Identification Of Differential Expression Research Articles

Elucidation of the mechanisms and molecular targets of KeChuanLiuWei-Mixture for treatment of severe asthma based on network pharmacology.

KeChuanLiuWei-Mixture(KCLW) is widely used as a Chinese medicine prescription to treat severe asthma. However, the underlying therapeutic mechanism of KCLW remains unclear. In this study, a network pharmacology method was used to identify the chemical constituents of KCLW by the TCMSP database and ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry. Differential expression identification, protein-protein interaction (PPI) network and functional enrichment analysis were used to screen key targets of KCLW for severe asthma. Our results confirmed that quercetin, luteolin, kaempferol, and wogonin are the most critical active ingredients in KCLW. Moreover, the 16 relevant severe asthma-related targets of KCLW were obtained by overlapping the PPI networks of the KCLW putative targets and severe asthma-related genes, among which the most important targets were IL-6, NOS2, VEGFA, CXCL2, and PLAT. Functionally, the 16-targets and their interacting differentially expressed genes were primarily related to biological functions and pathways related to immunity and inflammation, such as inflammatory response, T cell differentiation, Nrf2/HO-1 signaling pathway, TGF-β/Smad signaling pathway, and NF-κB signaling pathway. KCLW inhibited inflammation in PDGF-BB-induced airway smooth muscle cells. In summary, this study demonstrates the active substance and potential therapeutic mechanism of KCLW in severe asthma, and offers a clinical direction for KCLW against severe asthma.

Abstract 479: mRNA in situ expression of candidate genes for progression risk tissue biomarkers, in premalignant cervical lesions

Abstract Background Comparing the in situ differences in mRNA expression of candidate genes between normal cervical tissue and premalignant lesions, could contribute to detect progression risk biomarkers. Use of RNAscope chromogenic in situ hybridization (CISH) that amplifies specific mRNA signals and detects molecules as points that can be quantified, could help the detection of these biomarkers. Methods A tissue microarray was constructed from cervical samples (conization and histerectomy). Representative areas of normal cervix, low-grade (LSIL), and high-grade squamous intraepithelial lesions (HSIL) were chosen. RNAscope 2.5HD Red HPV-HR18 assay that detect E6/E7 mRNA for eighteen high-risk (HR) HPV genotypes, and RNAscope 2.5HD Red assays to detect mRNA from: KRT16, TMEM45A and RBP1, were performed. The stainings were scored by two pathologist from 0 to 4 in a semi-quantitative analysis based on the average number of dots per cell; mean score values were compared using GraphPad Prism 7.0. A p<0.05 was considered statistically significant. Results A total of 390 cervical tissues cores from 169 patients with mean age 36.08 years (SD ±10.48) were included. Mean mRNA expression of HR-HPV E6/E7, TMEM45A and RBP1 were significantly higher in HSIL compared to LSIL and normal cervix squamous epithelium. KRT16 mRNA expression scores in normal cervix squamous epithelium, LSIL, and HSIL, were not significantly different (Table 1). Conclusions CISH allowed to detect E6/E7 mRNA molecules for eighteen HR-HPV genotypes in routinely processed tissues, and to evaluate the expression of the described candidate genes in their context without destroying the tissue morphology. Identification of differential expression between normal cervical tissue and HSIL supports the possible role of TMEM45A and RBP1 in cervical cancer progression and its potential use as progression risk tissue biomarkers. Table 1.Biomarkers candidate genes expression profiles in normal cervix, LSIL and HSILCISH scoresMean (±SD)95% CINormal cervixLSILHSILp valueHR-HPV E6/E7 mRNAn=640.4098(±0.7611)0.2149-0.6048n=640.4098 (±0.7611)0.2149-0.6048n=1381.294 (±1.189)1.078-1.51<0.0001*TMEM45A mRNAn=761.105 (± 1.322)0.8031-1.407n=691.232 (±1.477)0.8771-1.587n=2381.514(± 1.491)1.264-1.7650.046*RBP1 mRNAn=660.4242(±0.7658)0.236-0.6125n=740.5135 (±0.9826)0.2859-1.159n=1301.3 (±1.186)1.094-1.506<0.0001*KRT16 mRNAn=840.9524(±1.15)0.7028-1.202n=770.8831 (±1.214)0.6076-1.159n=1571.115 (±1.41)0.8924-1.3370.573* Citation Format: Ines Benedetti, Felipe Ruiz, Reinhard Rodríguez. mRNA in situ expression of candidate genes for progression risk tissue biomarkers, in premalignant cervical lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 479.

HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

We present Hierarchical Bayesian Analysis of Differential Expression and ALternative Splicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing and allows genes to be characterized as having differential gene expression, differential alternative splicing, both, or neither. HBA-DEALS analysis of GTEx data demonstrated sets of genes that show predominant DGE or DAST across multiple tissue types. These sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.

Marker-Assisted Molecular Profiling, Deletion Mutant Analysis, and RNA-Seq Reveal a Disease Resistance Cluster Associated with Uromyces appendiculatus Infection in Common Bean Phaseolus vulgaris L.

Common bean (Phaseolus vulgaris L.) is an important legume, useful for its high protein and dietary fiber. The fungal pathogen Uromyces appendiculatus (Pers.) Unger can cause major loss in susceptible varieties of the common bean. The Ur-3 locus provides race specific resistance to virulent strains or races of the bean rust pathogen along with Crg, (Complements resistance gene), which is required for Ur-3-mediated rust resistance. In this study, we inoculated two common bean genotypes (resistant “Sierra” and susceptible crg) with rust race 53 of U. appendiculatus, isolated leaf RNA at specific time points, and sequenced their transcriptomes. First, molecular markers were used to locate and identify a 250 kb deletion on chromosome 10 in mutant crg (which carries a deletion at the Crg locus). Next, we identified differential expression of several disease resistance genes between Mock Inoculated (MI) and Inoculated (I) samples of “Sierra” leaf RNA within the 250 kb delineated region. Both marker assisted molecular profiling and RNA-seq were used to identify possible transcriptomic locations of interest regarding the resistance in the common bean to race 53. Identification of differential expression among samples in disease resistance clusters in the bean genome may elucidate significant genes underlying rust resistance. Along with preserving favorable traits in the crop, the current research may also aid in global sustainability of food stocks necessary for many populations.

Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches.

In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals. In this chapter, we demonstrate how HMMs and hierarchical Bayesian modeling methods capture the horizontal time dependency structures in time series expression profiles by focusing on the identification of differential expression. In addition, those differential expression genes and transcript variant isoforms over time detected in core prerequisite steps can be generally further applied in detection of genetic regulatory networks to comprehensively uncover dynamic repertoires in the aspects of system biology as the coupled framework.

Abstract B1-28: GEO2Enrichr: A Google Chrome extension to extract gene sets from the Gene Expression Omnibus and analyze these lists for common biological functions

