Transcriptome Expression Data Research Articles

Genome-Wide Identification and Expression Analysis of TCP Transcription Factors Responding to Multiple Stresses in Arachis Hypogaea L.

The TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING-CELL-FACTOR (TCP) gene family, a plant-specific transcription factor family, plays pivotal roles in various processes such as plant growth and development regulation, hormone crosstalk, and stress responses. However, a comprehensive genome-wide identification and characterization of the TCP gene family in peanut has yet to be fully elucidated. In this study, we conducted a genome-wide search and identified 51 TCP genes (designated as AhTCPs) in peanut, unevenly distributed across 17 chromosomes. These AhTCPs were phylogenetically classified into three subclasses: PCF, CIN, and CYC/TB1. Gene structure analysis of the AhTCPs revealed that most AhTCPs within the same subclade exhibited conserved motifs and domains, as well as similar gene structures. Cis-acting element analysis demonstrated that the AhTCP genes harbored numerous cis-acting elements associated with stress response, plant growth and development, plant hormone response, and light response. Intraspecific collinearity analysis unveiled significant collinear relationships among 32 pairs of these genes. Further collinear evolutionary analysis found that peanuts share 30 pairs, 24 pairs, 33 pairs, and 100 pairs of homologous genes with A. duranensis, A. ipaensis, Arabidopsis thaliana, and Glycine max, respectively. Moreover, we conducted a thorough analysis of the transcriptome expression profiles in peanuts across various tissues, under different hormone treatment conditions, in response to low- and high-calcium treatments, and under low-temperature and drought stress scenarios. The qRT-PCR results were in accordance with the transcriptome expression data. Collectively, these studies have established a solid theoretical foundation for further exploring the biological functions of the TCP gene family in peanuts, providing valuable insights into the regulatory mechanisms of plant growth, development, and stress responses.

Development and experimental validation of dephosphorylation-related biomarkers to assess prognosis and immunotherapeutic response in gliomas.

Gliomas are common aggressive brain tumors with poor prognosis. Dephosphorylation-related biomarkers are in a void in gliomas. This study aims to construct a validated prognostic risk model for dephosphorylation, which will provide new directions for clinical treatment, prognostic assessment, and temozolomide (TMZ) resistance in glioma patients. Screening dephosphorylation-related genes (DRGs) and transcriptome expression data from The Cancer Genome Atlas (TCGA), Molecular signatures database (MSigDB) and constructing risk scoring models. Kaplan-Meier (K-M), nomogram and ROC curve were used to assess the predictive efficacy of the model. Gene set enrichment analysis (GSEA), immune cell infiltration, immunotherapy response and chemotherapeutic drug sensitivity analysis were performed in this study. The correlation between chemotherapeutic drugs and the half maximal inhibitory concentration (IC50) values of 12 DRGs was analyzed. Cell division cycle 25A (CDC25A) and TMZ were screened and verified by experiments. Quantitative Real-Time PCR (qRT-PCR) detection of mRNA expression of 12 genes in human normal glial cells and two glioma cell lines. Transfection techniques overexpressed and knocked down CDC25A. qRT-PCR and Western Blot (WB) were used to detect the mRNA and protein expression levels of CDC25A. Subsequently, verify the effect of CDC25A on TMZ resistance in glioma cells. The model established in this study was able to accurately predict the prognosis of glioma patients. Besides, there were significant differences in GSEA, immune cell infiltration, immunotherapeutic response and chemotherapeutic drug sensitivity analysis between glioma patients in the high and low risk groups. The results of CCK8 experiments showed that overexpression of CDC25A increased the susceptibility of U251 and LN229 cells to TMZ, and knockdown of CDC25A attenuated the susceptibility of U251 and LN229 cells to TMZ.

Characteristics and multi-omics analysis of spontaneous spondyloarthritis in non-human primates: Case report.

