Identification Of Differentially Expressed Genes Research Articles

Transcriptomic analysis delineates potential regulatory network as therapeutic alternatives in chronic myeloid leukemia

BackgroundChronic myeloid leukemia (CML) relapse and progression after discontinuation of tyrosine kinase inhibitors (TKI) therapy in the chronic phase (CP) are attributed to primitive quiescent leukemic progenitor cells (LPCs). This study aimed to identify key differentially expressed genes (DEGs) involved in molecular pathways alternative to BCR-ABL, which could lead to the eradication of leukemic stem/progenitor cells.MethodsmRNA expression profiling dataset comprising newly diagnosed untreated CP CML leukemic progenitor cell samples n=5 and normal hematopoietic progenitor cell samples n=5 were selected for DEGs identification. A weighted gene co-expression network was constructed to identify significant hub modules. Protein–protein interaction (PPI) network construction and pathway enrichment analysis provided additional information on the biological pathways involved. The genes involved in the resultant PPI network were subjected to a gene transcriptional regulatory network based on transcription factors (TFs) and microRNAs (miRNAs) involved in the target genes.ResultsA total of 709 genes that were differentially expressed among those corresponding to p<0.05 and log2FC>0.5 0.5$$\\end{document}]]>. The WGCNA showed a significant hub module comprising 267 DEGs. The major pathways of the hub genes involved in the PPI network at the highest confidence score >0.9 0.9$$\\end{document}]]> were related to mitochondrial translational elongation. The integrated gene transcriptional regulatory network revealed a TF (ETS1), two miRNAs (miR-34a-5p and miR-205-5p) targeting a hub gene (GFM1) as the highest-order motif or subnetwork in the three-node miRNA feed-forward loop.ConclusionsThe study suggests that the regulatory feed-forward loop may provide a potential therapeutic target in CML. Consequently, the mitochondrial translation pathway may be explored as an alternative to BCR-ABL pathways, which could aid in eliminating or increasing sensitization of CML progenitor/stem cells to TKI treatment.

Bioinformatics Analysis Screening and Identification of Key Biomarkers and Drug Targets in Human Glioblastoma.

Glioblastoma is the most common type of brain cancer, with a prognosis that is unfortunately poor. Despite considerable progress in the field, the intricate molecular basis of this cancer remains elusive. The aim of this study was to identify genetic indicators of glioblastoma and reveal the processes behind its development. The advent and integration of supercomputing technology have led to a significant advancement in gene expression analysis platforms. Microarray analysis has gained recognition for its pivotal role in oncology, crucial for the molecular categorization of tumors, diagnosis, prognosis, stratification of patients, forecasting tumor responses, and pinpointing new targets for drug discovery. Numerous databases dedicated to cancer research, including the Gene Expression Omnibus (GEO) database, have been established. Identifying differentially expressed genes (DEGs) and key genes deepens our understanding of the initiation of glioblastoma, potentially unveiling novel markers for diagnosis and prognosis, as well as targets for the treatment of glioblastoma. This research sought to discover genes implicated in the development and progression of glioblastoma by analyzing microarray datasets GSE13276, GSE14805, and GSE109857 from the GEO database. DEGs were identified, and a function enrichment analysis was performed. Additionally, a protein-protein interaction network (PPI) was constructed, followed by module analysis using the tools STRING and Cytoscape. The analysis yielded 88 DEGs, consisting of 66 upregulated and 22 downregulated genes. These genes' functions and pathways primarily involved microtubule activity, mitotic cytokinesis, cerebral cortex development, localization of proteins to the kinetochore, and the condensation of chromosomes during mitosis. A group of 27 pivotal genes was pinpointed, with biological process analysis indicating significant enrichment in activities, such as division of the nucleus during mitosis, cell division, maintaining cohesion between sister chromatids, segregation of sister chromatids during mitosis, and cytokinesis. The survival analysis indicated that certain genes, including PCNA clamp-associated factor (PCLAF), ribonucleoside- diphosphate reductase subunit M2 (RRM2), nucleolar and spindle-associated protein 1 (NUSAP1), and kinesin family member 23 (KIF23), could be instrumental in the development, invasion, or recurrence of glioblastoma. The identification of DEGs and key genes in this study advances our comprehension of the molecular pathways that contribute to the oncogenesis and progression of glioblastoma. This research provides valuable insights into potential diagnostic and therapeutic targets for glioblastoma.

