion: These findings provide an important framework and resource for understanding the role of pathogenicity factors in purely quantitative Phytophthora pathosystems and their implications to the timing of the BNS phase.

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MOTIVATION: Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data is corrected using standard batch correction. RESULTS: In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. AVAILABILITY AND IMPLEMENTATION: COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io). SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

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Pulmonary hypertension (PH) is a life-threatening condition characterized by pulmonary vascular remodeling and endothelial dysfunction. Current therapies primarily target vasoactive imbalances but often fail to address adverse vascular remodeling. Long non-coding RNA (lncRNA), which are key regulators of various cellular processes, remain underexplored in the context of PH. To investigate the role of lncRNA in PH, we performed a comprehensive analysis using Weighted Gene Co-expression Network Analysis (WGCNA) on the GSE113439 dataset, comprising human lung tissue samples from different PH subtypes. Our analysis identified the lncRNA SNHG11 as consistently downregulated in PH. Functional assays in human pulmonary artery endothelial cells (HPAECs) demonstrated that SNHG11 plays a critical role in modulating inflammation, cell proliferation, apoptosis, and the JAK/STAT and MAPK signaling pathways. Mechanistically, SNHG11 influences the stability of PRPF8, a crucial mRNA spliceosome component, thereby affecting multiple cellular functions beyond splicing. In vivo experiments using a hypoxic rat model showed that knockdown of SNHG11 alleviates PH development and improves right ventricular function. These findings highlight SNHG11 as a key regulator in PH pathogenesis and suggest it as a potential therapeutic target.

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Growth-regulating factor (GRF) is a plant-specific family of transcription factors crucial for meristem development and plant growth. Sorghum (Sorghum bicolor L. Moench) is a cereal species widely used for food, feed and fuel. While sorghum stems are important biomass components, the regulation of stem development and the carbohydrate composition of the stem tissues remain largely unknown. Here, we identified 11 SbGRF-encoding genes and found the SbGRF expansion driven by whole-genome duplication events. By comparative analyses of GRFs between rice and sorghum, we demonstrated the divergence of whole-genome duplication (WGD)-derived OsGRFs and SbGRFs. A comparison of SbGRFs' expression profiles supports that the WGD-duplicated OsGRFs and SbGRFs experienced distinct evolutionary trajectories, possibly leading to diverged functions. RNA-seq analysis of the internode tissues identified several SbGRFs involved in internode elongation, maturation and cell wall metabolism. We constructed co-expression networks with the RNA-seq data of sorghum internodes. Network analysis discovered that SbGRF1, 5 and 7 could be involved in the down-regulation of the biosynthesis of cell wall components, while SbGRF4, 6, 8 and 9 could be associated with the regulation of cell wall loosening, reassembly and/or starch biosynthesis. In summary, our genome-wide analysis of SbGRFs reveals the distinct evolutionary trajectories of WGD-derived SbGRF pairs. Importantly, expression analyses highlight previously unknown functions of several SbGRFs in internode elongation, maturation and the potential involvement in the metabolism of the cell wall and starch during post-anthesis stages.

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Aspen (Populus tremula L.) is a keystone species and a model system for forest tree genomics. We present an updated resource comprising a chromosome-scale assembly, population genetics and genomics data. Using the resource, we explore the genetic basis of natural variation in leaf size and shape, traits with complex genetic architecture. We generated the genome assembly using long-read sequencing, optical and high-density genetic maps. We conducted whole-genome resequencing of the Umeå Aspen (UmAsp) collection. Using the assembly and re-sequencing data from the UmAsp, Swedish Aspen (SwAsp) and Scottish Aspen (ScotAsp) collections we performed genome-wide association analyses (GWAS) using Single Nucleotide Polymorphisms (SNPs) for 26 leaf physiognomy phenotypes. We conducted Assay of Transposase Accessible Chromatin sequencing (ATAC-Seq), identified genomic regions of accessible chromatin, and subset SNPs to these regions, improving the GWAS detection rate. We identified candidate long non-coding RNAs in leaf samples, quantified their expression in an updated co-expression network, and used this to explore the functions of candidate genes identified from the GWAS. A GWAS found SNP associations for seven traits. The associated SNPs were in or near genes annotated with developmental functions, which represent candidates for further study. Of particular interest was a ~177-kbp region harbouring associations with several leaf phenotypes in ScotAsp. We have incorporated the assembly, population genetics, genomics, and GWAS data into the PlantGenIE.org web resource, including updating existing genomics data to the new genome version, to enable easy exploration and visualisation. We provide all raw and processed data to facilitate reuse in future studies.