Spondyloarthritis is a prevalent and persistent condition that significantly impacts the quality of life. Its intricate pathological mechanisms have led to a scarcity of animal models capable of replicating the disease progression in humans, making it a prominent area of research interest in the field. To delve into the pathological and physiological traits of spontaneous non-human primate spondyloarthritis, this study meticulously examined the disease features of this natural disease model through an array of techniques including X-ray imaging, MRI imaging, blood biochemistry, markers of bone metabolism, transcriptomics, proteomics, and metabolomics. X-ray imaging results revealed that crab-eating monkeys (Macaca fascicularis) with spontaneous spondyloarthritis developed bone spurs in the spine and experienced bone destruction in peripheral joints. MRI imaging further confirmed inflammatory changes in the spine and facet joints of these monkeys, along with inflammation and bone destruction in peripheral joints. Blood biochemistry analysis showed abnormalities in liver function and kidney function indicators in crab-eating monkeys with spontaneous spondyloarthritis. Analysis of bone metabolism-related markers showed a decrease in bone formation (BGP) and bone resorption (β-CTx). A thorough correlation analysis was conducted on the transcriptome and proteome expression data, revealing a significant positive correlation between the two datasets. A total of eight genes and proteins with high expression levels were identified as common to both the transcriptome and proteome. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on these co-expressed genes and proteins, indicating a predominant enrichment in the IL-17 signaling pathway, with S100A8 and S100A9 identified as the core proteins. Further analysis using clinical data in conjunction with proteome data through Weighted Gene Co-expression Network Analysis (WGCNA) demonstrated a significant positive correlation between the high expression of S100A8 and S100A9 and the clinical phenotypes of spinal abnormalities, thereby corroborating the close association of S100A8 and S100A9 with the phenotype of spondyloarthritis. Human clinical studies have already established a link between S100A8 and S100A9 and autoimmune-related arthritic diseases, suggesting that the spontaneous spondyloarthritis observed in crab-eating macaques may be related to autoimmune diseases. It is hypothesized that S100A8 and S100A9 could serve as potential predictive biomarkers for spondyloarthritis in non-human primates.

Genome-wide identification and expression analysis of the WRKY gene family reveal essential roles in abiotic stress responses and polysaccharides and flavonoids biosynthesis in Platostoma palustre (Blume) A. J. Paton

BackgroundPlatostoma palustre (Blume) A. J. Paton is an economic crop with medicinal and edible properties. WRKY transcription factors are widely involved in the growth and development, response to adversity stress, and hormone transduction of plants. The identification of the WRKY gene family has been completed in many plants, however, it has not yet been identified and analyzed at the genome-wide level in P. palustre.ResultsIn this study, we identified 133 PpWRKY gene family members (PpWRKYs) at the whole genome level of P. palustre, which were unevenly distributed on 15 chromosomes. Based on their protein structure and phylogenetic characteristics, the 133 PpWRKYs were divided into 3 subgroups. Segmental duplication events might play a crucial role in the expansion of the PpWRKY gene family. Through the transcriptome expression data analysis, the expression profiles of PpWRKY genes under Cd, red light, salt, and drought stresses were analyzed in this study, suggesting that WRKY transcription factors may play a crucial role in responding to different abiotic stresses in P. palustre. Notably, PpWRKY92 exhibited simultaneous responses to Cd, light intensity, salt, and drought stresses. Additionally, PpWRKY21, 75, 90, 52, 124, 39, 115, 122, 20, and 76 demonstrated a strong correlation with both monosaccharides and flavonoids. Taken together, PpWRKY20, 39, 75, 76, 90, 92, 115, 122, and 124 were found to be associated with the abiotic stress response and polysaccharides and flavonoids biosynthesis in P. palustre, except the low-expressed PpWRKY21 and 52.ConclusionThe present study laid the foundation for the abiotic stress response and metabolite regulation of this gene family in P. palustre.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Increased UBD Is a Potential Diagnostic and Prognostic Biomarker in Glioma.

Various studies have demonstrated that ubiquitin D (UBD) is overexpressed in different cancer types and may serve as a potential prognostic factor. However, additional research is necessary to establish the prognostic significance and possible role of UBD in glioma. Transcriptomic expression data from The Cancer Genome Atlas database (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify UBD expression differences in tumor and normal tissues. The relative levels of UBD in glioma and normal tissues were determined using qRT-PCR and WB. Logistic regression analysis was performed to investigate the association between UBD expression and clinicopathological characteristics of glioma patients. To evaluate the diagnostic and prognostic predictive values of UBD, we used Kaplan-Meier survival curves, Cox regression analysis, diagnostic receiver operating characteristic (ROC) curves, and nomogram model. We also conducted wound healing assays, transwell assays, EdU assays, and colony formation assays to verify the UBD function. Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as gene set enrichment analysis (GSEA), were employed to determine the functions of UBD. Finally, we performed the western blot assays to assess changes in EMT markers as well as p-PI3K, p-AKT, and p-mTOR expressions. Our study revealed a remarkable increase of UBD expression in glioma samples. Cox regression analysis demonstrated that high expression of UBD mRNA was an independent prognostic factor for overall survival (OS) in TCGA. ROC curve analysis showed that UBD expression levels could differentiate glioma from adjacent normal tissues accurately. Additionally, knockdown of UBD reduced the migration, invasion, and proliferation ability of glioma cells while UBD overexpression had the opposite effect. GSEA showed that the expression of UBD involved with various pathways including epithelial-mesenchymal transition (EMT), PI3K-AKT-mTOR signaling, P53 pathway, angiogenesis, inflammatory response, KRAS signaling, hypoxia, as well as TGF-β signaling. Furthermore, our findings suggest that UBD accelerates the activation of EMT and PI3K/AKT/mTOR pathway.