Abstract 71: Analysis of First Morning Urine Transcriptomes in Normotensive and Hypertensive Patients Identify Upregulated Inflammatory and Signaling Pathways Associated with Hypertension

Introduction: Hypertension (HTN) remains poorly controlled at the population level. The kidney plays a prominent role in HTN, but the molecular mechanisms are unclear. We enrolled a cohort of patients with HTN, and controls, to study new molecular signatures in the transcriptome of cells shed in first morning urine samples. Methods: We collected demographics, 24-h and first-morning urine collections from 80 non-diabetic patients aged &gt;40 with HTN and 20 controls (no HTN). Descriptive variables were summarized. Gene expression was assessed after Next Gen sequencing of mRNA obtained from cells pulled down from first-morning urine and assessed for differentially expressed genes (DEG) (&lt;1 Log2 fold-, p&lt;0.05 FDR) by the Wilcoxon Rank-Sum test. Pathway analysis for DEGs was performed with an in-house Python script that highlights genes associated with cardiovascular diseases including HTN, CKD, type 2 diabetes, inflammation, surface proteins (GPCR, transporters, etc), and the kinome. Results: Cohort characteristics are shown in Table 1. After QC, full transcriptomes were analyzed for 11 control and 34 HTN subjects. 20,800 transcripts were identified (including known genes, ORFs and LncRNAs) and compared between groups. 916 DEGs (FDR p&lt;0.05) were identified: 911 genes up- and 5 down-regulated. In HTN subjects, there was overrepresentation of genes in inflammatory signaling pathways (12.5% of all DEGs) including those in IFN-alpha, IFN-gamma, Interleukin (IL)9, IL17 and IL18, and innate immunity. 15 genes, associated with HTN, and 45, associated with type-2 diabetes, at the genome-wide level were up-regulated in the HTN cohort including (LINC00592, ADRA1D, SLC39A12, WNT3A, RXFP2, DEFB136, PDE3A, OPRM1, RIBC2, GRIN2A, CHEK2, HOXA3, CCDC149, COL4A2). Transcripts for major apical nephron NaCl transporters were not significantly different, however, SLC12A5 (K/Cl cotransporter KCC2) and the voltage gated K channels (KCNE1, KCNE2) were up-regulated in HTN patients. Conclusions: We conclude that analysis of transcriptomes of cells shed in first morning urine of control and HTN patients allows for the identification of DEG. These data align well with our previous finding indicating that renal inflammation is associated with difficult-to-control hypertension, and as shown here, are also associated with HTN in general. This method allows for discovery of new inflammatory pathways that could render additional treatments for HTN or CKD.

Effects of Foliar Ca and Mg Nutrients on the Respiration of ‘Feizixiao’ Litchi Pulp and Identification of Differential Expression Genes Associated with Respiration

The ‘Feizixiao’ litchi cultivar, predominantly grown in Hainan Province, faces the issue of “sugar receding” during fruit ripening. The application of mixed foliar nutrients containing calcium and magnesium (Ca+Mg) during the fruit pericarp’s full coloring stage was investigated to overcome this issue. Experimental trials unveiled significant alterations in litchi pulp physiochemical properties, including the main nutrient and flavor quality, the total respiration rates of the main respiratory pathways, and the activities of some important enzymes associated with Embden–Meyerhof–Parnas (EMP), the tricarboxylic acid cycle (TCA) and the pentose phosphate pathway (PPP). The Ca+Mg treatment showed higher sugar levels than the control (CK) during ripening. Notably, the application of Ca+Mg in litchi pulp inhibited respiration rates through the EMP, TCA, and PPP pathways, resulting in a strong effect. RNA sequencing analysis revealed the impact of Ca+Mg treatment on respiratory pathways, revealing differentially expressed genes (DEGs) such as pyruvate PK1, PK2 (pyruvate kinase), and PDC (pyruvate dehydrogenase complex), validated through qRT-PCR with a significant correlation to RNA-seq results. In general, Ca+Mg treatment during litchi fruit ripening overcame “sugar receding” by inhibiting the expression of respiration key metabolic pathway genes. These findings provide insights for enhancing cultivation management strategies.