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Genética de Población , Genoma de Planta , Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido Simple , Populus , Populus/genética , Genoma de Planta/genética , Polimorfismo de Nucleótido Simple/genética , Cromosomas de las Plantas/genética , Fenotipo , Hojas de la Planta/genética , Genómica/métodos , Mapeo Cromosómico

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Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this paper, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer's disease and identified previously unknown cell-type-specific co-expressions among Alzheimer's disease risk genes, suggesting cell-type-specific disease mechanisms.

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

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Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes with a high incidence rate. Notably, the disturbance of lipid metabolism is associated with DN progression. The present study aimed to identify lipid metabolism-related hub genes associated with DN for improved diagnosis of DN. The gene expression profile data of DN and healthy samples (GSE142153) were obtained from the Gene Expression Omnibus database, and the lipid metabolism-related genes were obtained from the Molecular Signatures Database. Differentially expressed genes (DEGs) between DN and healthy samples were analyzed. The weighted gene co-expression network analysis (WGCNA) was performed to examine the relationship between genes and clinical traits to identify the key module genes associated with DN. Next, the Venn Diagram R package was used to identify the lipid metabolism-related genes associated with DN and their protein-protein interaction (PPI) network was constructed. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The hub genes were identified using machine-learning algorithms. The Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. The present study also investigated the immune infiltration discrepancies between DN and healthy samples, and assessed the correlation between the immune cells and hub genes. Finally, the expression levels of key genes were verified by reverse transcription-quantitative (RT-q)PCR. The present study determined 1,445 DEGs in DN samples. In addition, 694 DN-related genes in MEyellow and MEturquoise modules were identified by WGCNA. Next, the Venn Diagram R package was used to identify 17 lipid metabolism-related genes and to construct a PPI network. GO analysis revealed that these 17 genes were markedly associated with 'phospholipid biosynthetic process' and 'cholesterol biosynthetic process', while the KEGG analysis showed that they were enriched in 'glycerophospholipid metabolism' and 'fatty acid degradation'. In addition, SAMD8 and CYP51A1 were identified through the intersections of two machine-learning algorithms. The results of GSEA revealed that the 'mitochondrial matrix' and 'GTPase activity' were the markedly enriched GO terms in both SAMD8 and CYP51A1. Their KEGG pathways were mainly concentrated in the 'pathways of neurodegeneration-multiple diseases'. Immune infiltration analysis showed that nine types of immune cells had different expression levels in DN (diseased) and healthy samples. Notably, SAMD8 and CYP51A1 were both markedly associated with activated B cells and effector memory CD8 T cells. Finally, RT-qPCR confirmed the high expression of SAMD8 and CYP51A1 in DN. In conclusion, lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN. The present study provides fundamental information on lipid metabolism that may aid the diagnosis and treatment of DN.

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The tertiary lymphoid structure (TLS) plays a central role in cancer immune response, and its gene expression pattern, called the TLS signature, has shown prognostic value in breast cancer. The formation of TLS and tumor-associated high endothelial venules (TA-HEVs), responsible for lymphocytic infiltration within the TLS, is associated with the expression of cancer hallmark genes (CHGs) related to immunity and inflammation. In this study, we performed co-expression network analysis of immune- and inflammation-related CHGs to identify predictive genes for breast cancer. In total, 382 immune- and inflammation-related CHGs with high expression variance were extracted from the GSE86166 microarray dataset of patients with breast cancer. CHGs were classified into five modules by applying weighted gene co-expression network analysis. The survival analysis results for each module showed that one module comprising 45 genes was statistically significant for relapse-free and overall survival. Four network properties identified key genes in this module with high prognostic prediction abilities: CD34, CXCL12, F2RL2, JAM2, PROS1, RAPGEF3, and SELP. The prognostic accuracy of the seven genes in breast cancer was synergistic and exceeded that of other predictors in both small and large public datasets. Enrichment analysis predicted that these genes had functions related to leukocyte infiltration of TA-HEVs. There was a positive correlation between key gene expression and the TLS signature, suggesting that gene expression levels are associated with TLS density. Co-expression network analysis of inflammation- and immune-related CHGs allowed us to identify genes that share a standard function in cancer immunity and have a high prognostic predictive value. This analytical approach may contribute to the identification of prognostic genes in TLS.

