Tool For Analysis Of Gene Expression Research Articles

Focused cancer pathway analysis revealed unique therapeutic targets in retinoblastoma.

Retinoblastoma (RB) is a pediatric cancer of the eye that occurs in 1/15000 live births worldwide. Albeit RB is initiated by the inactivation of RB1 gene, the disease progression relies largely on transcriptional alterations. Therefore, evaluating gene expression is vital to unveil the therapeutic targets in RB management. In this study, we employed an RT2 Profiler™ PCR array for a focused analysis of 84 cancer-specific genes in RB. An interaction network was built with gene expression data to identify the dysregulated pathways in RB. The key transcript alterations identified in 13 tumors by RT2 Profiler™ PCR array was further validated in 15 tumors by independent RT-qPCR. Out of 84 cancer-specific genes, 68 were dysregulated in RB tumors. Among the 68 genes, 23 were chosen for further analysis based on statistical significance and abundance across multiple tumors. Pathway analysis of altered genes showed the frequent perturbations of cell cycle, angiogenesis and apoptotic pathways in RB. Notably, upregulation of MCM2, MKI67, PGF, WEE1, CDC20 and downregulation of COX5A were found in all the tumors. Western blot confirmed the dysregulation of identified targets at protein levels as well. These alterations were more prominent in invasive RB, correlating with the disease pathogenesis. Our molecular analysis thus identified the potential therapeutic targets for improving retinoblastoma treatment. We also suggest that PCR array can be used as a tool for rapid and cost-effective gene expression analysis.

Time- and Region-Specific Selection of Reference Genes in the Rat Brain in the Lithium-Pilocarpine Model of Acquired Temporal Lobe Epilepsy.

Reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) is a commonly used tool for gene expression analysis. The selection of stably expressed reference genes is required for accurate normalization. The aim of this study was to identify the optimal reference genes for RT-qPCR normalization in various brain regions of rats at different stages of the lithium-pilocarpine model of acquired epilepsy. We tested the expression stability of nine housekeeping genes commonly used as reference genes in brain research: Actb, Gapdh, B2m, Rpl13a, Sdha, Ppia, Hprt1, Pgk1, and Ywhaz. Based on four standard algorithms (geNorm, NormFinder, BestKeeper, and comparative delta-Ct), we found that after pilocarpine-induced status epilepticus, the stability of the tested reference genes varied significantly between brain regions and depended on time after epileptogenesis induction (3 and 7 days in the latent phase, and 2 months in the chronic phase of the model). Pgk1 and Ywhaz were the most stable, while Actb, Sdha, and B2m demonstrated the lowest stability in the analyzed brain areas. We revealed time- and region-specific changes in the mRNA expression of the housekeeping genes B2m, Actb, Sdha, Rpl13a, Gapdh, Hprt1, and Sdha. These changes were more pronounced in the hippocampal region during the latent phase of the model and are thought to be related to epileptogenesis. Thus, RT-qPCR analysis of mRNA expression in acquired epilepsy models requires careful selection of reference genes depending on the brain region and time of analysis. For the time course study of epileptogenesis in the rat lithium-pilocarpine model, we recommend the use of the Pgk1 and Ywhaz genes.

Pseudomonas aeruginosa transcriptome analysis of metal restriction in ex vivo cystic fibrosis sputum.

Chronic Pseudomonas aeruginosa lung infections are a feature of cystic fibrosis (CF) that many patients experience even with the advent of highly effective modulator therapies. Identifying factors that impact P. aeruginosa in the CF lung could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa studies using laboratory models or RNA isolated from sputum, we analyzed transcripts of strain PAO1 after incubation in sputum from different CF donors prior to RNA extraction. We compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in synthetic cystic fibrosis sputum medium to determine key genes, which are among the most differentially expressed or most highly expressed. Using the key genes, gene sets with correlated expression were determined using the gene expression analysis tool eADAGE. Gene sets were used to analyze the activity of specific pathways in P. aeruginosa grown in sputum from different individuals. Gene sets that we found to be more active in sputum showed similar activation in published data that included P. aeruginosa RNA isolated from sputum relative to corresponding in vitro reference cultures. In the ex vivo samples, P. aeruginosa had increased levels of genes related to zinc and iron acquisition which were suppressed by metal amendment of sputum. We also found a significant correlation between expression of the H1-type VI secretion system and CFTR corrector use by the sputum donor. An ex vivo sputum model or synthetic sputum medium formulation that imposes metal restriction may enhance future CF-related studies.IMPORTANCEIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatic analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to a synthetic cystic fibrosis sputum medium. Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which may be inadequately represented in some models. Future studies of P. aeruginosa transcript abundance in CF may benefit from the use of an expectorated sputum model or media supplemented with factors that induce metal restriction.

