Gene Network Analysis Research Articles

Identification and functional network analysis of inflammatory and apoptosis-related genes associated with infectious chronic rhinosinusitis: Thermal modeling of medical biological systems

The aim of this study was to identify inflammatory and apoptotic genes associated with infectious chronic sinusitis and to analyze their functional networks in medical biological systems by thermal modeling. Bioinformatics analysis was used to identify genes involved in apoptosis and inflammation and construct their functional networks. Finally, thermal modeling techniques were used to explore the dynamic changes of these genes in the pathogenesis of chronic sinusitis. In a comparative analysis, thermal modeling analysis revealed that these genes play important roles in apoptosis, immune response, and inflammatory signaling pathways. In addition, the heat model also revealed the interaction network between different genes, providing a new perspective for understanding the coordination mechanism of inflammation and apoptosis. Through the identification of genes associated with infectious chronic sinusitis and thermal modeling analysis of their functional networks, this study revealed the complex interaction between apoptosis and inflammation in the disease process.

Integrated data driven analysis identifies potential candidate genes associated with PCOS

Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ~2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.

Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection

AbstractBackgroundLatent tuberculosis infection (LTBI) is a significant source of active tuberculosis (ATB), yet distinguishing between them is challenging because specific biomarkers are lacking.MethodsWe analyzed four microarray datasets (GSE19491, GSE37250, GSE54992, GSE28623) from the gene expression omnibus to identify differentially expressed genes (DEGs). Using protein–protein interaction (PPI) networks and LASSO‐SVM algorithms, we selected three candidate biomarkers and evaluated their diagnostic efficacy. The expression levels of core genes were validated by RNA sequencing of healthy, ATB, and LTBI groups in a real‐world cohort. We conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, predicted shared upstream miRNAs, constructed miRNA–hub and transcription factor (TF)–hub gene networks, and performed immune infiltration analysis.ResultsThree hub genes (SERPING1, C1QC, C1QB) were identified from 45 DEGs by PPI networks and machine learning screening. The diagnostic model based on the three hub genes had an area under the curve (AUC) value of 0.843 in the training set GSE19491 and 0.865 in the validation set GSE28623. Real‐world transcriptome sequencing confirmed the expression trends of the hub genes across healthy, LTBI, and ATB groups. GO analysis showed that the 45 hub genes were primarily associated with immune inflammatory responses and pattern recognition receptors, whereas KEGG analysis indicated enrichment in complement and coagulation cascades. The miRNA–hub and TF–hub gene network analysis identified nine miRNAs and the zinc finger TF GATA2 as potential co‐regulators of SERPING1, C1QC, and C1QB. Immune cell infiltration analysis identified significant differences in the immune microenvironment between LTBI and ATB, with macrophages and natural killer cells showing significant correlations with tuberculosis infection.ConclusionThe diagnostic model with SERPING1, C1QC, and C1QB shows promise in distinguishing LTBI from ATB, indicating its potential as a diagnostic tool.

Proteomic profiling of oral squamous cell carcinoma tissues reveals altered immune-related proteins: implications for personalized therapy

ABSTRACT Objectives Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments. Methods Data-independent acquisition (DIA) mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data. Results DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling, and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein–protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3. Conclusions Further validation of these proteins could establish them as potential targets for personalized therapy.

Exploring transcriptomic mechanisms underlying pulmonary adaptation to diverse environments in Indian rams.

The Changthangi sheep thrive at high altitudes in the cold desert regions of Ladakh, India while Muzaffarnagri sheep are well-suited to the low altitude plains of northern India. This study investigates the molecular mechanisms of pulmonary adaptation to diverse environments by analyzing gene expression profiles of lung tissues through RNA sequencing. Four biological replicates of lung tissue from each breed were utilized to generate the transcriptomic data. Differences in gene expression analysis revealed discrete expression profiles in lungs of each breed. In Changthangi sheep, genes related to immune responses, particularly cytokine signaling, were significantly enriched. Pathway analysis highlighted the activation of NF-kB signaling, a key mediator of inflammation and immune response. Additionally, the gene network analysis indicated a strong association between cytokine signaling, hypoxia-inducible factor (HIF) and NF-kB activation, suggesting a coordinated response to hypoxic stress in lungs of Changthangi sheep. In Muzaffarnagri sheep, the gene expression profiles were enriched for pathways related to energy metabolism, homeostasis and lung physiology. Key pathways identified include collagen formation and carbohydrate metabolism, both of which are crucial for maintaining lung function and structural integrity. Gene network analysis further reinforced this by revealing a strong connection between genes associated with lung structure and function. Our findings shed light on the valuable insights into gene expression mechanisms that enable these sheep breeds to adapt to their respective environments and contribute to a better understanding of high altitude adaptation in livestock.