Abstract 5978: Therapeutic targeting of MELK using a drug repurposing approach to combat TNBC cells

Abstract Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer linked to high chemo-resistant metastatic cases and limited therapeutic options due to the absence of conventional bioreceptors. Therefore, to treat TNBC, it is imperative to identify promising druggable biological targets. Experimental Procedures: An integrated bioinformatic analysis of microarray datasets was performed to identify significantly differentially expressed genes (DEGs) in TNBC. For selecting a potent therapeutic target, the function and significance of the upregulated DEGs in TNBC was investigated by an in silico study employing the UALCAN portal, String database, KM plotter, and Enrichr tools. This analysis included gene expression validation, insights into protein-protein interactions, survival analysis, and pathway mapping of the genes. To obtain potent drugs binding to the selected target MELK (maternal embryonic leucine zipper kinase), a drug repurposing approach was employed starting with virtual screening followed by molecular dynamic simulation (MDS) using GROMACS. Further, the anti-cancer activity of the top two drugs was validated by live-dead imaging, gene expression analyses, MTT-based cell viability and ROS detection assays. Results: Based on a stringent criterion for significant DEG identification (log2|FC|&amp;gt;1 &amp; adjust P-value &amp;lt; 0.05), 25 overlapping upregulated genes were obtained from three selected datasets. Further, in silico analysis revealed that MELK is significantly upregulated across the four stages and subtypes of breast cancer, including TNBC. Higher MELK expression and its oncogenic interactions correlated with poor overall survival of TNBC patients. Evidence suggests that the existing inhibitor for MELK (OTSSP167) has shown poor specificity. Therefore, to block its activity, a library of 1293 FDA-approved drugs was screened against the kinase domain of MELK. MDS study and analysis using parameters like RMSD, RMSF and pair distance revealed that the top 10 drugs were strongly stabilized by H-bonds in the binding pocket of MELK, with minimal deviation and reduced fluctuations of MELK upon drug binding. Binding free energy calculation of drug-MELK interaction using the MM-PBSA method revealed that nine drugs possessed higher binding energy than the inhibitor. The top two drugs obtained from the study (originally kinase and reductase inhibitors respectively) were further validated for their in vitro anti-cancer efficacy in MDA-MB-231 and MDA-MB-468 cells. Treatment with these drugs exhibited (a) a dose-dependent decrease in proliferation, (b) significant cell death, (c) a marked increase in the cellular ROS levels, and (d) a decrease in the expression of MELK and its downstream targets in TNBC cell lines. Conclusion: The present study opens the avenue for using repurposed drugs as potential therapeutic molecules against MELK activity to combat TNBC progression. Citation Format: Arisha Arora, Shilpi Sarkar, Siddhartha S. Ghosh. Therapeutic targeting of MELK using a drug repurposing approach to combat TNBC cells [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 5978.

Bioinformatics to analyze the differentially expressed genes in different degrees of Alzheimer's disease and their roles in progress of the disease.

Employing bioinformatics approaches, this investigation pinpointed pivotal differentially expressed genes (DEGs) across the spectrum of Alzheimer's disease (AD), from incipient to severe stages, using the GSE28146 dataset from the GEO repository. Analytical methods included DEG identification via the limma package in R, coupled with GO and KEGG pathway analyses through clusterProfiler, to discern biological processes and pathway involvements. Key findings spotlighted the roles of proteasome subunits PSMB4, PSMB8, PSMC4, and PSMD6 in the early stage, ribosomal proteins RPS3 and RPL11 during moderate AD, and mitochondrial components COX5B, COX6B2, and COX7A2 in severe AD, underscoring their importance in the disease's pathogenesis. Conclusively, these results not only delineate the dynamic genetic shifts accompanying AD progression but also propose critical biomarkers for potential therapeutic targeting, offering a consolidated basis for future AD research and treatment development. This offered a novel idea for analyzing the pathogenesis and development of AD and investigation of targeted drugs.