Introducing the f0% method: a reliable and accurate approach for qPCR analysis

BackgroundqPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%).ResultsThe performance of the new f0% method was compared against the CT method along with another two outstanding methods—LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds.ConclusionsWe recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Prediction of target genes in community-acquired pneumonia based on the bioinformatics method.

To screen the related genes of community-acquired pneumonia (CAP) by bioinformatics technology, and to analyze the clinical value of key genes. Gene chip data sets containing CAP patients and normal controls were screened from the Gene Expression Omnibus (GEO) database. The downregulated differentially expressed genes (DEGs) were screened using a gene expression analysis tool (GEO2R). Simultaneously, gene set enrichment analysis (GSEA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and core genes related to CAP. The candidate genes were then intersected with the genes reported in Online Mendelian Inheritance in Man (OMIM), and the clinical value of these candidate genes was examined based on a literature search. Finally, the clinical data of the CAP patients were retrospectively analyzed. Detect the type of pathogenic bacteria in bronchial-alveolar lavage fluid (BALF) using metagenomics next-generation sequencing (mNGS) high throughput sequencing technology, and detect the expression of key genes through liquid based cell immunohistochemistry to analyze the correlation between pathogenic bacteria and key genes. Through the intersection of Venn diagrams, 175 co-expressed downregulated DEGs related to CAP were identified. A total of 4 candidate genes, including ICOS, IL7R, ITK, and ZAP70, were obtained by constructing the protein mutual aid network and conducting a module analysis of the common differentially expressed genes. The core genes in the GSEA enrichment pathways were intersected with the CAP-related genes reported in the relevant literature retrieved from the OMIM database. In the Venn diagram, two genes that coexist with OMIM include IL7R and PIK3R1. After considering our findings and the relevant literature, we determined that the key gene related to the occurrence and development of CAP was IL7R. The mNGS detected 13 kinds of bacteria, 4 kinds of fungi, and 2 kinds of viruses. Based on immunohistochemical results, it was found that there were relatively more bacteria detected in the IL7R high expression group. The identification of the key gene IL7R and the related signaling pathways extend understanding of the pathogenesis of CAP and provide a theoretical basis for clinical targeted therapy research.

VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data.

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome's web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data.

LCM-Seq for Retinal Cell Layer-Specific Responses During Optic Nerve Regeneration.

LCM-seq is a powerful tool for gene expression analysis from individual or groups of cells that can be spatially isolated. Within the visual system, retinal ganglion cells (RGCs), the cells that connect the eye to the brain through the optic nerve, reside in the retinal ganglion cell layer of the retina. This well-defined location provides a unique opportunity to harvest RNA by laser capture microdissection (LCM) from a highly enriched cell population. Using this method, it is possible to explore transcriptome-wide changes in gene expression following optic nerve injury. In the zebrafish model, this method can be used to identify molecular events driving successful optic nerve regeneration in contrast to mammals that fail to regenerate axons in the central nervous system. Here we provide a method for LCM from the different retinal layers of zebrafish following optic nerve injury and during the process of optic nerve regeneration. Purified RNA from this protocol is sufficient for RNA-seq or other downstream analysis.

Bioinformatics Analysis of Core Genes and Key Pathways in Myelodysplastic Syndrome

To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG. The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1. A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS. Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.