Abstract Background: Identification of differentially expressed genes from gene expression profiling studies is a necessary and important step in the analysis of microarray and RNA-seq data. Such data is deposited into the Gene Expression Omnibus (GEO). However, currently, to extract and analyze differentially expressed genes from GEO requires significant computational expertise. Methodology: GEO2Enrichr has a front-end browser extension and back-end server application. The back-end is written in Python and uses the Flask framework for the web application server. The server exposes three API endpoints to: (1) query GEO; (2) identify the differentially expressed genes; and (3) query Enrichr. The API endpoints are chained into a useful data pipeline. The front-end is a Google Chrome Extension written in JavaScript and CSS and programmatically inserts HTML elements into the native GEO interface. The Chrome web browser loads the extension whenever the user navigates to an NCBI GEO webpage. GEO2Enrichr unobtrusively inserts itself onto the page as a single button and checkboxes for easy sample selection. The button opens a modal box that allows users to edit their selected data, choose settings for differential expression identification, insert metadata about the experiments, download their resultant gene lists, and submit their gene lists to Enrichr for further analysis. Differential expression analysis has five options: Characteristic Direction1, T-test, limma, SAM, and fold-change. Other options are available to control for the size of the resultant differentially expressed gene lists by setting p-value and FDR cutoffs. Results: GEO2Enrichr is a Google Chrome Extension and Python-based API that adds functionality to GEO by allowing users to pipe Simple Omnibus Format in Text (SOFT) files to a differential expression analysis tool and then pipe the differentially expressed genes for analysis to Enrichr2, a popular gene list enrichment analysis web application. GEO2Enrichr addresses a need for improved functionality of GEO by embedding new features into the existing GEO pages, allowing researchers to easily select samples and process them for differential expression by various methods and perform enrichment analyses. GEO2Enrichr is free and available for installation at the Chrome Web Store. Conclusions: GEO2Enrichr can facilitate the more broad reusability of the GEO resource by lowering the point of entry to biologists without computational expertise. The systematic use of GEO2Enrichr can generate a new useful searchable resource. Overall, GEO2Enrichr can lead to improved extraction of knowledge from data3. [1] http://maayanlab.net/CD [2] http://amp.pharm.mssm.edu/Enrichr [3] http://bd2k.nih.gov Citation Format: Gregory W. Gundersen, Matthew R. Jones, Avi Ma'ayan. GEO2Enrichr: A Google Chrome extension to extract gene sets from the Gene Expression Omnibus and analyze these lists for common biological functions. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-28.

RNA-Seq Accuracy Comprehensive Assessment, Duplicability and Knowledge Content by the Sequencing Internal Control Consortium

We gift primary results from the Sequencing Internal Control (SEQC) project, coordinated by the America Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites victimization reference RNA samples with intrinsical controls, we have a tendency to assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression identification and compare it to microarray and quantitative PCR (qPCR) information victimization complementary metrics. in the slightest degree sequencing depths, we have a tendency to discover unannotated exon-exon junctions, with >80% valid by qPCR. We discover that measurements of relative expression area unit correct and reproducible across sites and platforms if specific filters area unit used. In distinction, RNA-seq and microarrays don't offer correct absolute measurements, and gene-specific biases area unit determined for all examined platforms, together with qPCR. Activity performance depends on the platform and information analysis pipeline, and variation is giant for transcript-level identification. The whole SEQC information sets, comprising >100 billion reads (10Tb), offer distinctive resources for evaluating RNA-seq analyses for clinical and regulative settings.

Active and passive MDMA (‘ecstasy’) intake induces differential transcriptional changes in the mouse brain

3,4-Methylenedioxymethamphetamine (MDMA, 'ecstasy') is a recreational drug widely used by adolescents and young adults. Although its rewarding effects are well established, there is controversy on its addictive potential. We aimed to compare the consequences of active and passive MDMA administration on gene expression in the mouse brain since all previous studies were based on passive MDMA administration. We used a yoked-control operant intravenous self-administration paradigm combined with microarray technology. Transcriptomic profiles of ventral striatum, frontal cortex, dorsal raphe nucleus and hippocampus were analysed in mice divided in contingent MDMA, yoked MDMA and yoked saline groups, and several changes were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The comparison of contingent MDMA and yoked MDMA vs. yoked saline mice allowed the identification of differential expression in several genes, most of them with immunological and inflammatory functions, but others being involved in neuroadaptation. In the comparison of contingent MDMA vs. yoked MDMA administration, hippocampus and the dorsal raphe nucleus showed statistically significant changes. The altered expression of several genes involved in neuroadaptative changes and synapse function, which may be related to learning self-administration behaviour, could be validated in these two brain structures. In conclusion, our study shows a strong effect of MDMA administration on the expression of immunological and inflammatory genes in all the four brain regions studied. In addition, experiments on MDMA self-administration suggest that the dorsal raphe nucleus and hippocampus may be involved in active MDMA-seeking behaviour, and show specific alterations on gene expression that support the addictive potential of this drug.