A Novel Multi-Omics approach for Identifying Key Genes in Intervertebral Disc Degeneration

Many different cell types and complex molecular pathways are involved in intervertebral disc degeneration (IDD). We used a multi-omics approach combining single-cell RNA sequencing, differential gene expression analysis, and Mendelian randomization to clarify the underlying genetic architecture of IDD. We identified 1,164 differentially expressed genes (DEGs) across four important cell types associated with IDD using publicly available single-cell datasets. A thorough gene network analysis identified 122 genes that may be connected to programmed cell death, a crucial route in the etiology of IDD. SLC40A1, PTGS2, and GABARAPL1 have been identified as noteworthy regulatory genes that may impede the advancement of IDD. Furthermore, distinct cellular subpopulations and dynamic gene expression patterns were revealed by functional enrichment analysis and pseudo-temporal ordering of chondrocytes. Our results highlight the therapeutic potential of GABARAPL1, PTGS2, and SLC40A1 targeting in the treatment of IDD.

Time-restricted feeding prevents memory impairments induced by obesogenic diet consumption, via hippocampal thyroid hormone signaling

ObjectivesThe early consumption of calorie-rich diet disrupts circadian rhythms and has adverse effects on memory, yet the effects of time-restricted feeding (TRF) and the underlying molecular mechanisms are unknown. Here, we set out to identify the behavioral and molecular circadian rhythms disruptions generated by juvenile obesogenic diet consumption and their restoration by TRF in male mice. MethodsMetabolic rhythms were measured by indirect calorimetry and memory performances by behavioral tasks. Hippocampal translatome (pS6_TRAP), enrichment and co-regulated gene network analyses were conducted to identify the molecular pathways involved in memory impairments and their restoration by TRF. Differential exon usage analyses, mass spectrometry and pharmacological intervention were used to confirm thyroid hormone signaling involvement. ResultsWe show that four weeks of TRF restore the rhythmicity of metabolic parameters and prevents memory impairments in mice fed a high fat-high sucrose (HFS) diet since weaning, independently of body fat levels. Hippocampal translatome and differential exon usage analyses indicate that impaired memory of mice under ad libitum HFS diet is accompanied by reduced thyroid hormone signaling and altered expression of astrocytic genes regulating glutamate neurotransmission. TRF restored the diurnal expression variation of part of these genes and intra-hippocampal infusion of T3, the active form of thyroid hormone, rescues memory performances and astrocytic gene expression of ad libitum HFS diet-fed mice. ConclusionsThus, thyroid hormones contribute to the TRF positive effects on both metabolism and memory in mice fed an obesogenic diet, highlighting this nutritional approach as a powerful tool in addressing obesity brain comorbidities and paving the way for further mechanistic studies on hippocampal thyroid signaling.

Differential Gene Expression in Late-Onset Friedreich Ataxia: A Comparative Transcriptomic Analysis Between Symptomatic and Asymptomatic Sisters.

Friedreich ataxia (FRDA) is the most common inherited ataxia, primarily impacting the nervous system and the heart. It is characterized by GAA repeat expansion in the FXN gene, leading to reduced mitochondrial frataxin levels. Previously, we described a family displaying two expanded GAA alleles, not only in the proband affected by late-onset FRDA but also in the younger asymptomatic sister. The molecular characterization of the expanded repeats showed that the affected sister carried two canonical uninterrupted GAA expended repeats, whereas the asymptomatic sister had a compound heterozygous for a canonical GAA repeat and an expanded GAAGGA motif. Therefore, we decided to perform RNA sequencing (RNA-seq) on fibroblasts from both sisters in order to understand whether some genes and/or pathways might be differently involved in the occurrence of FRDA clinical manifestation. The transcriptomic analysis revealed 398 differentially expressed genes. Notably, TLR4, IL20RB, and SLITRK5 were up-regulated, while TCF21 and GRIN2A were down-regulated, as validated by qRT-PCR. Gene ontology (GO) enrichment and network analysis highlighted significant involvement in immune response and neuronal functions. Our results, in particular, suggest that TLR4 may contribute to inflammation in FRDA, while IL20RB, SLITRK5, TCF21, and GRIN2A dysregulation may play roles in the disease pathogenesis. This study introduces new perspectives on the inflammatory and developmental aspects in FRDA, offering potential targets for therapeutic intervention.

