scholarly journals Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

2021 ◽  
Vol 22 (12) ◽  
pp. 6295
Author(s):  
Jeremiah Hadwen ◽  
Sarah Schock ◽  
Faraz Farooq ◽  
Alex MacKenzie ◽  
Julio Plaza-Diaz
Keyword(s):  
Differential Expression ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Single Gene ◽  
Drug Repurposing ◽  
Differentially Expressed ◽  
Expression Data ◽  
Gene Modulation ◽  
Upstream Regulator ◽  
Wide Range

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood–brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug–gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

scholarly journals Pre-Exposure to Defibrotide Prevents Endothelial Cell Activation by Lipopolysaccharide: An Ingenuity Pathway Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Nicoletta Orlando ◽  
Gabriele Babini ◽  
Patrizia Chiusolo ◽  
Caterina Giovanna Valentini ◽  
Valerio De Stefano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Endothelial Cell ◽  
Inflammatory Response ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Endothelial Protein C Receptor ◽  
Collagen Xviii ◽  
Wide Range ◽  
Regulated Gene Expression

Defibrotide (DFB) effects on different endothelial cell pathways have been investigated focusing on a limited number of genes or molecules. This study explored the modulation of the gene expression profile of steady-state or lipopolysaccharide (LPS)-activated endothelial cells, following the DFB exposure. Starting from differentially regulated gene expression datasets, we utilized the Ingenuity Pathway Analysis (IPA) to infer novel information about the activity of this drug. We found that effects elicited by LPS deeply differ depending on cells were exposed to DFB and LPS at the same time, or if the DFB priming occurs before the LPS exposure. Only in the second case, we observed a significant down-regulation of various pathways activated by LPS. In IPA, the pathways most affected by DFB were leukocyte migration and activation, vasculogenesis, and inflammatory response. Furthermore, the activity of DFB seemed to be associated with the modulation of six key genes, including matrix-metalloproteinases 2 and 9, thrombin receptor, sphingosine-kinase1, alpha subunit of collagen XVIII, and endothelial-protein C receptor. Overall, our findings support a role for DFB in a wide range of diseases associated with an exaggerated inflammatory response of endothelial cells.

Turnover of Heme-Bound Iron Is Associated with Activation of TLR4 and Chemokine Receptor Pathways in the Peripheral Blood Mononuclear Cell Transcriptome in Sickle Cell Anemia

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 819-819
Author(s):  
Eduard J. van Beers ◽  
Yanqin Yang ◽  
Susan Yuditskaya ◽  
Nalini Raghavachari ◽  
Gregory J. Kato
Keyword(s):  
Peripheral Blood Mononuclear Cell ◽  
Mononuclear Cell ◽  
Ingenuity Pathway Analysis ◽  
Sickle Cell ◽  
Pathway Analysis ◽  
Sickle Cell Anemia ◽  
Chemokine Receptor ◽  
Peripheral Blood ◽  
Differentially Expressed ◽  
Peripheral Blood Mononuclear

Abstract Abstract 819 Introduction It is widely accepted that inflammation plays an important role in the pathophysiology of Sickle Cell Anemia (SCA). Recently a number of studies indicated that peripheral blood mononuclear cell (PBMC) iron may contribute to inflammation in some diseases In SCA, PBMCs are continuously exposed to high turnover of iron in haptoglobin/hemoglobin complexes and free heme due to severe intravascular hemolysis. Therefore, we hypothesized that PBMC iron flux is marked by altered expression of iron-regulated genes, and in turn associated with the up regulation of genes in inflammatory pathways. To explore this hypothesis we correlated the expression of a predefined set of iron regulated genes to the SCA PBMC transcriptome as a whole. Methods Steady state patients with SCA were selected as published earlier. (Bereal-Williams et al. Haematologica 2012) Most noteworthy exclusion criteria were recent blood transfusion, liver or kidney dysfunction or a recent acute complication. Affymetrix Human Genome U133 Plus 2.0 arrays were used to measure PBMC mRNA profile. The primary source of free iron in PBMC in SCA is iron released from heme by heme oxygenase-1 (HMOX1), the first committed biochemical step in production of bilirubin. Therefore, we planned to validate the clustering of the study participants by correlating the cluster rank with bilirubin plasma levels. Genes which are published to be differentially expressed upon iron loading or chelation were used to prospectively create an ‘iron-regulated’ gene set (figure). Differentially expressed genes with the filter of a change greater than 40% between the clustering groups and 10% false discovery rate (FDR) were further analyzed with Ingenuity Pathway Analysis (IPA) System. Results Twenty-five subjects with SCA and 9 healthy subjects were analyzed. Hierarchical clustering using the predefined iron regulated gene list identified 3 groups of subjects with high, low and intermediate expression of iron activated genes (figure 1). As expected, none of the healthy controls was found in the high iron cluster. The cluster grouping was validated by correlation of serum bilirubin levels to the cluster rank (Spearman rho=0.358 p=0.044) and the expression of HMOX1 (Spearmen rho=0.387 p=0.034). In analysis of these three iron regulated gene clusters, we found 98 genes which were consistently and significantly differentially expressed, notably many genes important to crucial inflammatory pathways, especially Toll-like receptor 4 and 7 (TLR-4,7), chemokine receptor CCR1, and interleukin 15. The 98 markers identified ten canonical pathways in Ingenuity Pathway Analysis, most involving inflammation (each p<0.004, Table ). Expression profiling using a defined set of iron regulated genes identifies co-regulation of genes and pathways related to inflammatory cytokines, signaling cascades and innate immunity pathways. Our human PBMC findings confirm and extend recently presented data from several other groups linking TLR4-mediated heme-iron toxicity to pathological responses in sickle cell mice and in cultured cells. The role of heme-associated iron in sickle cell pathophysiology merits additional investigation. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Distinct Expression Profiles of lncRNAs Between Regressive and Mature Scars