HTG transcriptome panel (HTP): An accurate and robust tool for transcriptome-wide gene expression profiling.

e15063 Background: Analysis of transcriptome-wide gene expression has the potential to provide insights into biological pathways and molecular mechanisms that regulate disease progression and treatment response. RNA-Seq is considered the gold standard for transcriptome analysis; however, it may not perform well with older, archival formalin-fixed paraffin embedded (FFPE) tissues or low-quality/low-quantity RNA. To help researchers overcome these challenges, HTG Molecular Diagnostics developed a Research Use Only, extraction-free, targeted human transcriptome panel with an automated, proprietary web-based data analysis pipeline that provides fast, accurate, and repeatable quantitative gene expression data for 19,398 protein-coding mRNA targets from lysed FFPE tissue samples or extracted RNA (eRNA). Methods: Panel precision was evaluated by Lin’s Concordance Correlation Coefficient (Lc) using FFPE tissue lysates and eRNA from FFPE and fresh frozen tissues, including FFPE blocks that were older than ten years. The accuracy of the HTP to perform differential expression analysis was assessed by comparing it to RNA-Seq using FFPE samples from different cancer indications. The agreement of the log fold changes between the two platforms was measured by Pearson Correlation Coefficient (Pc). Additionally, a cohort of 83 breast cancer FFPE samples, that had also been evaluated for estrogen (ER) and progesterone (PR) receptor expression by immunohistochemistry (IHC), were used to determine the correlation between HTP gene expression and orthogonal IHC data. Results: The precision of the panel measured by Lc, ranged from 0.90-0.94 across multiple operators, instruments, lots, and processing days, demonstrating that the HTP is a precise and robust panel. In addition, HTP is a competitive alternate to RNA-seq (estimated log-fold changes, Pc = 0.83 between the platforms). Archival samples demonstrated high repeatability (Pc &gt; 0.9) with sample pass rates of 100% for HTP, which further confirms panel’s robust performance. Furthermore, the HTP gene expression results correlated with the IHC markers for ER (Pc = 0.92) and PR (Pc = 0.88) receptor status in a cohort of 83 breast cancer samples, which highlights the potential of the panel to replace IHC staining while providing expression data on thousands of additional genes. Conclusions: The results demonstrate the HTP is a powerful tool for comprehensive gene expression analysis that addresses several key limitations of RNA-Seq. The HTP is precise, accurate, user-friendly (avoids extraction-bias, fewer steps, automated data analysis), and efficient (quicker, requires lower amounts of sample, works well with samples of low quality and archival samples). Collectively, the data demonstrate HTP has the potential to be used for biomarker discovery and the development of clinical solutions.

Integrative Gene Expression and Metabolic Analysis Tool IgemRNA.

Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

Selection and validation of reference genes for RT-qPCR analysis in the pericarp of Litchi chinensis

Real-time reverse transcription quantitative PCR (RT-qPCR) is an important tool for gene expression analysis. Suitable reference genes are the basis of accurate and reliable RT-qPCR results. Litchi (Litchi chinensis Sonn.) is a commercially important tropical and subtropical fruit, but rapid pericarp browning is a substantial negative impact on its commercial use. Reference gene validation could help in the screening for genes involved in the browning mechanism. We assessed 15 new candidate reference genes from litchi transcriptome to determine stable reference genes for RT-qPCR analysis of pericarps from different cultivars, with differing postharvest storage, and under pathogenic stress. Ct values, geNorm, Normfinder, and RefFinder algorithms, were used to identify genes with the most stable transcription. GAGA-25 was the gene with the most stable transcription for comparing different varieties of the fresh pericarp. HDAC9 was the gene with the most stable transcription for postharvest pericarp. STAM was the gene with the most stable transcription for inoculated pericarp. Of the candidate reference genes, GAGA-25 was the most stable reference gene across the complete sample set. This study evaluated reference gene stability for RT-qPCR in litchi pericarp. This work provides a foundation for using qPCR to study gene function and molecular mechanism studies of litchi pericarp browning.

Inhibition of epidermal growth factor receptor suppresses parathyroid hormone-related protein expression in tumours and ameliorates cancer-associated cachexia.