Infiltrating B-cell subtypes and associated hub genes in nasopharyngeal carcinoma identified from integrated single-cell and bulk-RNA sequencing data.

135 Background: Nasopharyngeal carcinoma (NPC) is associated with lymphocyte infiltration; however, the majority of research on NPC has focused on the role of T cells, with relatively little known about the roles of B cells and their subtypes. Therefore, we evaluated the prognostic value of CD20+ B cell density and B-cell subtypes along with their functional enrichment and hub genes in NPC. Methods: The prognostic value of CD20+ B-cell density for distant metastasis-free survival (DMFS), overall survival (OS), and progression-free survival (PFS) was explored by immunohistochemistry using multivariate analysis. Transcriptomic expression data from Gene Expression Omnibus (GEO) datasets were analyzed to identify B-cell subtypes and their functional enrichment in NPC tissues. Pseudotime trajectory analysis was performed to evaluate the B-cell differentiation trajectory and hub genes were identified using Cytoscape software. Results: Patients with NPC exhibiting a high infiltrating density of CD20+ B cells showed significantly better 5-year DMFS, OS, and PFS compared to those of patients with a low infiltrating density. Naïve B cells, switched memory B cells, exhausted B cells, and plasma cells were identified as key B-cell subtypes infiltrating NPC tumors, with naïve B cells showing the highest infiltration levels associated with a better prognosis. Naïve B cells were closely associated with immune pathways and the hub genes were typical markers for T and B cells. Conclusions: A high infiltrating density of B cells showed strong prognostic value in patients with NPC. Naïve B cells may play an important role in tumor immunity for NPC.

Genome-Wide Association and RNA-Seq Analyses Reveal a Potential Candidate Gene Related to Oil Content in Soybean Seeds.

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (-log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high-low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network's underlying marker-assisted selection (MAS) and oil content.

Molecular Cloning and Functional Identification of a Pericarp- and Testa-Abundant Gene's (AhN8DT-2) Promoter from Arachis hypogaea.

Cultivated peanut (Arachis hypogaea L.) is a key oil- and protein-providing legume crop of the world. It is full of nutrients, and its nutrient profile is comparable to that of other nuts. Peanut is a unique plant as it showcases a pegging phenomenon, producing flowers above ground, and after fertilization, the developing peg enters the soil and produces seeds underground. This geocarpic nature of peanut exposes its seeds to soil pathogens. Peanut seeds are protected by an inedible pericarp and testa. The pericarp- and testa-specific promoters can be effectively used to improve the seed defense. We identified a pericarp- and testa-abundant expression gene (AhN8DT-2) from available transcriptome expression data, whose tissue-specific expression was further confirmed by the qRT-PCR. The 1827bp promoter sequence was used to construct the expression vector using the pMDC164 vector for further analysis. Quantitative expression of the GUS gene in transgenic Arabidopsis plants showed its high expression in the pericarp. GUS staining showed a deep blue color in the pericarp and testa. Cryostat sectioning of stained Arabidopsis seeds showed that expression is only limited to seed coat (testa), and staining was not present in cotyledons and embryos. GUS staining was not detected in any other tissues, including seedlings, leaves, stems, and roots, except for some staining in flowers. Under different phytohormones, this promoter did not show an increase in expression level. These results indicated that the AhN8DT-2 promoter drives GUS gene expression in a pericarp- and testa-specific manner. The identified promoter can be utilized to drive disease resistance genes, specifically in the pericarp and testa, enhancing peanut seed defense against soil-borne pathogens. This approach has broader implications for improving the resilience of peanut crops and other legumes, contributing to sustainable agricultural practices and food security.

Ubiquitination-Related Gene Signature, Nomogram and Immune Features for Prognostic Prediction in Patients with Head and Neck Squamous Cell Carcinoma.