Transcriptomic analysis of cellular senescence induced by ectopic expression of ATF6α in human breast cancer cells.

The transcriptomic profile of cellular senescence is strongly associated with distinct cell types, the specific stressors triggering senescence, and temporal progression through senescence stages. This implies the potential necessity of conducting separate investigations for each cell type and a stressor inducing senescence. To elucidate the molecular mechanism that drives endoplasmic reticulum (ER) stress-induced cellular senescence in MCF-7 breast cancer cells, with a particular emphasis on the ATF6α branch of the unfolded protein response. We conducted transcriptomic analysis on MCF-7 cells by ectopic expression of ATF6α. Transcriptomic sequencing was conducted on MCF-7 cells at 6 and 9 hours post senescence induction through ATF6α ectopic expression. Comprehensive analyses encompassing enriched functional annotation, canonical pathway analysis, gene network analysis, upstream regulator analysis and gene set enrichment analysis were performed on Differentially Expressed Genes (DEGs) at 6 and 9 hours as well as time-related DEGs. Regulators and their targets identified from the upstream regulator analysis were validated through RNA interference, and their impact on cellular senescence was assessed by senescence-associated β-galactosidase staining. ATF6α ectopic expression resulted in the identification of 12 and 79 DEGs at 6 and 9 hours, respectively, employing criteria of a false discovery rate < 0.05 and a lower fold change (FC) cutoff |log2FC| > 1. Various analyses highlighted the involvement of the UPR and/or ER Stress Pathway. Upstream regulator analysis of 9 hour-DEGs identified six regulators and eleven target genes associated with processes related to cytostasis and 'cell viability and cell death of connective tissue cells.' Validation confirmed the significance of MAP2K1/2, GPAT4, and PDGF-BB among the regulators and DDIT3, PPP1R15A, and IL6 among the targets. Transcriptomic analyses and validation reveal the importance of the MAP2K1/2/GPAT4-DDIT3 pathway in driving cellular senescence following ATF6α ectopic expression in MCF-7 cells. This study contributes to our understanding of the initial molecular events underlying ER stress-induced cellular senescence in breast cancer cells, providing a foundation for exploring cell type- and stressor-specific responses in cellular senescence induction.

Prognostic value of myocardial redox signalling: a machine learning multi-omics approach

Abstract Background Oxidative stress has been proposed to be implicated in cardiovascular pathology. Studies have linked myocardial oxidative stress with the development of atrial fibrillation, but its role in the prediction of long-term outcomes and mortality is yet to be elucidated. Purpose To apply discovery network transcriptomics and genomics to atrial tissue (AT) obtained from patients undergoing cardiac surgery, to phenotype myocardial redox-state and identify potential therapeutic targets. Methods We included 1,032 patients undergoing cardiac surgery. Arm 1 included 258 patients in whom atrial tissue (right atrial appendage) was used for ex-vivo quantification of NOX-derived superoxide by lucigenin-enhanced chemiluminescence. Arm 2 included 414 patients in whom exploratory, unsupervised gene network analysis in human atrial tissue RNA sequencing data was performed to identify atrial tissue redox-sensitive signatures (blue signature). Arm 3 included 881 patients genotyped with the genome-wide SNP array UK Biobank Axiom Array and used for machine learning construction of GenRedOx, a SNP model built to predict the blue transcriptomic signature. Associations with future incidence of major adverse myocardial events (MAME: cardiovascular death and/or heart failure) was assessed across all arms using cox regression models (adjusted for age, sex, hypertension, dyslipidaemia, diabetes mellitus, body mass index, and plasma TNFa). Results Over a median follow-up of 5.27 years [IQR: 4.29-6.83] high atrial tissue (AT) NOX-derived superoxide was independently associated with MAME risk (Adj. HR[95%CI]: 11.7 [2.18 , 62.82], p=0.004, a). Unsupervised transcriptomic analysis in Arm 2 revealed 15 coexpressed gene ‘modules’ or 'signatures' (b). The blue signature was significantly associated with AT superoxide production with "immune response-regulating and activating signalling" being amongst the top enriched pathways. Patients with high eigengene values for the blue signature showed a 5.5-fold higher adjusted risk of MAME (c). In Arm 3, extreme gradient boosting of SNP data, trained using the blue transcriptomic signature as ground truth, revealed GenRedOx (d), a genomic artificial intelligence score, which was found to be prognostic of MAME in the validation stage (e). Homozygosity for the minor allele of SNP rs723897 in the Chemokine-Like Factor Super Family 7 (CMTM7) gene had the highest gain in the model. Conclusion We present for the first time two novel human myocardial transcriptomic and genomic signatures that reflect changes in redox state and identify long-term cardiovascular risk. Targeting pathways in the myocardium related with immune response signalling may lead to the development of novel therapeutics in cardiovascular disease.