BackgroundLung cancer is the primary cause of cancer deaths worldwide. Activation of epidermal growth factor receptor (EGFR) leads to lung cancer progression and poor prognosis while involuntary weight loss remains a major problem. Tumour‐derived parathyroid hormone‐related protein (PTHrP) emerged as a potential mediator of cachexia. Here, we investigated the modulatory role of EGFR signalling in PTHrP (encoded by Pthlh) gene expression and the impact of this relationship on cancer cachexia.MethodsGlobal gene expression profiles of Lewis lung carcinoma (LLC) cells were analysed. Pthlh mRNA levels were measured by qRT‐PCR in LLC cells treated with EGFR ligands and tyrosine kinase inhibitors (TKIs). LLC tumour‐bearing mice received EGFR TKI erlotinib for 7 days via intraperitoneal injection or oral gavage. Tumour Pthlh mRNA, weight of fat/muscle tissue, and grip strength were assessed. RNA‐seq data from The Cancer Genome Atlas and gene expression analysis tools were used to characterize expression profiles of PTHLH and EGFR along with correlation analysis of PTHLH with EGFR and transforming growth factor alpha (TGFA) in human lung cancer and head and neck squamous carcinoma (HNSC). Survival of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) patients with EGFR gene alterations was analysed in regard to PTHLH expression.ResultsExpression of EGFR ligands, EGFR itself, and PTHrP co‐clusters in LLC cells. Activation of EGFR signalling with its ligands significantly increases (3.8‐fold, P < 0.0005) while EGFR TKIs significantly decrease (90%, P < 0.0005) Pthlh mRNA levels in LLC cells. Pthlh mRNA drops 65–75% (P < 0.0005) in tumours upon treatment of LLC tumour‐bearing mice with erlotinib while their muscle mass and grip strength increase (9.2% P < 0.05, 23% P < 0.005, respectively) compared with tumour‐bearing control mice. PTHLH is overexpressed in tumours of LUSC (45.8‐fold, P < 0.05) and HNSC (17.5‐fold, P < 0.05) compared with normal tissue. PTHLH expression correlates with EGFR and its ligand TGFA in both cancers (LUSC: n = 745, R = 0.32, P < 0.0001 and R = 0.51, P < 0.0001; HNSC: n = 545, R = 0.34, P < 0.001 and R = 0.50, P < 0.001, respectively). High PTHLH mRNA associates with poor overall survival in LUAD patients with activating EGFR mutations (n = 40, log‐rank test, P = 0.0451).ConclusionsEpidermal growth factor receptor signalling regulates expression of cachexia mediator PTHrP. EGFR inhibition reduces PTHrP expression in LLC tumours and ameliorates cachexia in LLC tumour‐bearing mice.

Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

QTeller: a tool for comparative multi-genomic gene expression analysis.

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary data are available at Bioinformatics online.

Amplification-free gene expression analysis of formalin-fixed paraffin-embedded samples using scanning single-molecule counting

In this paper, we present the applications of our newly developed, highly sensitive fluorescent detection method referred to as scanning single-molecule counting (SSMC). We found that the target RNA added to the total RNA was detected with high sensitivity at 384 aM by combining a magnetic bead-based assay and SSMC (MB-BA + SSMC). Gene expression analysis without reverse transcription or amplification confirmed that the pattern of gene expression was identical to that of real-time polymerase chain reaction (PCR). MB-BA + SSMC was also applied to formalin-fixed paraffin-embedded (FFPE) samples. RNA fragmentation and crosslinking owing to FFPE processing slightly affected gene expression. Conversely, FFPE samples showed an increase in gene Ct values and a decrease in the number of detectable genes when analyzed using real-time PCR. Overall, our results suggested that SSMC is a powerful tool for target RNA detection and amplification-free gene expression analysis.

LncRNA LINC01116 sponges miR-93-5p to promote cell invasion and migration in small cell lung cancer

BackgroundLINC01116 is a recently identified oncogenic lncRNA in glioma. Differential expression analysis using the public gene expression analysis tool GEPIA revealed the upregulation of LINC01116 in lung cancer. We studied the functions of LINC01116 in small cell lung cancer (SCLC).MethodsThe expression of LINC01116 in several types of cancer tissue and the paired non-tumor tissues was evaluated by GEPIA. The effects of the overexpression of LINC01116 and miR-93-5p on the expression of STAT3 were evaluated. The effects of the overexpression of LINC01116, miR-93-5p and STAT3 on SHP-77 cell behaviors were evaluated by Transwell assays.ResultsLINC01116 was highly expressed in SCLC and predicted poor survival. In SCLC tissues, the expression of LINC01116 was positively correlated with STAT3. Bioinformatics analysis revealed that miR-93-5p may target LINC01116. Overexpression of LINC01116 increased STAT3 but did not affect the expression of miR-93-5p. Transwell assay showed that LINC01116 and STAT3 increased cell invasion and migration rates. MiR-93-5p played an suppressed cell behaviors and suppressed the role of LINC01116.ConclusionTherefore, LINC01116 might upregulate STA3 by sponging miR-93-5p, thereby promoting cell invasion and migration in SCLC.

