Potential Differentially Expressed Genes Research Articles

A meta-analysis of differentially expressed and regulatory genes with their functional enrichment analysis for brain transcriptome data in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interactions and repetitive behavioral patterns. It is a significant problem emerging worldwide, as one in 100 children is affected by this disorder globally. In this study, a meta-analysis was performed for the identification of differentially expressed genes (DEGs) along with the expression analysis of regulatory genes. Functional enrichment analysis was an integral part of current findings to notify the significant pathways of this complex disorder. The study was conducted with two RNA-Seq datasets, viz., GSE64018 and GSE62098, for ASD patients and control samples from the GEO database. The identification of up-regulatory and down-regulatory genes was performed by the interaction analysis of transcription factors (TF) and DEGs. As an outcome of the meta-analysis, 2543 DEGs were identified as common across both of the datasets in which 1402 DEGs exhibited upregulation and 1130 genes have shown downregulation. In network analysis, upregulatory genes have shown strong interaction while downregulatory genes exhibit weak or null interaction. Further, in the enrichment analysis of screened upregulatory DEGs, three major significant pathways were identified namely the ATP synthesis pathway, FAS signaling pathway, and the Huntington’s disease pathway. The common expression of CYC 1 gene in all the identified pathways has indicated that it is an important key regulator gene for the majorly associated pathways. The study concludes that all the potential DEGs were found to show their related high expression in neurobiological regulations specifically with ASD. Communicated by Ramaswamy S. Sarma

Identifying Antidepressant Effects of Brain-Derived Neurotrophic Factor and IDO1 in the Mouse Model Based on RNA-Seq Data.

Deletion of brain-derived neurotrophic factor (BDNF) and upregulation of indoleamine 2,3-dioxygenase 1 (IDO1) are associated with depression severity in animals. The neurotransmitter hypothesis of depression at the transcriptomic level can be tested using BDNF- and IDO1-knockout mouse models and RNA-seq. In this study, BDNF+/−, IDO1−/−, and chronic ultra-mild stress (CUMS)-induced depression mouse models and controls were developed, and the differentially expressed genes were analyzed. Furthermore, the ceRNA package was used to search the lncRNA2Target database for potential lncRNAs. Finally, a protein–protein interaction (PPI) network was constructed using STRINGdb. By comparing the control and CUMS model groups, it was found that pathway enrichment analysis and ceRNA network analysis revealed that most differentially expressed genes (DEGs) were associated with protection of vulnerable neuronal circuits. In addition, we found the enriched pathways were associated with nervous system development and synapse organization when comparing the control and BDNF+/−model groups. When replicating the neurotransmitter disruption features of clinical patients, such comparisons revealed the considerable differences between CUMS and knockdown BDNF models, and the BDNF+/−model may be superior to the classic CUMS model. The data obtained in the present study implicated the potential DEGs and their enriched pathway in three mouse models related to depression and the regulation of the ceRNA network-mediated gene in the progression of depression. Together, our findings may be crucial for uncovering the mechanisms underlying the neurotransmitter hypothesis of depression in animals.

Comparative transcriptomic analysis reveals the regulatory mechanism of the gibberellic acid pathway of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn.) dwarf mutants

BackgroundTartary buckwheat is an important minor crop species with high nutritional and medicinal value and is widely planted worldwide. Cultivated Tartary buckwheat plants are tall and have hollow stems that lodge easily, which severely affects their yield and hinders the development of the Tartary buckwheat industry.MethodsHeifeng No. 1 seeds were treated with ethylmethanesulfonate (EMS) to generate a mutant library. The dwarf mutant ftdm was selected from the mutagenized population, and the agronomic characteristics giving rise to the dwarf phenotype were evaluated. Ultra-fast liquid chromatography-electrospray ionization tandem mass spectrometry (UFLC-ESI–MS/MS) was performed to determine the factors underlying the different phenotypes between the wild-type (WT) and ftdm plants. In addition, RNA sequencing (RNA-seq) was performed via the HiSeq 2000 platform, and the resulting transcriptomic data were analysed to identify differentially expressed genes (DEGs). Single-nucleotide polymorphism (SNP) variant analysis revealed possible sites associated with dwarfism. The expression levels of the potential DEGs between the WT and ftdm mutant were then measured via qRT-PCR and fragments per kilobase of transcript per million mapped reads (FPKM).ResultThe plant height (PH) of the ftdm mutant decreased to 42% of that of the WT, and compared with the WT, the mutant and had a higher breaking force (BF) and lower lodging index (LI). Lower GA4 and GA7 contents and higher contents of jasmonic acid (JA), salicylic acid (SA) and brassinolactone (BR) were detected in the stems of the ftdm mutant compared with the WT. Exogenous application of GAs could not revert the dwarfism of the ftdm mutant. On the basis of the transcriptomic analysis, 146 homozygous SNP loci were identified. In total, 12 DEGs with nonsynonymous mutations were ultimately identified, which were considered potential candidate genes related to the dwarf trait. When the sequences of eight genes whose expression was downregulated and four genes whose expression was upregulated were compared, SKIP14, an F-box protein whose sequence is 85% homologous to that of SLY1 in Arabidopsis, presented an amino acid change (from Ser to Asn) and was expressed at a lower level in the stems of the ftdm mutant compared with the WT. Hence, we speculated that this amino acid change in SKIP14 resulted in a disruption in GA signal transduction, indirectly decreasing the GA content and downregulating the expression of genes involved in GA biosynthesis or the GA response. Further studies are needed to determine the molecular basis underlying the dwarf phenotype of the ftdm mutant.ConclusionWe report a Tartary buckwheat EMS dwarf mutant, ftdm, suitable for high-density planting and commercial farming. A significant decrease in GA4 and GA7 levels was detected in the ftdm mutant, and 12 DEGs expressed in the stems of the ftdm mutant were selected as candidates of the dwarfing gene. One nonsynonymous mutation was detected in the SKIP14 gene in the ftdm mutant, and this gene had a lower transcript level compared with that in the WT.