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM.

The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene lists, and gene signatures (PAGs) enriched with metadata to represent biological functions. Furthermore, we established PAG-PAG networks by leveraging gene member similarity and gene regulations. However, in practice, high similarity in functional descriptions or gene membership often leads to redundant PAGs, hindering the interpretation from a fuzzy enriched PAG list. In this study, we developed todenE (topology-based and density-based ensemble) clustering, pioneering in integrating topology-based and density-based clustering methods to detect PAG communities leveraging the PAG network and Large Language Models (LLM). In computational genomics annotation, the genes can be grouped/clustered through the gene relationships and gene functions via guilt by association. Similarly, PAGs can be grouped into higher-level clusters, forming concise functional representations called Super-PAGs. TodenE captures PAG-PAG similarity and encapsulates functional information through LLM, in characterizing network-based functional Super-PAGs. In synthetic data, we introduced a metric called the Disparity Index (DI), measuring the connectivity of gene neighbors to gauge clusterability. We compared multiple clustering algorithms to identify the best method for generating performance-driven clusters. In non-simulated data (Gene Ontology), by leveraging transfer learning and LLM, we formed a language-based similarity embedding. TodenE utilizes this embedding together with the topology-based embedding to generate putative Super-PAGs with superior performance in semantic and gene member inclusiveness.

Dissecting the temporal genetic networks programming soybean embryo development from embryonic morphogenesis to post-germination

Key messageDesiccation-stage transcription factors perform similar functions, with early ones focused on desiccation tolerance and later ones on development. Gene networks governing late embryo development diverge between soybean and Arabidopsis.To understand gene activities programming seed embryo development, we profiled the soybean embryo transcriptome across embryonic morphogenesis through post-germination. Transcriptomic landscapes across embryo development feature highly prevalent transcripts, categorized into early and late groups, with shared and distinct functions. During the mid-storage reserve accumulation stage, the upregulated genes are enriched with regulatory tasks at both the transcriptional and chromatin levels, including DNA methylation and chromatin remodeling. The epigenetic-related functions also dominate in the upregulated genes during germination, involving core histone variants and histone chaperones. Gene network analysis reveals both stage-specific modules and modules active across multiple stages. The desiccation-associated gene module integrates diverse transcription factors (TFs) that are sequentially active during different desiccation stages, transitioning from abiotic stress functions early on to developmental functions later. Two TFs, active during the early and mid-desiccation stages were functionally assessed in Arabidopsis overexpression lines to uncover their potential roles in desiccation processes. Interestingly, nearly half of the Arabidopsis orthologs of soybean TFs active in the desiccation-associated module are inactive during Arabidopsis desiccation. Our results reveal that chromatin and transcriptional regulation coordinate during mid-storage reserve accumulation, while distinct epigenetic mechanisms drive germination. Additionally, gene modules either perform stage-specific functions or are required across multiple stages, and gene networks during late embryogenesis diverge between soybean and Arabidopsis. Our studies provide new information on the biological processes and gene networks underlying development from embryonic morphogenesis to post-germination.

RXRα/MR signaling promotes diabetic kidney disease by facilitating renal tubular epithelial cells senescence and metabolic reprogramming