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BACKGROUND: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). RESULTS: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. CONCLUSIONS: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

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Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Humanos , Análisis de Secuencia de ARN/métodos , Transcriptoma/genética , Algoritmos , Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes/genética

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Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

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Diurnal gene expression is a pervasive phenomenon occurring across all kingdoms of life, orchestrating adaptive responses to daily environmental fluctuations and thus enhancing organismal fitness. Our understanding of the plant circadian clock is primarily derived from studies in Arabidopsis and direct comparisons are difficult due to differences in gene family sizes. To this end, the identification of functional orthologs based on diurnal and tissue expression is necessary. The diurnal.plant.tools database constitutes a repository of gene expression profiles from 17 members of the Archaeplastida lineage, with built-in tools facilitating cross-species comparisons. In this database update, we expand the dataset with diurnal gene expression from 4 agriculturally significant crop species and Marchantia, a plant of evolutionary significance. Notably, the inclusion of diurnal gene expression data for Marchantia enables researchers to glean insights into the evolutionary trajectories of the circadian clock and other biological processes spanning from algae to angiosperms. Moreover, integrating diurnal gene expression data with datasets from related gene co-expression databases, such as CoNekt-Plants and CoNekt-Stress, which contain gene expression data for tissue and perturbation experiments, provides a comprehensive overview of gene functions across diverse biological contexts. This expanded database serves as a valuable resource for elucidating the intricacies of diurnal gene regulation and its evolutionary underpinnings in plant biology.

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Myricaria laxiflora is an endangered shrub plant with remarkable tolerance to waterlogging stress, however, little attention has been paid to understanding the underlying mechanisms. Here, physiological and transcriptomic approaches were applied to uncover the physiological and molecular reconfigurations in the stem of M. laxiflora in response to waterlogging stress. The accumulation of the contents of H2O2 and malonaldehyde (MDA) alongside increased activities of enzymes for scavenging the reactive oxygen species (ROS) in the stem of M. laxiflora were observed under waterlogging stress. The principal component analysis (PCA) of transcriptomes from five different timepoints uncovered PC1 counted for 17.3 % of total variations and separated the treated and non-treated samples. A total of 8714 genes in the stem of M. laxiflora were identified as differentially expressed genes (DEGs) under waterlogging stress, which could be assigned into two different subgroups with distinct gene expression patterns and biological functions. The DEGs involved in glycolysis were generally upregulated, whereas opposite results were observed for nitrogen uptake and the assimilation pathway. The contents of abscisic acid (ABA) and jasmonic acid (JA) were sharply decreased alongside the decreased mRNA levels of the genes involved in corresponding synthesis pathways upon waterlogging stress. A network centered by eight key transcription factors has been constructed, which uncovered the inhibited cell division processes in the stem of M. laxiflora upon waterlogging stress. Taken together, the obtained results showed that glycolysis, nitrogen metabolism and meristem activities played an important role in the stem of M. laxiflora in response to waterlogging stress.

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The omnigenic model posits that genetic risk for traits with complex heritability involves cumulative effects of peripheral genes on mechanistic "core genes," suggesting that in a network of genes, those closer to clusters including core genes should have higher GWAS signals. In gene co-expression networks, we confirmed that GWAS signals accumulate in genes more connected to risk-enriched gene clusters, highlighting across-network risk convergence. This was strongest in adult psychiatric disorders, especially schizophrenia (SCZ), spanning 70% of network genes, suggestive of super-polygenic architecture. In snRNA-seq cell type networks, SCZ risk convergence was strongest in L2/L3 excitatory neurons. We prioritized genes most connected to SCZ-GWAS genes, which showed robust association to a CRISPRa measure of PGC3 regulation and were consistently identified across several brain regions. Several genes, including dopamine-associated ones, were prioritized specifically in the striatum. This strategy thus retrieves current drug targets and can be used to prioritize other potential drug targets.

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Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2°C). The double mutants exhibited patterns of fitness epistasis that included diminishing-returns epistasis at 42.2°C, stronger diminishing-returns between mutations with larger beneficial effects, and both negative and positive (sign) epistasis across environments (20.0°C and 37.0°C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly-connected hub genes in co-expression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both co-expression and protein-interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination, but that was not the case. Altogether, gene expression and co-expression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression towards the unstressed state.