The objective of this research was to create a prognostic model focused on genes related to ubiquitination (UbRGs) for evaluating their clinical significance in head and neck squamous cell carcinoma (HNSCC) patients. The transcriptome expression data of UbRGs were obtained from The Cancer Genome Atlas (TCGA) database, and weighted gene co-expression network analysis (WGCNA) was used to identify specific UbRGs within survival-related hub modules. A multi-gene signature was formulated using LASSO Cox regression analysis. Furthermore, various analyses, including time-related receiver operating characteristics (ROCs), Kaplan-Meier, Cox regression, nomogram prediction, gene set enrichment, co-expression, immune, tumor mutation burden (TMB), and drug sensitivity, were conducted. Ultimately, a prognostic signature consisting of 11 gene pairs for HNSCC was established. The Kaplan-Meier curves indicated significantly improved overall survival (OS) in the low-risk group compared to the high-risk group (p < 0.001), suggesting its potential as an independent and dependable prognostic factor. Additionally, a nomogram with AUC values of 0.744, 0.852, and 0.861 at 1-, 3-, and 5-year intervals was developed. Infiltration of M2 macrophages was higher in the high-risk group, and the TMB was notably elevated compared to the low-risk group. Several chemotherapy drugs targeting UbRGs were recommended for low-risk and high-risk patients, respectively. The prognostic signature derived from UbRGs can effectively predict prognosis and provide new personalized therapeutic targets for HNSCC.

P-707 Discovery of novel serum biomarkers for premature ovarian insufficiency through machine-learning bioinformatic analysis

Abstract Study question Can we develop predictive, practical biomarkers for premature ovarian insufficiency (POI) from normal ovaries and human blood, tested and validated by machine-learning (ML) algorithm? Summary answer Applying random forest and XGBoost algorithms, we pinpointed 60 significant genes from ovarian tissues; transcriptome analysis identified RPM1 as the most significant biomarker. What is known already Accelerated ovarian aging has been suggested as one of the possible underlying mechanisms of POI; previous literature has focused on identifying its cause in genetic realm, commonly adopting first-generation sequencing (Sanger) and next-generation sequencing (NGS). However, such powerful tools are still limited in accurately depicting the complete transcript information of ovarian tissue. Moreover, theoretical value of such disease-predictive gene candidates needs to be validated using human tissues and blood, and finally, underlying mechanisms should be confirmed with ideal animal model and cell line. Study design, size, duration To overcome the existing limitations and maximize clinical relevance of disease-predictive gene analysis and theoretical biomarker exploration, the current study incorporated the transcriptome expression data from both young and old ovaries obtained from the Genotype-Tissue Expression (GTEx) database. Using supervised machine learning techniques, potential marker genes were identified, and a meaningful predictive index was subsequently developed using human plasma samples of control and POI women with and without pregnancy history. Participants/materials, setting, methods The performance evaluation of two ML models were achieved using the area under receiver operating characteristic curve (AUROC), accuracy, kappa, and F1 score, among various age groups. The expression profiles of final candidate marker genes were analyzed within the GTEx datasets, in relation to their correlation pattern with age. Finally, the potential transcriptome biomarkers were quantitatively evaluated using human blood samples from 92 controls and 108 POI women visiting a tertiary center in 2023. Main results and the role of chance We found 41 significant genes using the random forest algorithm and 19 significant genes using the XGboost algorithm. We then tested the ML-characterized 7 final candidate biomarkers in human blood samples (all p &amp;lt; 0.05). The AUROC and interventional predictive index results demonstrated the potential of RPM1 as a predictive marker with AUROC of 0.784, comparable to the AUROC of anti-mullerian hormone (AMH) as 0.659 and inhibin B (INHB) as 0.646. Limitations, reasons for caution To avoid potential biases, RPMs should be tested in a larger, multinational cohort with different genetic and geographic backgrounds, possibly incorporating Mendelian randomization method to estimate the causal effect of RPMs on POI. Finally, to ultimately confirm their therapeutic potential, preclinical studies should be considered using RPM recombinant proteins. Wider implications of the findings Our results proposed that predictive biomarkers for POI could be associated with premature, accelerated ovarian aging of the disease mechanism, and these biomarkers can be developed as evaluation metrics for the efficacy, progress, and prognosis of interventions and treatment modalities assigned to POI women seeking fertility. Trial registration number 202302240001

Time-course transcriptome data of silk glands in day 0–7 last-instar larvae of Bombyx mori (w1 pnd strain)