Cell senescence and metabolic reprogramming are significant features of diabetic kidney disease (DKD). However, the underlying mechanisms between cell senescence and metabolic reprogramming are poorly defined. Here, we report that retinoid X receptor α (RXRα), a key nuclear receptor transcription factor, regulates cell senescence and metabolic reprogramming in DKD. Through high-throughput sequencing, bioinformatic analysis and experimental validation, we confirmed the critical role of RXRα in promoting senescence and metabolic disturbances in renal tubular epithelial cells (RTECs) induced by lipid overload. In vivo, in situ injection of AAV9-shRxra into the kidney reduced proteinuria, RTECs senescence and insulin resistance in DKD mice. In vitro, knockdown of RXRα markedly improved G2/M phase arrest and suppressed the expression of senescence-associated secretory phenotypes (SASPs). Protein-protein interaction (PPI) analysis and unbiased bioinformatics were employed to identify the direct interactions between RXRα and the mineralocorticoid receptor (MR), which were subsequently validated through coimmunoprecipitation. Gene network analysis revealed the collaborative regulatory role of RXRα and MR in RTECs senescence. In an accelerated aging mouse model, an MR antagonist inhibited RXRα/MR signaling, improved RTECs senescence, and reduce interstitial fibrosis and lipid deposition in the kidneys. These findings indicate that inhibition of RXRα/MR signaling could alleviate cell senescence during metabolic disorders. Thus, our study revealed that RXRα/MR signaling serves as a critical regulatory factor mediating the crosstalk between cell senescence and metabolic reprogramming, shedding light on a novel mechanism for targeting cell senescence and metabolic dysregulation in DKD.

Blood transcriptome profiling reveals distinct gene networks induced by mRNA vaccination against COVID-19.

Messenger RNA (mRNA) vaccines represent a new class of vaccines that has been shown to be highly effective during the COVID-19 pandemic and that holds great potential for other preventative and therapeutic applications. While it is known that the transcriptional activity of various genes is altered following mRNA vaccination, identifying and studying gene networks could reveal important scientific insights that might inform future vaccine designs. In this study, we conducted an in-depth weighted gene correlation network analysis of the blood transcriptome before and 24h after the second and third vaccination with licensed mRNA vaccines against COVID-19 in humans, following a prime vaccination with either mRNA or ChAdOx1 vaccines. Utilizing this unsupervised gene network analysis approach, we identified distinct modular networks of co-varying genes characterized by either an expressional up- or downregulation in response to vaccination. Downregulated networks were associated with cell metabolic processes and regulation of transcription factors, while upregulated networks were associated with myeloid differentiation, antigen presentation, and antiviral, interferon-driven pathways. Within this interferon-associated network, we identified highly connected hub genes such as STAT2 and RIGI and associated upstream transcription factors, potentially playing important regulatory roles in the vaccine-induced immune response. The expression profile of this network significantly correlated with S1-specific IgG levels at the follow-up visit in vaccinated individuals. Those findings could be corroborated in a second, independent cohort of mRNA vaccine recipients. Collectively, results from this modular gene network analysis enhance the understanding of mRNA vaccines from a systems immunology perspective. Influencing specific gene networks could lead to optimized vaccines that elicit augmented vaccine responses.

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.

Genome-Wide Identification and Expression Analysis of the BTB Gene Superfamily Provides Insight into Sex Determination and Early Gonadal Development of Alligator sinensis.

The BTB gene superfamily is widely distributed among higher eukaryotes and plays a significant role in numerous biological processes. However, there is limited knowledge about the structure and function of BTB genes in the critically endangered species Alligator sinensis, which is endemic to China. A total of 170 BTB genes were identified from the A. sinensis genome, classified into 13 families, and unevenly distributed across 16 chromosomes. Analysis of gene duplication events yielded eight pairs of tandem duplication genes and six pairs of segmental duplication genes. Phylogenetics shows that the AsBTB genes are evolutionarily conserved. The cis-regulatory elements in the AsBTB family promoter region reveal their involvement in multiple biological processes. Protein interaction network analysis indicates that the protein interactions of the AsBTB genes are centered around CLU-3, mainly participating in the regulation of biological processes through the ubiquitination pathway. The expression profile and protein interaction network analysis of AsBTB genes during sex differentiation and early gonadal development indicate that AsBTB genes are widely expressed in this process and involves numerous genes and pathways for regulation. This study provides a basis for further investigation of the role of the BTB gene in sex differentiation and gonadal development in A. sinensis.

Weighted gene co-expression network analysis and identification of ginsenoside biosynthesis candidate genes for ginseng adventitious roots under MeJA treatment.