New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease.

One of the severe monogenic conditions with the highest prevalence in the globe is sickle cell disease. Although the significance of chronic anemia, hemolysis, and vasculopathy has been established, hemoglobin polymerization, which results in erythrocyte stiffness and Vaso-occlusion, is important to the pathophysiology of this disease. Clinical management is elementary, and there is scant reliable data for many treatments. The onset of cerebrovascular illness and cognitive impairment are two of the major issues associated with sickle cell disease in children, and it is only now that researchers are beginning to understand how blood transfusions and hydroxycarbamide can prevent these complications. When Vaso occlusion and inflammation occur repeatedly, the majority of organs are gradually damaged, including the brain, kidneys, lungs, bones, and cardiovascular system. This damage worsens with age. In our study, we focused on the specific pathways which are affected by the involvement of effected genes. Firstly, we retrieved the gene datasets from the publically available data source website DisGNET. Using literature-based genes, we identified 290 highly regulated genes that are directly associated with sickle cell disease. We subsequently performed a gene expression analysis and extracted a gene set using GEO2R analysis, which was then used to prune 290 differentially expressed genes (DEGs). After pruning we got 60 highly expressed genes. After identification of DEGs, we used these genes for pathway analysis. For the pathway analysis, we used Reactome software and we found that these DEGs are directly associated with 7 different pathways, which are alpha beta signaling pathways, 15 antiviral mechanism, Oligoadenylate synthetase (OAS) antiviral response, interleukin 1 signaling pathways, interleukin 4 and 13, interleukin 10 signaling pathway, and aspirin ADME pathway. After pathway analysis, we can exactly relate how sickle cell disease alters the gene expression and how these genes affect the different pathways. Additionally, we performed gene ontology of 60 genes and identified the gene biological process, cellular component, and molecular functions as we mentioned in our results. With the help of our study data, there is a chance for pre-identification of sickle cell disease person. Our gene result was used as a biomarker of sickle cell disease. In this paper, our result is the primary approach for sickle cell disease; with the help of this paper any researcher can get their primary data and use that for further research.

Identification of Hub Genes and Potential Molecular Pathogenesis in Substantia Nigra in Parkinson's Disease via Bioinformatics Analysis.

Parkinson's disease (PD) is the second most common neurodegenerative disease, with significant socioeconomic burdens. One of the crucial pathological features of PD is the loss of dopaminergic neurons in the substantia nigra (SN). However, the exact pathogenesis remains unknown. Moreover, therapies to prevent neurodegenerative progress are still being explored. We performed bioinformatics analysis to identify candidate genes and molecular pathogenesis in the SN of patients with PD. We analyzed the expression profiles, GSE49036 and GSE7621, which included 31 SN tissues in PD samples and 17 SN tissues in healthy control samples, and identified 86 common differentially expressed genes (DEGs). Then, GO and KEGG pathway analyses of the identified DEGs were performed to understand the biological processes and significant pathways of PD. Subsequently, a protein-protein interaction network was established, with 15 hub genes and four key modules which were screened in this network. The expression profiles, GSE8397 and GSE42966, were used to verify these hub genes. We demonstrated a decrease in the expression levels of 14 hub genes in the SN tissues of PD samples. Our results indicated that, among the 14 hub genes, DRD2, SLC18A2, and SLC6A3 may participate in the pathogenesis of PD by influencing the function of the dopaminergic synapse. CACNA1E, KCNJ6, and KCNB1 may affect the function of the dopaminergic synapse by regulating ion transmembrane transport. Moreover, we identified eight microRNAs (miRNAs) that can regulate the hub genes and 339 transcription factors (TFs) targeting these hub genes and miRNAs. Subsequently, we established an mTF-miRNA-gene-gTF regulatory network. Together, the identification of DEGs, hub genes, miRNAs, and TFs could provide better insights into the pathogenesis of PD and contribute to the diagnosis and therapies.

Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.

Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation.

Identification of key immune and stress related genes and pathways by comparative analysis of the gene expression profile under multiple environmental stressors in pacific white shrimp (Litopenaeus vannamei)

Water salinity, pH, and nitrite concentration are considered environmental factors affecting the growth rate, survival, health, and physiological conditions of aquatic animals. The identification of key genes that are involved in the response to environmental stressors is essential for controlling stress in aquatic animals and sustainable aquaculture. In this study, RNA sequencing was performed to identify the differentially expressed genes (DEGs) and biological pathways that are involved in the response of the hepatopancreas to environmental stressors, including low salinity stress, nitrite stress, low pH stress, and high pH stress. The DEGs were enriched in biological pathways related to immune response, energy metabolism, oxidative stress response, hemostasis, and enzymatic activity of the hepatopancreas. In addition to the identification of DEGs related to each stressor, some DEGs were found to be expressed among all groups. The most important overlapping DEGs under multiple stressors were juvenile hormone esterase-like protein 2 (JHE-like), myosin light chain, C-type lectin 2, myosin-9-like, anti-lipopolysaccharide factor 1 (ALF-1), peroxisomal acyl-coenzyme An oxidase 1-like (ACX1), hepatic lectin-like, venom phosphodiesterase 2-like, hemolymph clottable protein-like (CP), cathepsin L, and Ras-like protein 2. The results of the present study provide additional information regarding the transcriptional response of the hepatopancreas to low salinity, nitrite, low pH, and high pH stress. Moreover, the discovery of several overlapping DEGs among different stressors provided a better understanding of the molecular function of the hepatopancreas.

Long read isoform sequencing reveals hidden transcriptional complexity between cattle subspecies

The Iso-Seq method of full-length cDNA sequencing is suitable to quantify differentially expressed genes (DEGs), transcripts (DETs) and transcript usage (DTU). However, the higher cost of Iso-Seq relative to RNA-seq has limited the comparison of both methods. Transcript abundance estimated by RNA-seq and deep Iso-Seq data for fetal liver from two cattle subspecies were compared to evaluate concordance. Inter-sample correlation of gene- and transcript-level abundance was higher within technology than between technologies. Identification of DEGs between the cattle subspecies depended on sequencing method with only 44 genes identified by both that included 6 novel genes annotated by Iso-Seq. There was a pronounced difference between Iso-Seq and RNA-seq results at transcript-level wherein Iso-Seq revealed several magnitudes more transcript abundance and usage differences between subspecies. Factors influencing DEG identification included size selection during Iso-Seq library preparation, average transcript abundance, multi-mapping of RNA-seq reads to the reference genome, and overlapping coordinates of genes. Some DEGs called by RNA-seq alone appear to be sequence duplication artifacts. Among the 44 DEGs identified by both technologies some play a role in immune system, thyroid function and cell growth. Iso-Seq revealed hidden transcriptional complexity in DEGs, DETs and DTU genes between cattle subspecies previously missed by RNA-seq.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

Hepatopancreas transcriptome alterations in red claw crayfish (Cherax quadricarinatus) under microcystin-LR (MC-LR) stress