A practical method to screen and identify functioning biomarkers in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a rare malignancy, with the unique geographical and ethnically characteristics of distribution. Gene chip and bioinformatics have been employed to reveal regulatory mechanisms in current functional genomics. However, a practical solution addressing the unresolved aspects of microarray data processing and analysis have been long pursuit. This study developed a new method to improve the accuracy of identifying key biomarkers, namely Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Three mRNA expression profile of NPC were selected to feed UGM. Differentially expressed genes (DEGs) were identified with UGM and hub genes were derived from them to explore their association with NPC using functional enrichment and pathway analysis. 47 potential DEGs were identified by UGM from the 3 selected datasets, and affluent in cysteine-type endopeptidase inhibitor activity, cilium movement, extracellular exosome etc. also participate in ECM-receptor interaction, chemical carcinogenesis, TNF signaling pathway, small cell lung cancer and mismatch repair pathway. Down-regulation of CAPS and WFDC2 can prolongation of the overall survival periods in the patients. ARMC4, SERPINB3, MUC4 etc. have a close relationship with NPC. The UGM is a practical method to identify NPC-associated genes and biomarkers.

Predicting the Lung Squamous Cell Carcinoma Diagnosis and Prognosis Markers by Unique DNA Methylation and Gene Expression Profiles.

The early diagnosis of lung squamous cell carcinoma (LUSC) is difficult, causing an unsatisfactory prognosis. Therefore, the 5-year survival rate of LUSC is poor. This study aimed at screening the potential diagnostic and prognostic markers for LUSC. The data of LUSC gene expression profiles and DNA methylation were obtained from The Cancer Genome Atlas (TCGA) database; the differentially expressed genes (DEGs) and the differentially methylated genes (DMGs) were screened out by an independent t-test and Benjamini/Hochberg methods. Further, the classifiers of the gene expression and DNA methylation markers in LUSC were constructed. After that, diagnostic and prognostic markers in LUSC were analyzed by the protein-protein interaction (PPI) network. The DEGs and the DMGs from TCGA database of LUSC were screened out. After strict filtration, we identified three potential DMGs (POU domain, class 4, transcription factor 2 [POU4F2], EN1, single-minded homolog 1 [SIM1]) for early diagnosis and seven potential DEGs (G-protein coupled receptor 78 [GPR78], PCDHA5, myosin binding protein H [MYBPH], RTL3, KIAA0408, HSD3B2, PCDHA12) for prognosis of LUSC. The tumor-normal tissue classification model and prognosis model were validated in two independent datasets. In addition, the PPI network was constructed, including three DMGs and the five DEGs (GPR78, MYBPH, KIAA0408, HSD3B2, PCDHA12) of the seven DEGs. The potential DMGs (POU4F2, EN1, SIM1) and DEGs (GPR78, MYBPH, KIAA0408, HSD3B2, PCDHA12) for the diagnosis and prognosis of LUSC identified in this article are expected to be further applied in clinical practice of the treatment of LUSC.

Overexpression of microRNA-190 inhibits migration, invasion, epithelial-mesenchymal transition, and angiogenesis through suppression of protein kinase B-extracellular signal-regulated kinase signaling pathway via binding to stanniocalicin 2 in breast cancer.

Breast cancer (BC) is known as the most deadly cancer among females, worldwide. Despite the research advances in this regard, effective diagnosis and treatment still have a long way to go. In this study, our stance was to investigate the regulatory mechanism of miR-190 on epithelial-mesenchymal transition (EMT) and angiogenesis via mediation of protein kinase B (AKT)-extracellular signal-regulated kinase (ERK) signaling pathway by targeting stanniocalicin 2 (STC2) in BC. The BC gene chip was retrieved with differentially expressed genes (DEGs) obtained. MDA-MB-231 and T47D cell lines were selected and separately introduced with miR-190 inhibitors, activators, and small interfering RNAs with the intent of exploring the regulatory functions that miR-190 has shown while governing STC2 in BC. The regulatory effect of miR-190 on cell proliferation, migration, invasion, and angiogenesis was evaluated, followed by determination of AKT-ERK signaling pathway-related factors, EMT-related factors, and angiogenesis-related factors. The xenograft tumor of nude mice was also implemented for determining the change of tumor after transfection. The GSE26910 gene chip was obtained with STC2 being selected as the potential DEG. STC2 was the target gene of miR-190. The results showed that cells introduced with the miR-190 activators along with small interfering RNA-STC2 inhibited proliferation, invasion, migration, angiogenesis, as well as EMT. Moreover, the in vivo experiment also went on to confirm that the tumor volume had significantly increased in the nude mice along with an elevated expression of miR-190. Collectively, the findings suggested that overexpression of miR-190 inhibited EMT and angiogenesis by inactivating AKT-ERK signaling pathway via STC2 in BC.

A normalization strategy for comparing tag count data

BackgroundHigh-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.ResultsWe describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.ConclusionOur results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.