2015 ◽  
Vol 35 (2) ◽  
pp. 663-675 ◽  
Author(s):  
Jingyun Li ◽  
Wei Long ◽  
Qian Li ◽  
Qing Zhou ◽  
Yu Wang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Immune System ◽  
Differential Expression ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Molecular Targets ◽  
Differentially Expressed ◽  
Altered Expression ◽  
Qrt Pcr ◽  
Non Coding Rnas

Background: Recent studies suggest that long non-coding RNAs (lncRNAs) play crucial roles in human diseases. The function of lncRNAs in abnormal scar pathogenesis remains poorly understood. Methods: In this study, we examined the lncRNAs expression profiles among regressive and mature scars following caesarean sections. A total of 30,586 lncRNAs and 26,109 mRNAs were analyzed by microarrays (Human LncRNA Array v3.0, Arraystar, Inc.). Results: In total, we identified 1,871 lncRNAs and 817 mRNAs with differential expression between regressive and mature scar individuals (fold change≥3, p≤0.001). A set of differentially expressed lncRNA transcripts, in particular, lncRNA8975-1, AC097662.2 and RP11-586K2.1, were confirmed using qRT-PCR. Gene ontology and pathway analysis revealed that compared to mature scars, many processes over-represented in regressive scars are related to the immune system. Conclusion: Our results show significantly altered expression profiles of lncRNAs and mRNAs between regressive and mature scars. These transcripts are potential molecular targets for inhibiting abnormal scar formation following caesarean sections.

Proteomic Profiling of AML Bone Marrow-Derived Mesenchymal Stem Cells (MSC) Reveals Marked Differences From Normal MSC.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2355-2355
Author(s):  
Rui-yu Wang ◽  
Yi Hua Qiu ◽  
Suk Young Yoo ◽  
Teresa McQueen ◽  
Ye Chen ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Protein Expression ◽  
Differential Expression ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Expression Patterns ◽  
P Value ◽  
Proteomic Profiling