Red claw crayfish (Cherax quadricarinatus) is a valuable aquatic species with fast growth, big size, delicious meat, and strong resistance to diseases worldwide. Microcystin-LR (MC-LR) is the most common and potently toxic and is known to infect aquatic animals. The present study was performed at the transcriptome level to investigate the mechanism of C. quadricarinatus in responses to MC-LR by RNA sequencing (RNA-seq) analysis. A total of 57,153,501, 51,955,723, and 56,316,927 clean reads were retrieved from the three libraries of C. quadricarinatus (0 h, 6 h, and 48 h). A total of 37,616 unigenes were identified and compared with NR, Swiss-Prot, Pfam, COG, GO, and KEGG databases. A total of 4980 and 4585 differentially expressed genes (DEGs) were obtained at 6 h and 48 h compared with control (0 h), respectively. Most of these DEGs were up-regulated in 6 h, but most were down-regulated in 48 h, indicating that gene expression levels were inhibited by MC-LR in 48 h. Based on the GO and KEGG pathways analyses of DEGs, we obtained various pathways related to apoptosis, immunity, and detoxification. A total of 234 DEGs were related to the immune system, including 19 immune metabolic pathways, such as the Toll-like receptor signaling pathway, Toll and Imd signaling pathway, interleukin-17 (IL-17) signaling pathway, etc. Besides, detoxification-associated and apoptosis-associated pathways were also identified, including drug metabolism-cytochrome P450 and glutathione metabolism, apoptosis and p53 signaling pathway, etc. Twelve DGEs involved in immune response and MC-LR detoxification were discovered and validated by quantitative real-time polymerase chain reaction (qRT-PCR). The results indicated that the C. quadricarinatus might counteract the toxicity of MC-LR at the transcriptomic level by increasing expression levels of immune- and detoxification-related genes. In summary, this study first reported the response mechanism of the C. quadricarinatus with injected MC-LR at the transcriptional level. The identification of differential expression genes and pathways can help us to understand further the response mechanism of C. quadricarinatus under MC-LR stress.

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis.

Gastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly. HP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model. In this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.

Identifying differentially expressed genes and miRNAs in Kawasaki disease by bioinformatics analysis

Kawasaki disease (KD) is an acute systemic immune vasculitis caused by infection, and its etiology and underlying mechanisms are not completely clear. This study aimed to identify differentially expressed genes (DEGs) with diagnostic and treatment potential for KD using bioinformatics analysis. In this study, three KD datasets (GSE68004, GSE73461, GSE18606) were downloaded from the Gene Expression Omnibus (GEO) database. Identification of DEGs between normal and KD whole blood was performed using the GEO2R online tool. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis of DEGs was undertaken with Metascape. Analysis and visualization of protein–protein interaction networks (PPI) were carried out with STRING and Cytoscape. Lastly, miRNA-genes regulatory networks were built by Cytoscape to predict the underlying microRNAs (miRNAs) associated with DEGs. Overall, 269 DEGs were identified, including 230 up-regulated and 39 down-regulated genes. The enrichment functions and pathways of DEGs involve regulation of defense response, inflammatory response, response to bacterium, and T cell differentiation. KEGG analysis indicates that the genes were significantly enriched in Neutrophil extracellular trap formation, TNF signaling pathway, Cytokine-cytokine receptor interaction, and Primary immunodeficiency. After combining the results of the protein–protein interaction (PPI) network and CytoHubba, 9 hub genes were selected, including TLR8, ITGAX, HCK, LILRB2, IL1B, FCGR2A, S100A12, SPI1, and CD8A. Based on the DEGs-miRNAs network construction, 3 miRNAs including mir-126-3p, mir-375 and mir-146a-5p were determined to be potential key miRNAs. To summarize, a total of 269 DEGs, 9 hub genes and 3 miRNAs were identified, which could be considered as KD biomarkers. However, further studies are needed to clarify the biological roles of these genes in KD.

BestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data.

Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it. We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project. The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

RNAseq, transcriptome analysis and identification of DEGs involved in development and ripening of Fragaria chiloensis fruit.

Fragaria chiloensis (Chilean strawberry) is a native species that produces fruit with an exotic pinkish color and a fruity aroma. It has a non-climacteric pattern of fruit ripening, and it is the mother of the commercial Fragaria x ananassa. The ripening of F. chiloensis fruit seems stimulated by ABA, and a complete set of genes participate in its softening, color, and aroma development. In addition, a set of transcription factors regulate the entire process, but few of them have been described. Over the last two decades, RNA-seq was used to identify genes at three fruit development/ripening stages, named C2 (unripe, large green) to C4 (full ripe), in whole fruit and fruit without achenes. A total of 204,754 contigs were assembled considering all samples, obtaining an N50 of 1.125 bp. Differentially expressed genes (DEGs) between two samples were identified, obtaining a total of 77,181 DEGs. Transcripts for genes involved in ABA biosynthesis present high and differential expression during the C2, C3, and C4 stages. Besides, contigs corresponding to ABA receptors, which interact with a regulatory network, are also differentially expressed. Genes associated with cell wall remodeling and those involved in flavonoid synthesis were also differentially expressed. An interaction network was built considering differentially expressed genes for the phenylpropanoid and flavonoid molecular pathways and having FcMYB1 as a transcription factor regulator. Identifying key genes could give an option to control the ripening of this non-climacteric fruit.