Time-course transcriptome expression data were constructed for four parts of the silk gland (anterior, middle, and posterior parts of the middle silk gland, along with the posterior silk gland) in the domestic silkworm, Bombyx mori, from days 0 to 7 of the last-instar larvae. For sample preparation, silk glands were extracted from one female and one male larva every 24 hours accurately after the fourth ecdysis. The reliability of these transcriptome data was confirmed by comparing the transcripts per million (TPM) values of the silk gene and quantitative reverse transcription PCR results. Hierarchical cluster analysis results supported the reliability of transcriptome data. These data are likely to contribute to the progress in molecular biology and genetic research using B. mori, such as elucidating the mechanism underlying the massive production of silk proteins, conducting entomological research using a meta-analysis as a model for lepidopteran insect species, and exploring medical research using B. mori as a model for disease species by utilising transcriptome data.

Combinatorial metabolomic and transcriptomic analysis of muscle growth in hybrid striped bass (female white bass Morone chrysops x male striped bass M. saxatilis)

BackgroundUnderstanding growth regulatory pathways is important in aquaculture, fisheries, and vertebrate physiology generally. Machine learning pattern recognition and sensitivity analysis were employed to examine metabolomic small molecule profiles and transcriptomic gene expression data generated from liver and white skeletal muscle of hybrid striped bass (white bass Morone chrysops x striped bass M. saxatilis) representative of the top and bottom 10 % by body size of a production cohort.ResultsLarger fish (good-growth) had significantly greater weight, total length, hepatosomatic index, and specific growth rate compared to smaller fish (poor-growth) and also had significantly more muscle fibers of smaller diameter (≤ 20 µm diameter), indicating active hyperplasia. Differences in metabolomic pathways included enhanced energetics (glycolysis, citric acid cycle) and amino acid metabolism in good-growth fish, and enhanced stress, muscle inflammation (cortisol, eicosanoids) and dysfunctional liver cholesterol metabolism in poor-growth fish. The majority of gene transcripts identified as differentially expressed between groups were down-regulated in good-growth fish. Several molecules associated with important growth-regulatory pathways were up-regulated in muscle of fish that grew poorly: growth factors including agt and agtr2 (angiotensins), nicotinic acid (which stimulates growth hormone production), gadd45b, rgl1, zfp36, cebpb, and hmgb1; insulin-like growth factor signaling (igfbp1 and igf1); cytokine signaling (socs3, cxcr4); cell signaling (rgs13, rundc3a), and differentiation (rhou, mmp17, cd22, msi1); mitochondrial uncoupling proteins (ucp3, ucp2); and regulators of lipid metabolism (apoa1, ldlr). Growth factors pttg1, egfr, myc, notch1, and sirt1 were notably up-regulated in muscle of good-growing fish.ConclusionA combinatorial pathway analysis using metabolomic and transcriptomic data collectively suggested promotion of cell signaling, proliferation, and differentiation in muscle of good-growth fish, whereas muscle inflammation and apoptosis was observed in poor-growth fish, along with elevated cortisol (an anti-inflammatory hormone), perhaps related to muscle wasting, hypertrophy, and inferior growth. These findings provide important biomarkers and mechanisms by which growth is regulated in fishes and other vertebrates as well.

Oxidative DNA damage contributes to usnic acid-induced toxicity in human induced pluripotent stem cell-derived hepatocytes.

Dietary supplements containing usnic acid have been increasingly marketed for weight loss over the past decades, even though incidences of severe hepatotoxicity and acute liver failure due to their overuse have been reported. To date, the toxic mechanism of usnic acid-induced liver injury at the molecular level still remains to be fully elucidated. Here, we conducted a transcriptomic study on usnic acid using a novel in vitro hepatotoxicity model employing human induced pluripotent stem cell (iPSC)-derived hepatocytes. Treatment with 20 μM usnic acid for 24 h caused 4272 differentially expressed genes (DEGs) in the cells. Ingenuity Pathway Analysis (IPA) based on the DEGs and gene set enrichment analysis (GSEA) using the whole transcriptome expression data concordantly revealed several signaling pathways and biological processes that, when taken together, suggest that usnic acid caused oxidative stress and DNA damage in the cells, which further led to cell cycle arrest and eventually resulted in cell death through apoptosis. These transcriptomic findings were subsequently corroborated by a variety of cellular assays, including reactive oxygen species (ROS) generation and glutathione (GSH) depletion, DNA damage (pH2AX detection and 8-hydroxy-2'-deoxyguanosine [8-OH-dg] assay), cell cycle analysis, and caspase 3/7 activity. Collectively, the results of the current study accord with previous in vivo and in vitro findings, provide further evidence that oxidative stress-caused DNA damage contributes to usnic acid-induced hepatotoxicity, shed new light on molecular mechanisms of usnic acid-induced hepatotoxicity, and demonstrate the usefulness of iPSC-derived hepatocytes as an in vitro model for hepatotoxicity testing and prediction.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