Abstract Abstract 2355 The interaction of bone marrow (BM) mesenchymal stem cells (MSC) and acute myeloid leukemia (AML) cells creates a microenvironment (McrEnv) that supports and regulates the survival and proliferation of leukemic cells. These same BM McrEnv interactions can also create a sanctuary that protects subpopulations of AML blasts from chemotherapy. The mechanisms by which the BM-MSC McrEnv effects these changes remain unclear and the heterogeneity of these effects across different subtypes of AML (FAB, WHO or cytogenetics) is unknown. Furthermore, the functional differences between normal BM-MSC and AML BM-MSC biology are undefined. To address this we set out to perform proteomic profiling comparing normal and AML BM derived-MSC and to ascertain how AML BM-MSC protein expression patterns correlated with AML blast protein expression. We cultured BM samples for 4 to 8 weeks in MEM-alpha media with 20% fetal calf serum to isolate AML-MSC (n=106), and NL-MSC (n=70). Cells defined as MSC were positive for CD90 and CD105 and negative for CD45 by flow cytometry. Whole cell protein lysates were prepared. Matched AML protein samples prepared from mononuclear cell fractions from the same BM collection were available for most cases (n=96). We generated a custom Reverse Phase Protein Array (RPPA) from these samples and probed the array with 151 validated antibodies. Statistical analysis was performed by two-way ANOVA using Tukey's test to identify differential expression of individual proteins between the samples and Ingenuity pathway analysis was performed to elucidate differential pathway utilization. Comparison of AML-BM-MSC to NL-BM-MSC demonstrated similar levels of expression for 66 proteins (43.7%) but 85, 67 and 28 were different at the p-value of < 0.05, < 0.01 and <0.0001 with a false discovery rate (FDR) of 0.05, 0.01, 0.0001 respectively. By function, the 28 proteins with greatest differential expression (Italic = lower in AML-MSC) included cell cycle regulators (P21, Cyclin D1, CDK4), adhesion/integrins (CD49b, CD31, and galectin 3, signaling pathway members and their targets (Smad1, Smad4, Stat1, Stat5, pPDK1, GSK3), apoptosis regulators (Bak, BCLXL, Smac,) growth and proliferation regulators (pCREB, EIF2α, FOXO1α, Sirt1 Strathmin) and pIRS, cleaved Notch1, HSP90, TP53 CK2 and PP2A. Using mixed linear effectors protein expression levels in AML-MSC did not show correlation with patient's age (< 50 >), gender, blast count in BM or peripheral blood, or the percent of CD34 positivity. Protein expression in AML-BM-MSC from cases with favorable cytogenetics had significantly lower levels of GAB2, P27 P70S6K, SMAC and 14.3.3e and cases with unfavorable cytogenetics had significantly lower levels of antiapoptosis proteins Bax, Bad and BCL-XL and higher levels of Smac as well as lower levels of phosphor-FOXO3a and pELK. Levels of ARC were higher in cases with intermediate cytogenetics. Ingenuity pathway analysis also demonstrated differential utilization of several families of proteins regulating signal transduction, apoptosis and transcription and connected to surface growth factor receptors and adhesion molecules. As anticipated for cells of different origin, the expression patterns were completely different between AML BM-MSC and AML blasts for 131 of 151 proteins (86.1%) (Tukey's p-value <0.0001 and FDR 0.0001). These results suggest that protein expression in AML MSC is markedly different from that of NL-MSC. Differential expression was observed in multiple functional groups suggesting that AML-MSC are functionally distinct from NL-MSC. Since MSC influence adjacent and nearby AML blasts it is likely that these variances impact AML blast biology. Additional analysis is underway to determine if recurrent patterns of protein expression exist in AML-BM-MSC, how these differ from protein expression patterns in NL-MSC, and whether AML-BM-MSC protein expression patterns correlate with AML-blast protein expression patterns. Correlation between MSC patterns and AML-blast patterns would provide therapeutically targetable sites in MSC that could be exploited to influence AML blast biology. Disclosures: No relevant conflicts of interest to declare.

207 CIRCULATORY microRNA SIGNATURES IN FOLLICULAR FLUID IN RELATION TO THE GROWTH STATUS OF BOVINE OOCYTES

2013 ◽  
Vol 25 (1) ◽  
pp. 251 ◽  
Author(s):  
M. M. H. Sohel ◽  
D. Salilew-Wondim ◽  
M. Hölker ◽  
F. Rings ◽  
K. Schellander ◽  
...  
Keyword(s):  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Follicular Fluid ◽  
Mirna Expression ◽  
Cell Communication ◽  
Bioactive Molecules ◽  
Differentially Expressed ◽  
Oocyte Growth ◽  
Mirna Expression Pattern ◽  
Protein Ubiquitination