Ginseng (Panax ginseng) is an herb with a long history and a wide range of applications. Ginsenoside is one of the most representative and active ginseng compounds, with various pharmacological effects. Therefore, the development of bioreactors using methyl jasmonate (MeJA) as an inducer for targeted ginsenoside production is of great commercial value. Combined with transcriptomic research tools, screenings to obtain candidate genes involved in ginsenoside biosynthesis are crucial for future discoveries about the molecular mechanism of MeJA-regulated ginsenoside biosynthesis. In our study, the ginsenoside content of ginseng adventitious roots treated with MeJA at different times was analyzed. Transcriptome analysis was performed to investigate the effects of MeJA on changes in ginsenoside content in ginseng adventitious roots. The MeJA could significantly increase changes in the content of pro-ginsenodiol ginsenosides as well as pro-triol ginsenosides Rg3, Re, and Rf in ginseng adventitious roots. Differential gene expression analysis showed that a total of 14,009 differentially expressed genes were obtained from the screening of the present study. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that differentially expressed genes were mainly enriched under GO terms in response to stimuli, metabolic processes, and the regulation of biological processes, with significant annotation to the metabolic terms of terpenoids and polyketides. Two expression modules of genes highly related to ginsenoside biosynthesis were obtained via WGCNA. Our study provides a reference system for the targeted ginsenoside production using MeJA as an inducer, and also provides genetic and gene resources for subsequently validating genes related to the regulation of ginsenoside biosynthesis using weighted gene co-expression network analysis (WGCNA).

Prognostic implications of metabolism-related genes in acute myeloid leukemia.

Acute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development , presenting new opportunities for prognostic assessment and therapeutic intervention. Metabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance. An 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment. A novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML.

Differential gene expression and chromatin accessibility in the medial prefrontal cortex associated with individual differences in rat behavioral models of opioid use disorder.

Opioid use disorder (OUD) is a neuropsychological disease that has a devastating impact on public health. Substantial individual differences in vulnerability exist, the neurobiological substrates of which remain unclear. To address this question, we investigated genome-wide gene transcription (RNA-seq) and chromatin accessibility (ATAC-seq) in the medial prefrontal cortex (mPFC) of male and female rats exhibiting differential vulnerability in behavioral paradigms modeling different phases of OUD: Withdrawal-Induced Anhedonia (WIA), Demand, and Reinstatement. Ingenuity Pathway Analysis (IPA) of RNA-seq revealed greater changes in canonical pathways in Resilient (vs. Saline) rats in comparison to Vulnerable (vs. Saline) rats across 3 paradigms, suggesting brain adaptations that might contribute to resilience to OUD across its trajectory. Analyses of gene networks and upstream regulators implicated processes involved in oligodendrocyte maturation and myelination in WIA, neuroinflammation in Demand, and metabolism in Reinstatement. Motif analysis of ATAC-seq showed changes in chromatin accessibility to a small set of transcription factor (TF) binding sites as a function either of opioid exposure (i.e., morphine versus saline) generally or of individual vulnerability specifically. Some of these were shared across the 3 paradigms and others were unique to each. In conclusion, we have identified changes in biological pathways, TFs, and their binding motifs that vary with paradigm and OUD vulnerability. These findings point to the involvement of distinct transcriptional and epigenetic mechanisms in response to opioid exposure, vulnerability to OUD, and different stages of the disorder.

Unravelling the protective effects of peptide isolated from marine sponge extract MS01 against SH-SY5Y cell line and its in-silico pharmacokinetic analysis

Parkinson's disease (PD) is characterised by developing postural instability, resting seizures, tremors, and a movement problem coupled with stiffness. All the available drugs can improve motor function considerably, but they can also have negative side effects, especially if the problem gets worse. The structure-activity relation was performed in the DISCOVERY STUDIO V2.5.5 pharmacophore model using the HypoGen algorithm for a training set of 15 compounds. Here, xenin peptide fits well with a least cost difference and a fit value of 10.46, indicating a favourable pharmacological characteristic. Therefore, we tried applying gene network analysis in cytoHubba to find the hub gene for PD in Danio rerio and Homo sapiens, as zebrafish and humans share many disease proteins and processes. Molecular docking studies for the hub gene polyubiquitin B from Danio rerio and Parkin from Homo sapiens, as well as the peptide xenin obtained from the marine sponge extract MS01 was performed. The peptide exhibits a substantial binding affinity with the receptor UBB through 8 and PRKN through 4 intermolecular hydrogen bonds in their bonded and non-bonded interactions, although it has little effect on the protein structure, according to simulation studies and dynamical free energy calculations. The protein structure has also been stabilised in terms of energy, secondary structure, and flexibility by the peptide binding. In addition to in-silico analysis the extract was tested in-vitro on SH-SY5Y cells for its effectiveness against ROS and cell viability, which proved its qualitative effect.