PurposeThe objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. MethodsAML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. ConclusionLncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Molecular mechanisms underlying structural plasticity of electroconvulsive therapy in major depressive disorder.

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.

Integrated analysis of transcriptome and miRNAome reveals the heat stress response of Pinellia ternata seedlings

Pinellia ternata (Thunb.) Briet., a valuable herb native to China, is susceptible to the “sprout tumble” phenomenon because of high temperatures, resulting in a significant yield reduction. However, the molecular regulatory mechanisms underlying the response of P. ternata to heat stress are not well understood. In this study, we integrated transcriptome and miRNAome sequencing to identify heat-response genes, microRNAs (miRNAs), and key miRNA-target pairs in P. ternata that differed between heat-stress and room-temperature conditions. Transcriptome analysis revealed extensive reprogramming of 4,960 genes across various categories, predominantly associated with cellular and metabolic processes, responses to stimuli, biological regulation, cell parts, organelles, membranes, and catalytic and binding activities. miRNAome sequencing identified 1,597 known/conserved miRNAs that were differentially expressed between the two test conditions. According to the analysis, genes and miRNAs associated with the regulation of transcription, DNA template, transcription factor activity, and sequence-specific DNA binding pathways may play a major role in the resistance to heat stress in P. ternata. Integrated analysis of the transcriptome and miRNAome expression data revealed 41 high-confidence miRNA-mRNA pairs, forming 25 modules. MYB-like proteins and calcium-responsive transcription coactivators may play an integral role in heat-stress resistance in P. ternata. Additionally, the candidate genes and miRNAs were subjected to quantitative real-time polymerase chain reaction to validate their expression patterns. These results offer a foundation for future studies exploring the mechanisms and critical genes involved in heat-stress resistance in P. ternata.

ShinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration

Abstract Despite advancements in deep learning for histopathology, integrating these insights with multi-omics data to uncover clinically relevant omics pathway-level signatures remains a challenge. Our study addresses this gap by applying unsupervised learning techniques on pan-cancer multi-omics data, leveraging 3,080 Hematoxylin and Eosin (H&amp;E) images from 1,010 patients in Clinical Proteomic Tumor Analysis Consortium (CPTAC) to uncover omics pathway-level signatures that drive discernable morphology phenotypes at the tissue level. First, imaging models were trained to predict clinical and mutation outcomes, and thereafter, integrated with transcriptomic and proteomic expression data using sparse canonical correlation analysis. Our findings reveal that images of TP53 mutated samples exhibited increased nuclear size and dense lymphoplasmacytic infiltration. These morphological changes correlated with neutrophil and macrophage signaling at the proteomic level, and IL-1 mediated signaling at the transcriptomic level, highlighting the complementary perspectives different omics can provide. To further elucidate immune interactions, we applied multi-omics deconvolution to identify 7 immune subtypes, characterizing each with gene set enrichment, germline DNA variations, and kinase activations. We show that imaging models trained to predict these subtypes differentiate tissue morphologies corresponding to immune enrichment (AUROC: 0.84). To further confirm the robustness of our approach, we trained models to predict co-regulated proteomic modules clustered by independent component analysis. We demonstrate a signature in pan-squamous tumors, consisting of T cell markers and interferon-gamma response proteins like CD4, CD48, and GBP5, which imaging models can successfully predict from histopathology. Manual review from pathologists confirmed lymphocytic T-cell density as a differentiator in samples with the highest and lowest T-cell signaling. Together, these approaches demonstrate that genomic characteristics discovered via unbiased mining of pan-cancer multi-omics data manifest as quantitative imaging phenotypes. These results underscore the potential of multi-omics and digital pathology to integratively uncover and confirm new cancer biology. Our ongoing work will explore predicting drug response and survival from morphology patterns related to multi-omics signatures. Citation Format: Joshua Wang, Runyu Hong, Jimin Tan, Wenke Liu, David Fenyȯ. Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 888.