Cell-to-cell communication within the follicle involves many signalling molecules, and this process is believed to be mediated by secretion and uptake of exosomes that contained several bioactive molecules including circulatory microRNAs (miRNA). The present study was conducted to investigate the circulating miRNA expression pattern in exosome and nonexosomal portion of follicular fluid (FF) in follicles with fully grown or growing oocytes. For this, the FF and cumulus–oocyte complexes (COC) were retrieved from 5- to 8-mm individual follicles from ovaries obtained from local abattoir. Then, the oocytes were subjected to brilliant cresyl blue (BCB) staining and classified as BCB+ (fully grown oocytes) and BCB– (growing oocytes). Accordingly, the corresponding FF was classified as BCB+ and BCB– based on their oocyte source. Following this, the exosomes were trapped from each FF categories using ExoQuick™ (SBI System Bioscience). Thus, total RNA was isolated from exosomal and nonexosomal portion of the FF using miRNeasy mini kit (Qiagen) and subjected to miRNA expression studies. The human miRCURY LNA™ Universal RT miRNA PCR array system (Exiqon) was used for miRNA expression profiling. Data analysis was performed using a comparative threshold cycle (ΔCT) method. The results revealed that 26 miRNAs were found to be differentially expressed (fold change ≥2 and P < 0.05) between the exosomal portion of FF from fully grown and growing oocyte groups. Among these, 17 miRNA including miR-608, miR-654-5p, miR-640, miR-582-5p, miR-449b, miR-155 were upregulated and 9 miRNA including miR-373, miR-526b*, miR-33a*, miR-30b, miR-29a* were downregulated in exosomal portion of FF of growing oocyte groups. The ingenuity pathway analysis of genes predicted to be targeted by those miRNAs were found to be involved in WNT/β catenine, purine metabolism, protein ubiquitination, and cAMP-mediated signalling pathways. Similarly, 36 miRNA were differentially expressed between the non-exosomal portion of FF of fully grown and growing oocytes. From those, 27 miRNA including let-7i*, miR-328, miR-223, miR-19b-1*, miR-423-5p, miR-29c, miR-659 were upregulated, whereas the expression level of 9 miRNA including miR-381, miR-18a*, miR-30e*, miR-934, and miR-302c was downregulated in the nonexosomal portion of FF of growing oocyte groups. In addition, the ingenuity pathway analysis indicated that the genes predicted to be targeted by these miRNA were found to be involved in NRF2-mediated oxidative stress response, tight junction signalling, and protein ubiquitination pathways. In conclusion, this study detected the presence of exosome or non-exosome-mediated circulation of miRNA in the bovine follicular fluid and oocyte growth-dependent variation of circulatory miRNA in the follicular environment.

scholarly journals Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

2018 ◽  
Author(s):  
Yu Hu ◽  
Hayley Dingerdissen ◽  
Samir Gupta ◽  
Robel Kahsay ◽  
Vijay Shanker ◽  
...  
Keyword(s):  
Differential Expression ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Literature Mining ◽  
Wide Range ◽  
Cancer Types

AbstractA number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21 SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

scholarly journals Low ARID1A Expression is Associated with Poor Prognosis in Hepatocellular Carcinoma

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2002
Author(s):  
Sun Yim ◽  
Sang Kang ◽  
Ji-Hyun Shin ◽  
Yun Jeong ◽  
Bo Sohn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Clinical Significance ◽  
Ingenuity Pathway Analysis ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Poor Prognosis ◽  
Specific Gene ◽  
Expression Data ◽  
Immune Activity

AT-rich interactive domain 1A (ARID1A) is one of the most frequently mutated genes in hepatocellular carcinoma (HCC), but its clinical significance is not clarified. We aimed to evaluate the clinical significance of low ARID1A expression in HCC. By analyzing the gene expression data of liver from Arid1a-knockout mice, hepatic Arid1a-specific gene expression signature was identified (p < 0.05 and 0.5-fold difference). From this signature, a prediction model was developed to identify tissues lacking Arid1a activity and was applied to gene expression data from three independent cohorts of HCC patients to stratify patients according to ARID1A activity. The molecular features associated with loss of ARID1A were analyzed using The Cancer Genome Atlas (TCGA) multi-platform data, and Ingenuity Pathway Analysis (IPA) was done to uncover potential signaling pathways associated with ARID1A loss. ARID1A inactivation was clinically associated with poor prognosis in all three independent cohorts and was consistently related to poor prognosis subtypes of previously reported gene signatures (highly proliferative, hepatic stem cell, silence of Hippo pathway, and high recurrence signatures). Immune activity, indicated by significantly lower IFNG6 and cytolytic activity scores and enrichment of regulatory T-cell composition, was lower in the ARID1A-low subtype than ARID1A-high subtype. Ingenuity pathway analysis revealed that direct upstream transcription regulators of the ARID1A signature were genes associated with cell cycle, including E2F group, CCND1, and MYC, while tumor suppressors such as TP53, SMAD3, and CTNNB1 were significantly inhibited. ARID1A plays an important role in immune activity and regulating multiple genes involved in HCC development. Low-ARID1A subtype was associated with poor clinical outcome and suggests the possibility of ARID1A as a prognostic biomarker in HCC patients.

scholarly journals Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study

2020 ◽  
Vol 14 ◽  
pp. 117793222095274
Author(s):  
Deepak Mav ◽  
Dhiral P Phadke ◽  
Michele R Balik-Meisner ◽  
B Alex Merrick ◽  
Scott Auerbach ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Differential Expression Analysis ◽  
Entire Genome ◽  
Upstream Regulator ◽  
Downstream Analysis ◽  
The Unfolded Protein Response ◽  
Whole Transcriptome

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the “measured only” and “whole transcriptome” (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the “measured only” genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original “measured only” dataset. Furthermore, the inclusion of the extrapolated genes raised “tunicamycin” from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.

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