Preclinical Experimental Applications of miRNA Loaded BMSC Extracellular Vesicles.

Bone marrow mesenchymal stem cells have been investigated for many years, especially for tissue regeneration, and have inherent limitations. One of the rapidly developing fields in the scientific world in recent years is extracellular vesicles. Especially, bone marrow mesenchymal stem cell originated extracellular vesicles are known to have positive contributions in tissue regeneration, and these extracellular vesicles have also been used as gene transfer systems for cellular therapy. Through gene expression analysis and bioinformatics tools, it is possible to determine which genes have changed in the targeted tissue or cell and which miRNAs that can correct this gene expression disorder. This approach connecting the stem cell, extracellular vesicles, epigenetics regulation and bioinformatics fields is one of the promising areas for the treatment of diseases in the future. With this review, it is aimed to present the studies carried out for the use of bone marrow stem cell-derived extracellular vesicles loaded with targeted miRNAs in different in vivo and in vitro human disease models and to discuss recent developments in this field.

An integrated approach in gene-expression landscape profiling to identify housekeeping and tissue-specific genes in cattle

Context High-throughput transcriptome sequencing (RNA-Seq) has been widely applied in cattle studies. Public databases such as the National Center for Biotechnology Information (NCBI) contain large collections of gene expression data from various cattle tissues that can be used in gene expression analysis research Aims This study was conducted to investigate patterns of transcriptome variation across tissues of cattle through large-scale identification of housekeeping genes (i.e. those crucial to maintaining basic cellular activity) and tissue-specific genes in cattle tissues. Methods Using data available in the NCBI Sequence Read Archive database, we analysed 1377 transcriptome data sequences from 60 bovine tissue types, identified tissue-specific and housekeeping genes, and set up a web-based bovine gene expression analysis tool. Key results We found 101 genes widely expressed in almost all tissue and screened out five housekeeping genes: RPL35A, eIF4A2, GAPDH, IPO5 and PAK2. Focusing on 12 major organs, we found 861 genes specifically expressing in these tissues. Furthermore, 187 significantly differentially expressed genes were found among six types of muscle tissues. All expression data were made available at our new website http://cattleExp.org, which can be freely accessed for future gene expression analyses. Conclusions The housekeeping genes and tissue-specific genes identified will provide more information for researchers studying gene expression in cattle. Implications The web-based cattle gene expression analysis tool will make it easy for researchers to access large public datasets. Users can easily access all publicly available RNA data and upload their own RNA-Seq data.

Genetic response to low‑intensity ultrasound onmouse ST2 bone marrow stromal cells.

Although low-intensity ultrasound (LIUS) is a clinically established procedure, the early cellular effect of LIUS on a genetic level has not yet been studied. The current study investigated the early response genes elicited by LIUS in bone marrow stromal cells (BMSCs) using global-scale microarrays and computational gene expression analysis tools. Mouse ST2 BMSCs were treated with LIUS [ISATA, 25 mW/cm2 for 20 min with a frequency of 1.11 MHz in a pulsed-wave mode (0.2-s burst sine waves repeated at 1 kHz)], then cultured for 0.5, 1 and 3 h at 37°C. The time course of changes in gene expression was evaluated using GeneChip® high-density oligonucleotide microarrays and Ingenuity® Pathway Analysis tools. The results were verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A single exposure of LIUS did not affect cell morphology, cell growth or alkaline phosphatase activity. However, 61 upregulated and 103 downregulated genes were identified from 0.5 to 3 h after LIUS treatment. Two significant gene networks, labeled E and H, were identified from the upregulated genes, while a third network, labeled T, was identified from the downregulated genes. Gene network E or H containing the immediate-early genes FBJ osteosarcoma oncogene and early growth response 1 or the heat shock proteins heat shock protein 1a/b was associated mainly with the biological functions of bone physiology and protein folding or apoptosis, respectively. Gene network T containing transcription factors fos-like antigen 1 and serum response factor was also associated with the biological functions of the gene expression. RT-qPCR indicated that the expression of several genes in the gene networks E and H were elevated in LIUS-treated cells. LIUS was demonstrated to induce gene expression after short application in mouse ST2 BMSCs. The results of the present study provide a basis for the elucidation of the detailed molecular mechanisms underlying the cellular effects of LIUS.