Identification of Differentially Expressed Genes and Prediction of Expression Regulation Networks in Dysfunctional Endothelium.

The detection of early coronary atherosclerosis (ECA) is still a challenge and the mechanism of endothelial dysfunction remains unclear. In the present study, we aimed to identify differentially expressed genes (DEGs) and the regulatory network of miRNAs as well as TFs in dysfunctional endothelium to elucidate the possible pathogenesis of ECA and find new potential markers. The GSE132651 data set of the GEO database was used for the bioinformatic analysis. Principal component analysis (PCA), the identification of DEGs, correlation analysis between significant DEGs, the prediction of regulatory networks of miRNA and transcription factors (TFs), the validation of the selected significant DEGs, and the receiver operating characteristic (ROC) curve analysis as well as area under the curve (AUC) values were performed. We identified ten genes with significantly upregulated signatures and thirteen genes with significantly downregulated signals. Following this, we found twenty-two miRNAs regulating two or more DEGs based on the miRNA–target gene regulatory network. TFs with targets ≥ 10 were E2F1, RBPJ, SSX3, MMS19, POU3F3, HOXB5, and KLF4. Finally, three significant DEGs (TOX, RasGRP3, TSPAN13) were selected to perform validation experiments. Our study identified TOX, RasGRP3, and TSPAN13 in dysfunctional endothelium and provided potential biomarkers as well as new insights into the possible molecular mechanisms of ECA.

Identification of Differential Expression Genes between Volume and Pressure Overloaded Hearts Based on Bioinformatics Analysis.

Volume overload (VO) and pressure overload (PO) are two common pathophysiological conditions associated with cardiac disease. VO, in particular, often occurs in a number of diseases, and no clinically meaningful molecular marker has yet been established. We intend to find the main differential gene expression using bioinformatics analysis. GSE97363 and GSE52796 are the two gene expression array datasets related with VO and PO, respectively. The LIMMA algorithm was used to identify differentially expressed genes (DEGs) of VO and PO. The DEGs were divided into three groups and subjected to functional enrichment analysis, which comprised GO analysis, KEGG analysis, and the protein–protein interaction (PPI) network. To validate the sequencing data, cardiomyocytes from AR and TAC mouse models were used to extract RNA for qRT-PCR. The three genes with random absolute values of LogFC and indicators of heart failure (natriuretic peptide B, NPPB) were detected: carboxylesterase 1D (CES1D), whirlin (WHRN), and WNK lysine deficient protein kinase 2 (WNK2). The DEGs in VO and PO were determined to be 2761 and 1093, respectively, in this study. Following the intersection, 305 genes were obtained, 255 of which expressed the opposing regulation and 50 of which expressed the same regulation. According to the GO and pathway enrichment studies, DEGs with opposing regulation are mostly common in fatty acid degradation, propanoate metabolism, and other signaling pathways. Finally, we used Cytoscape’s three techniques to identify six hub genes by intersecting 255 with the opposite expression and constructing a PPI network. Peroxisome proliferator-activated receptor (PPARα), acyl-CoA dehydrogenase medium chain (ACADM), patatin-like phospholipase domain containing 2 (PNPLA2), isocitrate dehydrogenase 3 (IDH3), heat shock protein family D member 1 (HSPD1), and dihydrolipoamide S-acetyltransferase (DLAT) were identified as six potential genes. Furthermore, we predict that the hub genes PPARα, ACADM, and PNPLA2 regulate VO myocardial changes via fatty acid metabolism and acyl-Coa dehydrogenase activity, and that these genes could be employed as basic biomarkers for VO diagnosis and treatment.