Abstract 5116: Low abundance protein enrichment by ProteoMiner coupled to iTRAQ-labeled LC-MS/MS for plasma-based quantitative proteomics

Abstract Background: Protein biomarkers for breast cancer are desired for early diagnosis, disease prognosis and drug response monitoring. Biomarkers in bodily fluids, such as plasma, allow for non-invasive monitoring and thus have additional value compared to tissue-based markers. Plasma-based biomarker discovery faces a challenge in that the wide dynamic range of protein concentrations prevents the detection of lower abundance proteins. Additionally, large sample sizes are needed for sufficient statistical power. In this setting, experimental design and normalization are important considerations. In this study, we have investigated the use of a novel protein enrichment strategy combined with isobaric label-based LC-MS/MS as well as two experimental designs for the identification of biomarkers of early stage breast cancer. Methods: Plasma from 12 patients with benign breast lesions and 12 with stage I breast cancer were processed using ProteoMiner enrichment followed by on-bead digestion. Two types of standards were investigated: a pooled standard, consisting of equal portions of the 24 plasma digests and a universal standard, consisting of 12 independent benign, stage I, stage II, and stage III breast cancer plasma samples. These designs have opposing strengths and weaknesses: pooled standards minimize variation but fix the sample size; universal standards allow for addition of new samples but introduce additional variation. The study required 4 iTRAQ-8plex sets which were subjected to 2-D LC separation followed by HPLC-Chip/Q-TOF analysis. Proteins were identified and quantified using Spectrum Mill software. Algorithms were developed for normalization, missing data imputation, protein grouping and identification of differential expression. Results: Use of ProteoMiner beads resulted in extraction of sufficient protein for at least 10 technical replicates and cut down preparation time by 80%, as compared to MARS-based immunodepletion. A total of 416 plasma proteins were identified, 96% of which are low abundance plasma proteins and 14 of which were differentially expressed. Expression values normalized using the pooled vs. universal standards were significantly correlated; however the use of the universal standard design yields a larger variation in expression values. Conclusion: This study demonstrated use of the ProteoMiner technology for enrichment of low abundance proteins from plasma. Fourteen plasma-based biomarkers of stage I breast cancer were identified with statistical significance. A number of these proteins (e.g. protocadherin FAT2, flightless-1 homolog) have been linked to breast cancer relevant processes, such as cell migration, adhesion, estrogen receptor signaling and proliferation. Data indicates that the universal standard is an adequate replacement for pooled standards with the caveat that higher variance should be expected. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5116. doi:10.1158/1538-7445.AM2011-5116

Identification of differentially expressed biliary proteins induced by cholangiocarcinoma using 2D-DIGE

Objective To determine the probability of identification of differential expression of biliary proteins induced by cholangiocarcinoma using 2D-DIGE. Methods Bile was obtained from 12patients with obstructive jaundice (including 6 cases of cholangiocarcinoma and 6 of cholelithiasis).Each sample was labeled with three different CyDyes (y3,Cy5,Cy2) including one internal standard,pooled from all the samples, and separated with 2-D DIGE in triplicate experiments. MALDI-TOF-MS and bioinformatics were adopted to identify and elucidate the significance of differentially expressed proteins in bile induced by cholangiocarcinoma. Results 55 matched protein spots differences in abundance were detected with statistical variance of two groups(Average Volum Ratio ≥1.5, t-test, P＜0. 05). Among these proteins, 13 PMF were obtained by MALDI-TOF-MS analysis. Eight proteins were identified by searching a protein database. Conclusion The differentially displayed proteomes between the pathological bile obtained from benign and malignant obstructive jaundice indicates the potential application of 2D-DIGE to identify the biomarker of cholangiocarcinoma. Key words: Bile duct neoplasms; Bile; Fluorescence two-dimensional difference gel electrophoresis

Combining genetical genomics and bulked segregant analysis-based differential expression: an approach to gene localization

Positional gene isolation in unsequenced species generally requires either a reference genome sequence or an inference of gene content based on conservation of synteny with a genomic model. In the large unsequenced genomes of the Triticeae cereals the latter, i.e. conservation of synteny with the rice and Brachypodium genomes, provides a powerful proxy for establishing local gene content and order. However, efficient exploitation of conservation of synteny requires ‘homology bridges’ between the model genome and the target region that contains a gene of interest. As effective homology bridges are generally the sequences of genetically mapped genes, increasing the density of these genes around a target locus is an important step in the process. We used bulked segregant analysis (BSA) of transcript abundance data to identify genes located in a specific region of the barley genome. The approach is valuable because only a relatively small proportion of barley genes are currently placed on a genetic map. We analyzed eQTL datasets from the reference Steptoe × Morex doubled haploid population and showed a strong association between differential gene expression and cis-regulation, with 83% of differentially expressed genes co-locating with their eQTL. We then performed BSA by assembling allele-specific pools based on the genotypes of individuals at the partial resistance QTL Rphq11. BSA identified a total of 411 genes as differentially expressed, including HvPHGPx, a gene previously identified as a promising candidate for Rphq11. The genetic location of 276 of these genes could be determined from both eQTL datasets and conservation of synteny, and 254 (92%) of these were located on the target chromosome. We conclude that the identification of differential expression by BSA constitutes a novel method to identify genes located in specific regions of interest. The datasets obtained from such studies provide a robust set of candidate genes for the analysis and serve as valuable resources for targeted marker development and comparative mapping with other grass species.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-011-1538-3) contains supplementary material, which is available to authorized users.

Assessing and selecting gene expression signals based upon the quality of the measured dynamics

BackgroundOne of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features.ResultsBy pre-selecting genes based upon quality before the identification of differential expression via algorithm such as EDGE, it was found that the percentage of statistically enriched ontologies (p < .05) was improved. Furthermore, it was found that a majority of the genes found via the proposed technique were also selected via an EDGE selection though the reverse was not necessarily true. It was also found that improvements offered by the proposed algorithm are anti-correlated with improvements in the various microarray platforms and the number of replicates. This is illustrated by the fact that newer arrays and experiments with more replicates show less improvement when the filtering for quality is first run before the selection of differentially expressed genes. This suggests that the increase in the number of replicates as well as improvements in array technologies are increase the confidence one has in the dynamics obtained from the experiment.ConclusionWe have developed an algorithm that quantifies the quality of temporal biological signal rather than whether the signal illustrates a significant change over the experimental time course. Because the use of these temporal signals, whether it is in mathematical modeling or clustering, focuses upon the entire time series, it is necessary to develop a method to quantify and select for signals which conform to this ideal. By doing this, we have demonstrated a marked and consistent improvement in the results of a clustering exercise over multiple experiments, microarray platforms, and experimental designs.

A model selection approach to discover age-dependent gene expression patterns using quantile regression models

BackgroundIt has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns.ResultsHere we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models.ConclusionIn this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.

Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates

BackgroundHigh-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing).ResultsThe DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery.ConclusionBased on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.

A Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification

Although both clustering and identification of differentially expressed genes are equally essential in most microarray studies, the two tasks are often conducted without regard to each other. This is clearly not the most efficient way of extracting information. The main aim of this article is to develop a coherent statistical method that can simultaneously cluster and detect differentially expressed genes. Through information sharing between the two tasks, the proposed approach gives more sensible clustering among genes and is more sensitive in identifying differentially expressed genes. The improvement over existing methods is illustrated in both our simulation results and a case study.